Spring 2019/2020

June, 19th – Isabelle Ferezou

Title: A mesoscopic view of tactile sensory information processing in the cerebral cortex

Abstract: Since the first description of its remarkable cellular organization by Woolsey and Van der Loos (1970), the whiskers representation in the rodent primary somatosensory cortex (S1) has become a major model for studying the cortical processing of tactile sensory information. In its layer 4, neurons form clusters, called barrels, that share the same topology as the whiskers on the snout of the animal, each neuronal column associated with a barrel receiving primarily inputs coming from its corresponding whisker.

A huge amount of information has been collected over the past 50 years on the whiskers sensory system; however it is still largely unknown how it really integrates distributed information to build a global percept of the tactile scene. Working at a mesoscopic scale that allows visualizing how the information flows throughout cortical columns and further propagates to other cortical areas is a real asset to address this question. Voltage sensitive dye imaging, which benefits from a sub-columnar spatial resolution and a millisecond time resolution reveals how, upon tactile stimulation of a given whisker, information is rapidly transmitted to its corresponding column in S1, but also, within the next couple of milliseconds, to the secondary somatosensory cortex and then to the primary motor cortex. Using this method, we described with an unprecedented precision the topography of whiskers representation, as well as the lateral propagation of sensory inputs within these cortical areas, thus providing insights in the neuronal dynamics at play for integration of complex multi-whisker inputs in the cortical network.


June, 12th – Pradeep Dheerendra

Title: Dynamics underlying auditory object boundary detection and segregation

Abstract: A visual object might be easy to define and understand, but objects perceived via audition are also important. Auditory object analysis involves the process of detecting, segregating and representing spectro-temporal regularities in the acoustic environment into stable perceptual units. Thus the auditory system accomplishes the process of transformation of acoustic waveform into an object based representation. This talk focuses on two fundamental aspects of auditory object processing viz. detection of auditory object boundary and auditory segregation. In the first study, I present the dynamics underlying the detection of emergence of a new auditory object in an ongoing auditory scene using MEG. I found a slow drift signal at the object boundary which I think might be the precision signal. In the second study, I present the brain basis underlying human auditory figure-ground analysis in a macaque model using fMRI and psychophysics. This has provided spatial priors for macaque neurophysiology.

June, 5th – Alex Cayco Gajic

Title: High-dimensional representations in cerebellar granule cells

Abstract: The cerebellum is thought to learn sensorimotor relationships to coordinate movement. Sensory and motor information is sent to a large number of cerebellar granule cells, which comprise the vast majority of neurons in the brain. Theoretically, this large anatomical expansion is thought to help pattern separation by representing sensorimotor information in a high-dimensional granule cell population code. However, how the granule cell population activity encodes sensory and motor information, and whether granule cell populations can support high-dimensional representations, is poorly understood. To address this, we used a high-speed random-access 3D 2-photon microscope to simultaneously monitor the Ca2+ activity in hundreds of granule cell axons of spontaneously behaving animals. We find that granule cell population activity transitions between separate, orthogonal coding spaces representing periods of quiet wakefulness vs. active movement, and that the granule cell representation is higher dimensional than has previously been observed.

May, 29th – Lucia Prieto Godino

Title: Evolution of olfactory systems on the fly

Abstract: Sensory systems encode the world around us to guide context-dependent appropriate behaviours that are often species-specific. This must involve evolutionary changes in the way that sensory systems extract environmental features and/or in the downstream sensory-motor transformations implemented. However, we still know little about how evolution shapes neural circuits. We are studying the olfactory system of Drosophila and tsetse flies across multiple species spanning a wide range of ecological niches and divergence times. We find divergent odour-guided behaviour towards host odours. To elucidate the cellular, circuit and molecular basis behind this behavioural evolution we are employing a multidisciplinary approach, including field work, the development of genetic tools across species, calcium imaging, single cell transcriptomics and reconstruction of central olfactory circuits at synaptic resolution. I will discuss the progress we have made in our efforts to understand how evolution tinkers neural circuits as animals adapt to different environments.


May, 22nd – Bradley Love

Title: A clustering account of spatial and non-spatial concept learning

Abstract: How do we learn to categorise novel items and what is the brain basis of these acts? For example, after a child is told an animal is a dog, how does that experience shape how she classifies future items? I will present model-based fMRI results concerning how people learn categories from examples and touch on parallel findings with monkey single-unit recordings. Our analyses indicate that the medial temporal lobe (MTL), including the hippocampus, plays an important role in both learning and recognition. Successful cognitive models, which explain both behavioural and brain measures, learn to selectively weight (i.e., attend) to stimulus aspects that are task relevant. This form of weighting, or top-down attention, can be viewed as a compression process. I will discuss how the medial prefrontal cortex (mPFC) and the hippocampus coordinate to build low-dimensional representations of learned concepts, as well as how the dimensionality of visual representations along the ventral stream is altered by the learning task. Finally, this general learning mechanism offers a straightforward account of spatial learning, including place and grid cell activity in both human and rodent studies.


May, 15th – Grace Lindsay

Title: Modelling the influence of feedback in the visual system

Abstract: Cortico-cortical feedback is common in the visual system and is believed to be involved in processes such as perceptual inference, attention, and learning. In this talk I will demonstrate how convolutional neural networks can be used to explore how such feedback works. In the first half of the talk, I will focus on the signals from prefrontal areas that are believed to control top-down feature attention. In the second half, I’ll discuss ongoing work on how local feedback connections help process noisy images.


May, 8th – bank holiday


May, 1st –

April, 24th –


April, 17th – Easter


April, 10th – Bank holiday


April, 3rd – Timothy O’Leary

March, 27th – Silvia Maggi


Winter 2019/2020

March, 20th –


March, 13th – Krasimira Tsaneva-Atanasova

Title: The Origin of GnRH Pulse Generation: An Integrative Mathematical-Experimental Approach

Abstract: The gonadotropin-releasing hormone (GnRH) pulse generator controls the pulsatile secretion of the gonadotropic hormones LH and FSH and is critical for fertility. The hypothalamic arcuate kisspeptin neurons are thought to represent the GnRH pulse generator, since their oscillatory activity is coincident with LH pulses in the blood; a proxy for GnRH pulses. However, the mechanisms underlying GnRH pulse generation remain elusive. We developed a mathematical model of the kisspeptin neuronal network and confirmed its predictions experimentally, showing how LH secretion is frequency-modulated as we increase the basal activity of the arcuate kisspeptin neurons in vivo using continuous optogenetic stimulation. Our model provides a quantitative framework for understanding the reproductive neuroendocrine system and opens new horizons for fertility regulation.


March, 6th – Matthias Hennig

Title: SpikeInterface: A project for reproducible next generation electrophysiology

Abstract: Many electrophysiologists would agree that spike sorting is somewhat of a dark art, with many secrets, black-box algorithms (occasionally probably written in blood) and heuristics and superstitions. With exciting new large scale probes and arrays now shipped to many labs and producing terabytes of recordings, reliable and reproducible analysis becomes increasingly harder to achieve. In this talk I will show (and attempt to live-demo) SpikeInterface, a project that aims to bring together the many efforts that have been put into spike sorting by many groups over the past decade and beyond. This project not only wraps many sorters, tools and and file formats, but also provides new methods for assessing quality of sorted spikes based on comparison between sorters and with ground truth data. We found a surprisingly low agreement between sorters, and show that this is due to high false positive rates that cannot be corrected for using common heuristics. Here I will suggest methods and workflows to remedy and improve this situation, which are often implemented with a few lines of code.



This project is joint work with: Alessio P. Buccino, Cole L. Hurwitz, Jeremy Magland, Samuel Garcia, Joshua H. Siegle, Roger Hurwitz

Febbruary, 28st – Mara Cercignani

Title: MRI for In Vivo Imaging of the Effects of Inflammation on the CNS

Abstract: Recent evidence supports a role for inflammation in several psychiatric disorders such as Alzheimer’s disease and major depression. One of the mechanisms underpinning CNS inflammation is the activation of microglia, which can be imaged using Translocator Protein (TSPO) PET. This technique, however, is costly and difficult to implement. This talk will present some of the results obtained in our lab using non-invasive, quantitative MRI approaches to assess the effects of inflammation on the brain.

Febbruary, 21st – Arno Onken


Febbruary, 14th – Marcus Kaiser

Title: Structure and Dynamics of Human Connectomes: Applications for Informing Diagnosis and Treatment of Brain Disorders


Our work on connectomics over the last 15 years has shown a small-world, modular, and hub architecture of brain networks [1,2]. Small-world features enable the brain to rapidly integrate and bind information while the modular architecture, present at different hierarchical levels, allows separate processing of various kinds of information (e.g. visual or auditory) while preventing wide-scale spreading of activation [3]. Hub nodes play critical roles in information processing and are involved in many brain diseases [4].

After discussing the organisation of brain networks, I will show how connectivity in combination with machine learning and computer simulations can identify the progression towards dementia before the onset of symptoms informing interventions that can delay disease progression [5].

For epilepsy patients, connectome-based simulations can also be used to predict the outcome of surgical interventions as well as alternative target regions [6]. I will also present recent results on local changes in epilepsy, concerning structural connectivity within brain regions, which are more indicative of surgery outcome than connectivity between brain regions. In addition, we also developed models of tissue within a brain region (http://www.vertexsimulator.org). Such models can observe the effects of invasive [7] or non-invasive electrical brain stimulation.

I will finally outline how these models could, in the future, inform invasive interventions, such as optogentic stimulation in epilepsy patients (http://www.cando.ac.uk) or non-invasive interventions using electrical, magnetic or focused ultrasound stimulation.

[1] Martin, Kaiser, Andras, Young. Is the Brain a Scale-free Network? SfN Abstract, 2001.

[2] Sporns, Chialvo, Kaiser, Hilgetag. Trends in Cognitive Science, 2004.

[3] Kaiser et al. New Journal of Physics, 2007.

[4] Kaiser et al. European Journal of Neuroscience, 2007.

[5] Peraza et al. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 2019.

[6] Sinha et al. Brain, 2017.

[7] Thompson et al. Wellcome Open Research, 2019.

Febbruary, 7th – Liad Baruchin

Title: The early developing brain undergoes many changes in its basic neuronal connectivity.

Abstract: Specifically, in our lab, looking at the barrel cortex, we find that circuits involving VIP+ and SST+ IN completely change from birth to adulthood. Currently, I am investigating how these interneuronal populations are involved in early sensory perception. To do that, I am using a genetic model in which either SST+ or VIP+ interneurons are completely silenced. Thus, using silicon probes I can record from different layers over the barrel field and see how silencing this neuronal populations affect the neuronal response to passive whisking. In this talk I will present my most recent results that show that these neuronal populations differentially affect the cortical processing of whisking speed and paired-pulse adaptation.

January, 31st – Eleni Vasilaki

Title: Sparse Reservoir Computing (SpaRCe) for neuromorphic devices

Abstract: In this talk I will present fundamental ideas about biological learning in fruit flies, and how these are related to Machine Learning. Inspired by the architecture of small brains, and within the framework of Ecco State Networks, I will discuss the importance of neuron selectivity to specific stimuli. I will then introduce a threshold per reservoir neuron as an efficient mechanism to achieve sparseness in the neuronal representation. The threshold is adapted via a gradient rule on an error function structurally identical to threshold learning via backpropagation. And yet, a simple mathematical analysis of its consequences for the specific architecture shows that it leads to neuronal selectivity. I will show in simulations that, within this context, our approach is advantageous in terms of performance versus imposing sparseness of weights via L1 norm. I will also discuss how such learning architectures can be exploited in the context of neuromorphic engineering.

January, 24th – Miguel Maravall

Title: Tactile sequence learning induces selectivity to multiple task variables in the mouse barrel cortex.

Abstract: Sequential temporal patterning is a key feature of natural signals, used by the brain to decode stimuli and perceive them as sensory objects. To explore the neuronal underpinnings of sequence recognition and determine if neurons adjust temporal integration as a result of learning, we developed a task in which mice had to discriminate between sequential stimuli constructed from distinct vibrations delivered to the vibrissae (whiskers), assembled in different orders.

Optogenetic inactivation experiments showed that both primary somatosensory ‘barrel’ cortex (S1bf) and secondary somatosensory cortex are involved in the task, consistent with a serial flow of sensory input to decision-making stages. Two-photon imaging in superficial layers of S1bf of well-trained animals revealed heterogeneous neurons with selectivity to task variables including sensory input, the animal’s action decision, and trial outcome (rewards and their departure from prediction). A large fraction of neurons were activated preceding goal-directed licking, thus predicting the animal’s learned response to a target sequence rather than the sequence itself. These neurons were absent in naïve animals. Therefore, in S1bf learning resulted in neurons that embodied the learned association between the presence of the target sequence and licking, instead of neurons that categorically responded to the sequence or integrated features over time.


January, 17th – Petra Vertes

Title: Maps, Models and Maths: New strategies for understanding the biological basis of mental ill-health.

Abstract: The last 20 years have witnessed extraordinarily rapid progress in neuroscience, including breakthrough technologies such as optogenetics and the collection of unprecedented amounts of neuroimaging, genetic and other data. However, the translation of this progress into improved understanding and treatment of mental health symptoms has been comparatively slow. One central challenge has been to reconcile different scales of investigation, from genes and molecules to cells, circuits, tissue, whole-brain and ultimately behaviour. In this talk I will describe several strands of work using mathematical, statistical, and bioinformatic methods to bridge these gaps. First, I will describe my work on linking neuroimaging data to the Allen Brain Atlas (a brain-wide, whole-genome map of gene expression) and how we can apply these tools in the nascent field of imaging transcriptomics to further our understanding of schizophrenia and other neuropsychiatric disorders. Next, I will discuss parallel efforts for using network science and control theory for linking microscopic function (ie the role of individual cells) to large-scale behaviour in C. elegans.

Januar, 10th – Mark Walton

Title: Regulation of dopamine during reward-guided decision making: tracking reward prediction in action

Abstract: It is widely accepted that the activity of many dopamine neurons and dopamine release in parts of the striatum represent predictions of future rewards, which in turn can be used to shape decision making. Nonetheless, the precise content and function of these dopamine signals during reward-guided behaviours remains a matter of great controversy. I’ll present ongoing work to examine how dopaminergic correlates of reward prediction and choice, recorded in rodents performing reward-guided decision making tasks, are modulated by action requirements, task structure and context. These data – along with others’ – suggests that dopamine activity can be shaped by a mixture of influences over different timescales and across different parts of striatum.


Autumn 2019/2020

December, 13th – Marc Goodfellow

Title: Modelling pathological brain dynamics


Disorders of the brain can often result in alterations to its large-scale dynamics. An example is epilepsy, in which electrographic measurements display abnormal rhythms, particularly during seizures. Understanding why these dynamics are generated is challenging, particularly in the clinical setting, but better insight could help to improve diagnosis and treatment. In this talk I will discuss a particular approach to this problem, using mathematical models of large-scale brain networks to understand pathological dynamics. I will demonstrate how the study of such models can lead to new insight into the generation of seizures, and how models can be combined with clinical data to generate predictions for the surgical treatment of epilepsy.

December, 6th – Armin Lak

Title: Dopaminergic and prefrontal basis of learning from sensory confidence and reward value


Deciding between stimuli requires combining their learned value with one’s sensory confidence. We trained mice in a visual task that probes this combination. Mouse choices reflected not only present confidence and past rewards but also past confidence. Their behaviour conformed to a model that combines signal detection with reinforcement learning. In the model, the predicted value of the chosen option is the product of sensory confidence and learned value. We found precise correlates of this variable in the pre-outcome activity of midbrain dopamine neurons and of medial prefrontal cortical neurons. However, only the latter played a causal role: inactivating medial prefrontal cortex before outcome strengthened learning from the outcome. Dopamine neurons played a causal role only after outcome, when they encoded reward prediction errors graded by confidence, influencing subsequent choices. These results reveal neural signals that combine learned value with sensory confidence before choice outcome and guide subsequent learning.

November, 29nd – Nothing!!


November, 22nd – Bernhard Staresina

Title: Memory consolidation during sleep: Mechanisms and representations


In this talk, I will first present direct recordings from the human hippocampus during natural sleep. Analyses focus on the question how different sleep signatures (slow oscillations, spindles and ripples) interact and may facilitate hippocampal-neocortical information transfer. I will then turn to memory representations being reactivated during sleep. Using targeted memory reactivation, we show that sleep spindles seem to facilitate content-specific consolidation.

November, 15th – Jacques-Donald Tournier

Title: Multi-shell diffusion MRI and its applications in the neonatal brain


Recent advances in MRI acquisition now allow the routine acquisition of large amounts of so-called multi-shell diffusion MRI data within reasonable time frames. This opens up exciting new possibilities, but also brings additional challenges. This talk will present new methods for the acquisition and analysis of such data, both at the single-subject and at the group level. The talk will focus primarily (but not exclusively) on applications within the neonatal brain, using data acquired as part of the developing human brain connectome project.

November, 8th – Dan Goodman

Title: The Reluctant Machine Learner


The unique quality of the brain is that it can perform difficult tasks.

The traditional approach to modelling in neuroscience, though, has focussed on simple tasks, because those were the only ones we could model. Recently, that has all changed with the advent of powerful new methods from machine learning that can recognise some images better than humans, for example. I will argue that we have to study the brain solving difficult tasks, and therefore we have to be using techniques from machine learning because these are the only known methods that enable us to do that. However, that doesn’t mean that the brain is at all like the current best known machine learning models. Those models miss out on a lot of important points, like temporal dynamics and spiking neurons. Moreover, they make mistakes that humans would never make and require vastly more data than we do to learn. Despite these issues, neuroscience has a lot to gain from adopting machine learning methods, and I’ll talk about a couple of ongoing projects in my lab that attempt to use machine learning methods in a way that is more compatible with traditional neural modelling: modelling speech recognition in the auditory system; and trying to understand the computational role of the heterogeneity observed in real brains.


November, 1st – Christina Buetfuring

Title: Decision coding by layer 2/3 neurons in primary somatosensory cortex


Sensory information enables us to make informed choices that are critical for survival. While primary sensory areas provide information on sensory stimuli, behaviourally-relevant decision-making variables have been shown to be represented in higher-order association cortices. Therefore, sensory coding and decision-making are typically studied under the assumption of anatomical separation. Neurons in the superficial layers of the whisker region of primary somatosensory cortex (S1), barrel cortex, not only receive somatotopically mapped bottom-up inputs from the thalamorecipient layer 4 but also lateral projections from neighbouring barrels and top-down projections from higher cortical areas. Therefore, layer 2/3 (L2/3) neurons in barrel cortex are a prime candidate for providing an intersection of sensory processing and decision-making in complex behavioural tasks. Previous work using electrophysiological recordings in monkeys, rats and mice has not found conclusive choice activity in S1 but was limited to low number of neurons. Studies using two-photon calcium imaging found that some behavioural aspects modulate activity in L2/3 barrel cortex neurons. It is unclear, however, whether the signal difference across trial types in those studies reflects choice-related signals or a modulation of activity by action-related variables such as motivation, movement preparation etc. Here, we used two-photon calcium imaging of neurons in L2/3 mouse barrel cortex during a cued texture discrimination task with two lickports to determine whether these neurons can code for behaviourally-relevant decision variables. We found neurons carrying information about the stimulus irrespective of the behavioural outcome (‘stimulus neurons’) as well as neurons whose activity carried information about the choice to be made (‘decision neurons’). Choice-related activity in decision neurons is not driven by signals related to motor output, but instead follows stimulus presentation. Furthermore, ambiguous population coding of decision neurons predicts miss trials and an improvement in categorical coding in decision neurons coincides with learning the stimulus-choice association. Our identification of neurons encoding stimulus and behaviourally-relevant decision signals within the same circuit suggests a direct involvement of L2/3 S1 in the decision-making process.

Location: GEOG BLDG G.11N SR1

October, 25th – first year student projects

October, 18th – first year student projects


October, 11th – Cian O’Donnell

Title: Neural variability in Autism


Autistic people often have sensory processing deficits, and we would like to understand why. One clue comes from the observation that Autistic peoples’ EEG and fMRI responses to sensory stimuli are more variable than those in neurotypical people. We used in vivo two-photon calcium imaging of populations of layer 2/3 cortical neurons in young wild-type and Fragile-X Syndrome mouse models to search for three aspects of such variability at a cellular level: 1) across single trials from identical stimuli in the same animal, 2) across animals of the same age, and 3) longitudinally across days in the same animals. I will present what we found. Work with Beatriz Mizusaki (Univ of Bristol), Nazim Kourdougli, Anand Suresh, and Carlos Portera-Cailliau (Univ of California, Los Angeles).

Location: PHYS BLDG 3.34

October, 4th – Dimitris Pinotsis


In this talk, I will discuss how deep neural networks can reveal semantic and biophysical properties of memory representations in the brain (neural ensembles or cell assemblies).

First, I will consider a flexible decision-making paradigm and show that deep neural networks allow us to understand the sensory domains and semantics different brain areas prefer (motion vs color) and code (sensory signals vs abstract categories) respectively. These results will also suggest a way for studying sensory and categorical representations in the brain by combining behavioural and neural network models.

Then, I will show that deep neural networks can also reveal cortical connectivity in neural ensembles and explain a well-known behavioral effect in psychophysics, known as the oblique effect. This work will also introduce a new mathematical approach for identifying neural ensembles that exploits a combination of machine learning, biophysics and brain imaging.


Spring 2019

June , 28th – Thomas Wills

Geog Sciences G.11N SR1

June, 14th – Shana Silverstein

Geog Sciences G.11N SR1

June, 7th – Denize Atan

Geog Sciences G.11N SR1

Mai, 24th – Tim Howe

Geog Sciences G.11N SR1

Title: Extending evidence for REM-associated Replay in Hippocampal CA1 Place Cells.

Abstract: During periods of inactivity, hippocampal CA1 neurons with spatial receptive fields (“place cells”), reactivate in patterns that recapitulate previously experienced spatial sequences, a phenomenon known as replay. CA1 replay is most prominently associated with sharp-wave ripple (SWR) events during non-REM sleep or quiet wake, and has been implicated in the consolidation of episodic memory. Replay has also been reported during REM sleep [1], however evidence for this phenomenon is substantially less extensive than for replay during non-REM. Non-REM replay occurs on a temporally compressed timescale (approximately 8 times faster than during active behaviour) around brief, discrete SWR events. REM-replay appeared less temporally compressed and occurred during extended periods of elevated theta power, necessitating alternative detection methods to those established for SWR-associated replay. Using tetrode recordings from adult rat dorsal CA1, we present data that corroborate the existence of REM-replay. The activity of multiple place cells was recorded simultaneously while rats performed simple goal-directed maze tasks, and during subsequent extended rest periods in a sleep box. Replay was detected using a moving-window correlation algorithm (from [1]), and confirmed with complementary approaches including hidden Markov model (HMM) and Bayesian trajectory decoding. Extending evidence for REM replay paves the way for analyses exploring its experience-dependence, extra-hippocampal correlates and function contributions.

[1] Louie K & Wilson MA (2001) Neuron 21: 145-156

Mai, 17th- Anna Schapiro (University of Pennsylvania)

Geog Sciences G.11N SR1


Title: Learning and consolidating patterns in experience

Abstract: There is a fundamental tension between storing discrete traces of individual experiences, which allows recall of particular moments in our past without interference, and extracting regularities across these experiences, which supports generalization and prediction in similar situations in the future. This tension is resolved in classic memory systems theories by separating these processes anatomically: the hippocampus rapidly encodes individual episodes, while the cortex slowly extracts regularities over days, months, and years. This framework fails, however, to account for the full range of human learning and memory behavior, including: (1) how we often learn regularities quite quickly—within a few minutes or hours, and (2) how these memories transform over time and as a result of sleep. I will present evidence from fMRI and patient studies suggesting that the hippocampus, in addition to its well-established role in episodic memory, is in fact also responsible for our ability to rapidly extract regularities. I will then use computational modeling of the hippocampus to demonstrate how these two competing learning processes can coexist in one brain structure. Finally, I will present empirical and simulation work showing how these initial hippocampal memories are replayed during offline periods to help stabilize and integrate them into cortical networks. Together, the work provides insight into how structured information in our environment is initially encoded and how it then transforms over time.

Mai, 10th- Andrea Martin (MPI for Psycholinguistic)

Geog Sciences G.11N SR

Title: On neural systems, oscillations, and compositionality 

Abstract: There continues to be vibrant controversy about the fundamental relationship between the information in biological signals and the neural systems that represent and process them. Compositionality is a property of a system such that the meanings of complex entities are derived from the meanings of constituent entities and their structural relations. It is a crucial part of what enables human thought and language to “make infinite use of finite means,” but also part of what makes human thought and language difficult to account for within extant theories of cognition, artificial intelligence, and human neurobiology. I focus on this foundational puzzle and discuss the computational requirements, including the role of neural oscillations, for what I believe is necessary in order to compose structures and meanings within the constraints of a neurophysiological system.

Mai, 3th – Camin Dean

Geog Sciences G.11N SR1


April, 18th – Bridget Lumb

Geog Sciences G.11N SR1


April, 11th – Chris Bailey

43 Woodland Rd G.10 LR


April, 4th – Naoki Masuda

43 Woodland Rd G.10 LR

Title: Atypical intrinsic neural timescale in autism
Abstract: How long neural information is stored in a local brain area reflects functions of that region and is often estimated by the magnitude of the autocorrelation of intrinsic neural signals in the area. Here we investigated such intrinsic neural timescales in high-functioning adults with autism. By analysing resting-state fMRI data, we identified shorter neural timescales in the sensory/visual cortices and a longer timescale in the right caudate in autism. The shorter intrinsic timescales in sensory/visual areas were correlated with the severity of autism, whereas the longer timescale in the caudate was associated with cognitive rigidity. Moreover, the intrinsic timescale was correlated with local grey matter volume. This study shows that functional and structural atypicality in local brain areas is linked to higher-order cognitive symptoms in autism. The talk is based on our recent paper: Takamitsu Watanabe, Geraint Rees & Naoki Masuda.

March, 29th- Sam Berens (University of York)

43 Woodland Road, LR G.10

Title: Learning and memory in an uncertain world
We often need to pick up on subtle patterns and learn complex associations in our environment; even when its unclear which pieces of information are important. How is this achieved? I will discuss some of my recent behavioural and fMRI work exploring how we are able to acquire knowledge under uncertain conditions and in the absence of feedback (so-called ‘unsupervised learning’). These studies test various computational models of learning, investigate whether some types of information are preferentially retained or consolidated, and examines the role of metacognitive learning intentions.


March, 22th- Gareth Barker (University of Bristol)

43 Woodland Rd G.10 L

Title: There and back again: Investigations into associative recognition memory network function.

Abstract: Associative recognition memory, our ability to form an association between an object and its spatio-temporal context, is critical for everyday memory function. A network of brain regions critical for associative recognition memory has been identified, however how these brain regions function as a network during associative recognition memory formation is poorly understood at present. We investigated the role of connections between three key nodes in the network, the hippocampus, medial prefrontal cortex and nucleus reuniens of thalamus, by using a combination of optogenetic and chemogenetic approaches.

By manipulating specific connections within this thalamo-cortico-hippocampal memory network, we have revealed that distinct types of associations rely on anatomically distinct projections and have identified distinct, but interleaving circuits for associative recognition memory encoding and retrieval.

March, 15th- Rui Ponte Costa (University of Bristol)

43 Woodland Rd G.10 LR


Title: Powerful learning via cortical microcircuits

Abstract: Cortical circuits exhibit intricate excitatory and inhibitory motifs, whose computational functions remain poorly understood. I will start out by introducing our work on how state-of-the-art recurrent neural networks used in machine learning may be implemented by cortical microcircuits. In addition, our new results suggest that such biologically plausible recurrent networks exhibit better learning of long-term dependencies. However, learning in such networks relies on solving the credit assignment problem using the classical backpropagation algorithm that appears to be biologically implausible. I will finish my talk discussing our recent work on a biologically plausible solution to the credit assignment problem using well-known properties of cortical microcircuits, which approximates the backpropagation algorithm. Overall, our work demonstrates how cortical microcircuits may enable powerful learning in the brain.


March, 8th- Helen Barron (University of Oxford)

43 Woodland Rd G.10 LR


Title: Inhibitory engrams in memory storage and recall

Abstract: Memories are thought to be represented in the brain by activity in groups of neurons described as memory engrams. Although memory engrams are typically thought to be made up of excitatory neurons, several recent studies suggest that inhibitory neurons also contribute. Indeed, by matching their excitatory counterparts, selective inhibitory interneurons may facilitate a stable storage system that allows memories to lie quiescent unless the balance between excitation and inhibition is perturbed. Here I will present a set of studies that show evidence for selective neocortical inhibition in the human brain using ultra-high field 7T MRI and brain stimulation. I will show that matched excitatory-inhibitory engrams provide a stable storage mechanism for neocortical associations, and protect memories from interference. Finally, I will explore how neocortical memory engrams might interact with the hippocampus during recall, to selectively perturb excitatory-inhibitory balance.

March, 1st- Natalie Doig (University of Oxford)

43 Woodland Rd G.10 LR


Title: Structure is Function: Cellular and Network Substrates of Basal Ganglia Dynamics

Abstract: In order to fully understand how the dynamic functions of the nervous system are realised we must evaluate its structure through static measures. In this talk I will discuss two studies which employed a range of neuroanatomical methods to reveal specific cellular and network principles of the organisation of the basal ganglia. In the first study I will discuss the use of modern trans-synaptic tracing techniques to examine the cell type selective connections between nuclei of the basal ganglia. Second, I will highlight the features of a novel connection between the dorsal hippocampus and the nucleus accumbens that shapes memory guided appetitive behaviour. Using these examples, I would like to promote a discussion on the advantages and disadvantages of specific neuroanatomical techniques and what they can tell us about the substrates underlying the neural dynamics of the basal ganglia.

February, 22th- Maria Wimber (University of Birmingham)

43 Woodland Rd G.10 LR


Title: Tracking the temporal dynamics of memory reactivation in the human brain

Abstract: Our memories are not static. Each attempt to retrieve a past event can adaptively change the underlying memory space. Here I discuss my work on the neurocognitive mechanisms that enable the selective retrieval of episodic memories. I present behavioural and electrophysiological (M/EEG) work that provides insight into how a memory trace unfolds in time during retrieval, on a sub-trial scale. Further, I show evidence from a series of fMRI studies in which we track the representational changes that occur in a memory trace over time and across repeated retrievals. The latter findings demonstrate that retrieval adaptively modifies memories by strengthening behaviourally relevant and weakening behaviourally irrelevant, interfering components. Together, this work sheds light onto the neural dynamics of the retrieval process, and informs theories of adaptive memory.

February, 15th- Jim Dunham (University of Bristol)

43 Woodland Road, G.10 LR

Computing pain – Real time signal processing in human pain nerves.

January, 18th-  Quentin Huys (UCL)

Geog Sciences G.11N SR1

Perceptual conditioning


January, 11th – Vitor Lopes dos Santos (Oxford)

Life sciences G14

Neural oscillations

In this talk, I would like to discuss general concepts regarding the study of neuronal oscillations. What does it take for an event to be defined as an oscillation? What is there beyond frequency? Why are oscillations important (are they?)? I will use CA1 oscillations as main case studies, particularly my recent published results (Lopes dos Santos et al. 2018) to discuss such points and more. My aim is to engage in an informal debate shaped by the thoughts of the audience as much as my own.

Winter 2018

September, 21st – Mark Humphries (University of Nottingham)
3.34 Physics Building
The plasticity of population activity in prefrontal cortex is independent of learning
The prefrontal cortex is thought to represent our knowledge about what action is worth doing in which context. But we do not know how the activity of neurons in prefrontal cortex collectively changes when learning which actions are relevant. Here we show in a trial-and-error task that population activity in rat prefrontal cortex is persistently changing, irrespective of whether the animal shows evidence of learning. Only during overt learning of the correct action are the accompanying changes to population activity carried forward into sleep, suggesting a long-lasting form of neural plasticity. Our results suggest that representations of relevant actions in prefrontal cortex are acquired by reward imposing a direction onto ongoing population plasticity.

 September, 28th – Tom Baden (University of Sussex)
3.34 Physics Building
The Evolution of Function in the Brain: What can we learn from the vertebrate retina? 
Sighted animals use their eyes in vastly different ways, and therefore evolved a staggering array of visual specialisations to navigate their individual visuo-ecological niches. These specialisations are deeply rooted at every level of visual systems, from the optical properties of eye to functional and structural retuning of neuronal microcircuits in the retina and brain. Taking advantage of the exquisite experimental accessibility of the larval zebrafish visual system and drawing on available knowledge gathered in the retina of other species with different visuo-ecological demands, I will present our lab’s recent efforts to better understand how animals can retune their retinal circuits for efficient sensory processing. Focussing on colour vision, I will highlight how zebrafish use different circuit motifs in different parts of their eyes to simultaneously support differential visual requirements imposed by the need for feeding, predator avoidance and object recognition.
Key References
Zimmermann MJY*, Nevala NE*, Yoshimatsu T*, Osorio D, Nilsson DE, Berens P, Baden T § . 2018. Zebrafish differentially process colour across visual space to match natural scenes. Current Biology 28(1-15).
Franke K*, Berens P*, Schubert T, Bethge M, Euler T § , Baden T § . 2017. Inhibition decorrelates visual feature representations in the inner retina. Nature; doi: 10.1038/nature21394. link. 
 Baden T*, Berens *P, Franke K*, Roman Roson M, Bethge M, Euler T § . The functional diversity of mouse retinal ganglion cells. Nature. doi:10.1038/nature16468.

October, 5th – Demian Battaglia (University Aix-Marseille)
Physics 3.34
Perception, cognition and behavior rely on flexible communication between microcircuits in distinct cortical regions.
It has been proposed based on growing experimental evidence that changing patterns of oscillatory coherence support flexible information routing. The stochastic and transient nature of oscillations in vivo, however, is hard to reconcile with such a function.
Here, through a computational modelling approach, we add a new chapter to this debate between “oscillo-partisans” and “oscillo-skeptics”, by showing  that models of cortical circuits near the onset of oscillatory synchrony are well able to selectively route input signals despite the short duration of oscillatory bursts and their stochastic-like irregularity. In canonical multi-areal circuits, we find that gamma bursts spontaneously arise with matched timing and frequency and that they organise information flow by large-scale routing states. We thus hypothesise that a self-organized network-wide re-organization of routing could be induced by suitable weak control perturbations or minor modulations of background activity.
Information routing constitutes nevertheless just a component of neural information processing by neural circuits. Moving to the analysis of actual electrophysiological recordings in hippocampus, enthorinal cortex and prefrontal cortex of anaesthetised and sleeping rats, we investigate whether dynamic changes between oscillatory modes also affect ongoing computational manipulations of information within local circuits, beyond inter-circuit routing. Through an unsupervised algorithmic approach, we are able to identify a multiplicity of internal “computing micro-states”, characterized by the flexible recruitment of alternative hub neurons, transiently specialising in different primitive operations of information processing (buffering and funneling). We find that global oscillatory states have an impact on both the “dictionary” of available computing micro-states and on the “syntax” of their sequences, whose complexity is systematically boosted by e.g. the presence of theta oscillations vs slow-oscillation dominated states.


October, 19th – Ruth Betterton (University of Bordeaux)
Physics 3.34
A biophysical network model of CA3, hippocampus: functional architecture and learning induced changes
A key function of the brain is the storage and recall of information as memories. The hippocampus and specifically area CA3 are involved in the rapid encoding of short-term spatial, episodic, and contextual memory. A unique feature of the CA3 network is the presence of recurrent excitatory cell connectivity which has led to the theory that CA3 acts as an attractor or auto-associative network. It is thought that, during encoding of new memories connection weights between activated excitatory cells within CA3 are rapidly enhanced through Hebbian plasticity creating a micro-network of cells known as an assembly. The potentiated assembly provides an attractive candidate for the location of the neuronal ‘engram’ or cellular correlate of memory. Using a combination of mathematical modelling and in vitro slice recordings we are gathering evidence of this theory of the importance of recurrent connectivity in hippocampal function. Presented here is the development of a network model of biophysical, multi-compartmental neurons carried out in the simulation environment NEURON. The network includes many features of the in vivo CA3 region including specific inputs, recurrent connectivity and region specific plasticity rules.

November, 2nd – Gareth Barnes (UCL)
Physics 3.34
A new generation of MEG scanners
I will talk about collaborative work between University College London and the University of Nottingham to use optically pumped magnetometers (OPMs) for human brain imaging. These sensors have comparable sensitivity to current cryogenic devices but do not require cooling. This means that the sensor array can be worn (rather than climbed into) and the smaller separation between sensor and brain means optimal (and improved) signal to noise ratio in all subject cohorts. I will talk about our initial modelling and experimental work with these new sensors. One of the exciting advances has been to keep these arrays operational during head-movement through a static magnetic field. This has opened up many new clinical and neuroscientific possibilities and I will talk about some of our experiences with these new paradigms.

November, 9th – George Stothart (University of Bath)

Physics 3.34

Using Fast-Periodic-Visual-Stimulation to assess cognition in neurological disorders 

 Fast Periodic Visual Stimulation (FPVS) provides a new objective method for assessing an individual’s ability to discriminate between different categories of visual stimuli. Using a combination of steady state visual evoked potentials and oddball paradigms it has been demonstrated to be a powerful measure of visual discrimination in single subjects. Importantly what defines the visual categories can range from low-level perceptual properties to abstract cognitive properties. We have adapted this approach to examine a range of cognitive processes and will demonstrate that the technique can be used to assess the integrity of semantic categorisation, short term memory and visuo-spatial processing in single subjects in as little as 3 minutes EEG recording time. The implications for the objective assessment of cognition in dementia and the potential as an early diagnosis tool will be discussed.



November, 23th – Jiaxiang Zhang (Cardiff University)
Physics 3.34

The neurocognitive mechanisms of voluntary decision  

We can voluntarily make decisions to fulfil our goals and desires, even when all the options are similar to each other. This talk will discuss our work on using brain imaging, electrophysiology and computational modelling to understand the cognitive processes underlying such voluntary decisions. First, I will present fMRI evidence that during voluntary decision, a decision network in the medial frontal cortex accumulates action intention until a threshold is reached. Second, the behavioural randomness in a sequence of decisions fluctuates over time and correlated with both fMRI and MEG activity in the frontopolar cortex. This suggests a cortical network sensitive to information regularities, which concurrently monitor the choice in voluntary decisions. Last, I will present recent results on how perceptual salience and action outcomes affect the behavioural, EEG, and metacognitive measures in voluntary decisions. Our results highlight the potential and challenges of establishing a neurobiological theory of voluntary behaviour in humans.


November, 30th –  Dimitrije Marković (TU Dresden)
Physics 3.34

Anticipating changes: decision-making with temporal expectations 

Being able to experience time and build temporal expectations about future events is essential for our everyday activities and survival. Despite the central role that time plays in our lives, the neuronal and computational mechanisms that link our experience of time with decision-making remain poorly understood. In this talk, I will focus on the computational underpinning of decision-making with temporal expectations and present a probabilistic behavioural model that enables a systematic investigation of the interplay between temporal expectations and behaviour. The central assumption here is that humans form prior beliefs about the temporal regularities of a dynamic environment; these beliefs shape both the inference and the planning process. Using a sequential reversal learning task, I will illustrate the key properties of the model and demonstrate how it can be applied to behavioural data to infer prior beliefs of participants and to investigate interindividual behavioural differences.

Summer 2018

May, 11th – Rafal Bogacz (University of Oxford)
Life Sciences G13

Title: Theory of reinforcement learning and motivation in the basal ganglia

This talk will suggest a simple mathematical description of how subcortical circuits in the basal ganglia select actions on the basis of past experience and the current motivational state. According to the presented theory, the basal ganglia evaluate the utility of considered actions by combining the positive consequences (e.g. nutrition) scaled by the motivational state (e.g. hunger) with the negative consequences (e.g. effort). The theory proposes how the basal ganglia compute utility by combining the positive and negative consequences encoded in the synaptic weights of striatal Go and No-Go neurons, and the motivational state carried by neuromodulators including dopamine. Furthermore, the theory suggests how the striatal neurons learn separately about payoffs and costs of actions. The model accounts for the effects of dopaminergic modulation on behaviour, and patterns of synaptic plasticity in striatum.



May, 18th –  Simon Thorpe ( CerCo UMR 5549, CNRS-UT3)

Priory Road Complex, Senior Common Room, Level 2 (2D17 )
How can the brain store sensory memories that can last a lifetime? I will argue that if neurones can be made so selective that they remain silent unless they are presented with something close to the original stimuli (effectively Grandmother cells), they can keep their selectivity for very long periods. This suggests that the long term memory store may consist of large numbers of silent neurones (neocortical dark matter). I will describe some recent research showing that both the visual and auditory systems can store long lasting sensory memories  with only a small number of repeats. We have also some suggestions for Spike-Time Dependent Plasticity Rules that are capable of allowing this sort of rapid sensory learning.


May, 25th – Katarina Kolaric (University of Bristol)

Life Sciences G13
Title: The role of mossy cells in regulating the local dentate gyrus circuit and pattern separation 

The function of mossy cells in the dentate gyrus of the hippocampus, and their role in pattern separation memory, is still largely unknown. In this talk, I will present my PhD work, which investigated the role of mossy cells in the local dentate gyrus circuit using immunohistochemistry, in vitro electrophysiology and computational modelling techniques. Moreover, I will discuss how mossy cells are implicated in pattern separation memory by presenting behavioural data from a transgenic mouse model that specifically lack mossy cells. Finally, I will give a brief overview of my current work at the Integrative Epidemiology Unit (IEU), where I use Mendelian Randomisation as a method to detect potential risk factors in the human population for cognitive decline and Alzheimer’s Disease. 


June, 1st – Robert Schmidt  (University of Sheffield)
G13 Life Sciences
 Title: Basal ganglia transmission of motor signals to the thalamus: effect of correlations and sensory responses:      

One prominent feature of Parkinson’s disease is the emergence of correlated activity in basal ganglia output neurons. In contrast, in healthy animals, the activity in basal ganglia output regions is usually uncorrelated, potentially due to “active decorrelation”. We investigated the effect of correlations among basal ganglia output neurons on the transmission of motor signals via rebound spikes in a Hodgkin-Huxley model of a thalamocortical neuron. We found that correlations in the basal ganglia output decrease the signal-to-noise ratio in the transmission of motor signals to the thalamus, potentially related to the emergence of motor symptoms in Parkinson’s disease. In addition, our model indicates that thalamocortical neurons may be a key site for the integration of sensory and motor signals.

June, 8th – Adam Packer (University of Oxford)

G13 Life Sciences
Title: Technologies for all-optical interrogation of neural circuits in behaving animals

Neural circuits display complex spatiotemporal patterns of activity on the millisecond timescale during behavior. Understanding how these activity patterns drive behavior is a fundamental problem in neuroscience, and remains a major challenge due to the complexity of their spatiotemporal dynamics. The ability to manipulate activity in genetically defined sets of neurons on the millisecond timescale using optogenetics has provided a powerful new tool for making causal links between neuronal activity and behavior. I will discuss novel approaches that combine simultaneous two-photon calcium imaging and two-photon targeted optogenetic photostimulation with the use of a spatial light modulator (SLM) to provide ‘all-optical’ readout and manipulation of the same neurons in vivo. This approach enables reading and writing of activity in neural circuits with single-cell resolution and single action potential precision during behavior. I will describe the power, limitations and future potential of this approach; and discuss how it can be used to address many important problems in neuroscience, including transforming our search for the neural code and the links between neural circuit activity and behavior.

June, 15th – Tamar Makin (University of Oxford)

1.58 Queens Building

Title: From phantoms to artificial limbs
In this seminar I will present recent results and ideas about how the brain adapts to extreme changes in resources, due to hand loss and the need to pick up adaptive behavioural strategies. I will explore the neural correlates of a range of “alternative hands”, including phantom hands, extraordinarily dexterous foot usage, and artificial limbs. Based on these findings I will challenge some of the basic textbook assumptions about the triggers and barriers of brain plasticity, and suggest alternative frameworks, with reference to recent BMI developments.

Spring 2018

January 12th – Matt Jones (University of Bristol)

E204, Chemistry

Working memory: some stuff on my mind

Working memory (WM) reflects the temporary storage and manipulation of information pertinent to online behaviour. Sustained activity in recurrently interconnected prefrontal cortical neurons is commonly credited as central to WM. However, recent recordings of prefrontal neural dynamics portray an increasingly complex picture involving mixed selectivity, trajectories through state space, spectral signatures and other daunting words.

Could implanting arrays of microelectrodes and optrodes into and around rat prefrontal cortex help? Maybe – I will try to set some Jones Lab findings in the context of this knotty neurodynamical nebula.


January 19th – Giovanni Diana (King’s College London)

E204, Chemistry

Inference of functional subtypes and spontaneous neuronal assemblies in the zebrafish optic tectum

Understanding the functional diversity of the nervous system is one of the most prominent questions in neuroscience. Neurons are often classified based on their tuning curves under selected stimuli, however describing how neural computation is orchestrated within a complex network requires a paradigm shift from single neurons to neuronal ensembles emerging in the brain. By applying functional imaging of calcium activity in the zebrafish optic tectum we could address these questions directly. In the presence of visual stimulation we identified neuronal sub-types involved in prey-capture and escape response. In the absence of visual input, tectal neurons display synchronous activity events. We will discuss the inference methodologies that can be reliably employed to detect these spontaneous ensembles as well as the clustering techniques used to classify evoked responses.


January 26th – Rasmus Petersen (University of Manchester)

SM3, Maths

Sensation and locomotion in behaving mice: Investigation using ‘Neuropixel’ probes

Despite the fact that almost all regions of the brain are richly interconnected, the standardview of sensory processing is that the subcortical, ascending sensory pathway simply encodes sensory stimuli and relays them onto the cerebral cortex. However, this view is derived from experiments on anaesthetised animals where motor and cognitive circuits are disengaged, and sensation can only be studied in a reduced, passive form. Using the whisker system as a model, we have developed a paradigm for studying active sensation in behaving mice that permits us to record the activity of neurons at the same time as measuring the mechanical input to the whiskers. In our most recent work, in collaboration with Karel Svoboda, we have used high-density Neuropixel silicon probes to record the spiking activity of neurons across >10 brain regions (including Ventro Posterior Medial thalamus, Posterior Medial thalamus and ZonaIncerta) whilst at the same time measuring sensory input to the whiskers and locomotor velocity. Surprisingly, even in relay nuclei conventionally considered simply to be sensory relays, we observed modulation of neural activity by locomotion velocity. Indeed, we observed such modulation in all recorded areas, including the hippocampal formation. Our data suggest that sensory processing, right from the earliest stages of the Central Nervous System, is substantially dependent on motor control context.


February 2nd – Melanie Stefan (Edinburgh Medical School)

SM3, Maths

It’s not what it looks like: Counter-intuitive effects in synaptic biochemistry

A lot of what we know about Biochemistry comes from tightly controlled experiments with large numbers of molecules in well-mixed test-tubes. Real biological systems such as dendritic spines are often very different from that: Absolute numbers of any one molecular species can be very small, their spatial distribution is often highly non-uniform, and they compete for ligands that are in short supply. This can give rise to behaviours that are difficult to predict and often counter-intuitive. In such cases, computational modelling can uncover and explain system behaviours that are otherwise difficult to understand. In this seminar, I will talk about a few examples from our past and ongoing research on Calcium-regulated synaptic proteins.


February 9th – Nelson Totah (Max Planck Institute)

E204, Chemistry

The locus coeruleus is a complex and differentiated neuromodulatory system

Understanding forebrain neuromodulation by the noradrenergic locus coeruleus (LC) is fundamental for cognitive and systems neuroscience. The diffuse projections of individual LC neurons and their presumably synchronous spiking have long been perceived as features of the global nature of noradrenergic neuromodulation. Yet, the commonly referenced “synchrony” underlying global neuromodulation, has never been assessed in a large population, nor has it been related to projection target specificity. We recorded up to 52 single units simultaneously (3164 unit pairs in total) in rat LC and characterized projections by stimulating 15 forebrain sites. Spike count correlations were low and, surprisingly, only 13% of pairwise spike trains had synchronized spontaneous discharge. Notably, even noxious sensory stimulation did not activate the entire population. We also identified a novel firing pattern: infra-slow (0.01-1 Hz) fluctuations of LC unit spiking, which were also asynchronous across the population. A minority of LC neurons, synchronized possibly by gap junctions, was biased toward restricted (non-global) forebrain projection patterns. Finally, we characterized two types of LC single units differing by waveform shape, propensity for synchronization, and interactions with cortex. These cell types formed finely-structured ensembles. Our findings suggest that the LC conveys a highly complex, differentiated, and potentially target-specific neuromodulatory signal.


February 16th – John Grogan (University of Bristol)

SM3, Maths

Dopamine and Parkinson’s disease’s effects on working memory: behaviour, pharmacology and genetics

Parkinson’s disease (PD) causes cognitive as well as motor impairments, some of which are improved by the dopaminergic medication prescribed for motor symptoms, while others are exacerbated by it. Working memory (WM) is one such function that shows both improvements and impairments caused by dopaminergic medication in healthy people and people with PD.

We have run several studies into different aspects of WM: using the digit span in PD patients and healthy older adults given levodopa; examining the influence of genetic polymorphisms in dopamine-related genes on digit span and n-back tasks; and looking at how dopaminergic medication affects spatial WM in PD patients. We use a simple model to analyse the spatial WM task which allows us to separate different sources of errors in participants’ responses, so we can examine the precision of memory, and the mis-binding of items and locations.

These diverse approaches allow us to combine large-sample, high-powered analyses with smaller more sensitive tasks to zero in on the range of WM deficits caused by PD, and how dopamine affects these deficits. This can help us understand dopamine’s function in WM in healthy brains.


February 23rd – Friedemann Zenke (University of Oxford)

E204, Chemistry

Making Cell Assemblies: What can we learn about plasticity from spiking neural network models?

Long-term synaptic changes are thought to underlie learning and memory. Hebbian plasticity and homeostatic plasticity work in concert to combine neurons into functional cell assemblies. This is the story you know. In this talk, I will tell a different tale. In the first part, starting from the iconic notion of the Hebbian cell assembly, I will show the difficulties that synaptic plasticity has to overcome to form and maintain memories stored as cell assemblies in a network model of spiking neurons. Teetering on the brink of disaster, a diversity of synaptic plasticity mechanisms must work in symphony to avoid exploding network activity and catastrophic memory loss – in order to fulfill our preconception of how memories are formed and maintained in biological neural networks. I will introduce the notion of Rapid Compensatory Processes, explain why they have to work on shorter timescales than currently known forms of homeostatic plasticity, and motivate why it is useful to derive synaptic learning rules from a cost function approach. Cost functions will also serve as the motivation for the second part of my talk in which I will focus on the issue of spatial credit assignment. Plastic synapses encounter this issue when they are part of a network in which information is processed sequentially over several layers. I will introduce several recent conceptual advances in the field that have lead to algorithms which can train spiking neural network models capable of solving complex tasks. Finally, I will show that such algorithms can be mapped to voltage-dependent three-factor Hebbian plasticity rules and discuss their biological plausibility.


March 2nd – Mark Olenik (University of Bristol)

SM3, Maths

Modelling struggling tadpoles: Interactions of strong and weak synaptic coupling for burst rhythm generation

Xenopus hatchling tadpoles “struggle” when held at their tail, rhythmically bending their body in an attempt to free themselves. During struggling, neurons on opposite sides of the tadpole spinal cord fire bursts of spikes in alternation, resulting in motor activity and the rhythmic bends of the whole body. A central question of interest in tadpole struggling is the role of the excitatory and inhibitory coupling for the rhythm generation. In this talk we will therefore study a reduced biophysical model of tadpole struggling that contains weakly excitatory neurons and strongly inhibitory neurons, where the latter are also subject to short-term synaptic depression. We will discuss approaches to understanding the dynamics of such a network, focusing on reducing the complex model equations to a more tractable, and intuitive, quantity. For the reduction we will employ two useful mathematical techniques: geometric singular perturbation theory and phase response curves.


March 9th – Ramin Azodi Avval (University of Tübingen)

E204, Chemistry

Characterization of cortico-subthalamic networks during deep brain stimulation surgery in Parkinson’s disease

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a well-established symptomatic treatment for Parkinson’s diseases (PD). However, knowledge on local electrophysiological biomarkers within the STN and their cortical connectivity profile is still scarce. Such information would be necessary for optimal positioning of the DBS leads based on PD network pathophysiology. This talk describes the introduction and exploration of a novel technique for electrophysiological measurements during DBS surgery. Combined electroencephalography (EEG) with stepwise local field potentials recordings during insertion of the DBS lead was performed intraoperatively, thereby, allowing to capture local STN and cortico-subthalamic physiology with high speactral and spatial specificity. Our results revealed that strong beta oscillatory activity in the STN was located more dorsally than the STN-ipsilateral motor network phase coupling; the respective frequency bands were in the low and high beta-band, respectively. Moreover, the spot within the STN, where this STN-cortical phase coupling occurred, correlated highly with the STN spot where the phase of beta oscillations modulated the amplitude of high-frequency oscillations. This STN location was furthermore, characterized by information flowed from the ipsilateral motor cortex to the STN in the high beta-band suggesting a pathologically synchronized network with a direct STN-motor cortex connection via the hyperdirect pathway. Interestingly, the very same STN spot showed a resonance like responses to electrical stimulation suggesting a decoupling of pathologically synchronized STN-motor cortex connectivity during therapeutic DBS. In conclusion, we provide first evidence that macroelectrode recordings with the chronic electrode concurrent with EEG recordings are a reliable method for STN localization during DBS surgery. Additionally, combining LFP and EEG recordings during mapping of STN offered a new way of DBS targeting on the basis of pathological local biomarkers and network activity.


March 16th – Tara Keck (UCL)

SM3, Maths

Interactions between different Hebbian and homeostatic plasticity

Homeostatic plasticity is thought to be mediated by mechanistic changes, such as synaptic scaling and shifts in the excitation/inhibition balance. These mechanisms are thought to be distinct from the Bienenstock, Cooper, Munro (BCM) learning rule, where the threshold for the induction of long-term potentiation and long-term depression slides in response to changes in activity levels; however, both mechanisms produce a homeostatic response of a relative increase (or decrease) in strength of excitatory synapses in response to overall activity-level changes. I will present a model that suggests how these two mechanisms may interact to facilitate firing-rate homeostasis, while maintaining functional properties of neurons.


March 23rd – Petroula Laiou (University of Exeter)

E204, Chemistry

Evaluation of epilepsy surgery using mathematical modelling

Surgery is one option for treating pharmacoresistant epilepsy patients. In this treatment, brain tissue is resected to render a patient free from seizures. However, epilepsy surgery is often ineffective. A basic reason for this is our insufficient understanding of the generation of seizures in the brain network. Mathematical modelling can be used to study normal and abnormal dynamical states of the brain. Moreover, it can elucidate the effects of brain surgery by quantifying the contribution of different brain regions (nodes in the brain network) to the generation of seizures. In this talk, I will present a mathematical framework which aims to identify the optimal set of nodes that has to be removed from a brain network in order to obtain seizure-free patients. The ultimate goal of this approach is to provide a support tool for epilepsy surgery to help in the presurgical evaluation.


April 27th – Mark Olenik (University of Bristol)
W415, Chemistry
Modelling struggling tadpoles: Interactions of strong and weak synaptic coupling for burst rhythm generation
Xenopus hatchling tadpoles “struggle” when held at their tail, rhythmically bending their body in an attempt to free themselves. During struggling, neurons on opposite sides of the tadpole spinal cord fire bursts of spikes in alternation, resulting in motor activity and the rhythmic bends of the whole body. A central question of interest in tadpole struggling is the role of the excitatory and inhibitory coupling for the rhythm generation. In this talk we will therefore study a reduced biophysical model of tadpole struggling that contains weakly excitatory neurons and strongly inhibitory neurons, where the latter are also subject to short-term synaptic depression. We will discuss approaches to understanding the dynamics of such a network, focusing on reducing the complex model equations to a more tractable, and intuitive, quantity. For the reduction we will employ two useful mathematical techniques: geometric singular perturbation theory and phase response curves.

Autumn 2017

September 22nd – Antonis Asiminas (University of Edinburgh)

SM4, Maths

Sustained correction of associative learning deficits following brief, early treatment in a rat model of Fragile X Syndrome.’ Fragile X Syndrome (FXS) is a major heritable cause of intellectual disability and autism. Despite the early emergence of symptoms associated with FXS, it is still not clear whether treatments restricted to early development of brain circuits can permanently prevent impairments in cognitive function. In the work I’ll present, we examine the developmental trajectory of learning and memory in a rat model of FXS using spontaneous object exploration tasks and tested whether temporary early intervention can prevent the emergence of cognitive deficits later in life.


September 29th – Ole Jensen (University of Birmingham)

SM3, Maths

Hunting For Phase Coding

In 1993 O’Keefe and Recce demonstrated that hippocampal place cells fire in phase specific manner with respect to ongoing theta oscillations. This suggests that slower oscillations serve to organize a code in which individual objects are activated as a sequence within a cycle. I will discuss the functional consequences of this phenomenon and to what extend the principle generalized to other rhythms such as human alpha oscillations.

Suggested readings:

Jensen, O., Gips, B., Bergmann, T.O. and Bonnefond, M. (2014) Temporal coding organized by coupled alpha and gamma oscillations prioritize visual processing. Trends in Neurosciences
Lisman, J.E. and Jensen, O. (2013) The theta-gamma neural code. Neuron 77:1002-1016


October 20th – Lisa Genzel (Donders Institute)

Sleep for systems consolidation

Distinct forms of memory consolidation (cellular and systems) influence the persistence of spatial memory within the hippocampus (cellular) and following hippocampal-neocortical interactions (systems). Factors influencing these processes include: (1) novelty exposure that enhances the persistence of hippocampal traces via neuromodulation; and (2) sleep that aids systems consolidation and thus cortical memory. In a sequence of experiments, we could show that sleep leads to systems consolidation via learning-dependent cortical consolidation. In contrast, novelty tags a memory to remain more hippocampal by increasing gene expression. Further, I will show evidence that different behaviour such as novelty and normal learning influence hippocampal-cortical communication during subsequent sleep and such behaviours after learning influence the behavioural expression of memory.


October 27th – Pooran Dewari (University of Edinburgh)

LT1, 43 Woodland Road

An efficient and scalable CRISPR/Cas9 pipeline for epitope tagging in mammalian stem cells

CRISPR-Cas9 technology has revolutionized genome editing at an unprecedented scale across multiple organisms and cell types. Knock-in of small epitope tags into endogenous genes simplifies antibody-based assays, overcoming issues of specificity and sensitivity. We have developed a highly efficient and scalable Cas9 ribonucleoprotein(RNP)-assisted method for epitope tagging in mouse and human primary neural stem (NS) cells and glioblastoma tumour-derived cultures. Delivery of RNP complexes containing synthetic dual-guide RNA (crRNA 36-mer and tracrRNAs 67-mer) facilitates efficient knock-in of V5 tag (5-30%) in mouse NS cells without requirements for any selection strategy. Similar efficiencies were achieved in human NS and glioma stem cells. Importantly, with these optimized conditions and a newly developed web-based tool for crRNA and donor DNA design, we were able to demonstrate medium-throughput epitope tagging in a 96-well plate format. 192 transcription factors (key regulators of neural stem cell self-renewal and differentiation) were tested for tagging in parallel, and 60 of these were effectively tagged with V5. Our method provides a step-change in our ability to interrogate mammalian proteins in stem cells and their glioma counterparts. As a proof-of –principle, we used the newly tagged Sox2-V5 glioma cell lines and performed ChIP-SICAP (selective isolation of chromatin associated proteins) to identify on-chromatin partners of Sox2 transcription factor. In summary, we have developed a highly efficient and scalable pipeline for tagging of endogenous proteins in mouse and human neural and glioma stem cells using off-the-shelf reagents. Our method will enable elucidation of the subcellular localisation and interaction partners for a multitude of mouse and human proteins.


November 3rd – James Rankin (University of Exeter)

Dynamics of visual and auditory perception

Ambiguous sensory stimuli provide a unique opportunity to characterize intrinsic neural dynamics of cortical processing. The perceptual interpretation of fixed sensory stimuli can switch spontaneously due to ambiguity, so-called perceptual bistability. Common examples discussed here include binocular rivalry, ambiguous motion and the auditory streaming paradigm. Dynamical systems modelling has helped to reveal the common computational mechanisms behind perceptual bistability. A canonical model that incorporates three competition mechanisms (mutual inhibition, slow adaptation and noise) is adapated to investigate processing across the different sensory modalities. The work helps to reveal the mechanisms behind attention, the role of contrast for visual stimuli and how sudden stimulus changes can drive perceptual switching.


November 10th – Nicholas Robinson (UCL)

LT1, 43 Woodland Road

Hippocampal-Medial Entorhinal Interactions Underlying Memory Encoding and the All-Optical Interrogation of Hippocampal Function

The talk will initially cover work looking at the importance of medial entorhinal cortex activity for the expression of hippocampal neural correlates during epochs of spatial, temporal and object based experience, and the importance of this coding for on going memory performance. The study utilized large scale optogenetic inactivation while simultaneously performing tetrode recordings in animals performing an object-delay-response association task. It will then move onto current work using ‘all-optical physiology’, the 2-photon calcium imaging of the activity of a large population of neurons while using a second light path to provide cellular resolution optogenetic control of a chosen subset of the same population. This allows the manipulation of a functionally defined population of neurons rather than relying on genetic targeting. The technique is being applied to determine the link between specific place field firing, memory and behaviour.


November 17th – Stuart Allan (University of Manchester)


Inflammation and stroke: contributions across the whole patient journey, from risk to recovery

Stroke is a leading cause of death and disability yet there are few effective therapeutic interventions. It is now well recognised that inflammation is a key aspect of the different risk factors for stroke and also contributes to the acute injury. In addition inflammation likely plays an important role in the resolution of ischaemic injury and in the repair and recovery processes in the damaged brain. The pro-inflammatory cytokine interleukin-1 is a key mediator of sterile inflammation and there is a substantial body of evidence to suggest it may be an effective therapeutic target. In this seminar these different aspects of the role of inflammation in stroke will be discussed.


November 24th – Michael Kohl (University of Oxford)

LT1, 43 Woodland Road

Roles of hippocampus, retrosplenial cortex, and neocortex in learning and memory in mice – from synapse to behaviour

We identified a left-right dissociation of synaptic plasticity in the mouse hippocampus. To investigate possible effects on behaviour, we used optogenetics to unilaterally silence hippocampal CA3 pyramidal cells in mice performing hippocampus-dependent memory tasks. Whilst silencing of either the left or the right CA3 caused a short-term memory deficit, strikingly, only left CA3 silencing impaired long-term memory. Our data suggests that memory is routed via distinct left-right pathways within the mouse hippocampus. We are currently investigating whether this hemispheric specialisation extends to neocortical structures involved in associative memory. Recent data implicates the retrosplenial cortex in sensory preconditioning, a form of associative learning, and suggests that retrosplenial cortex aids learning in the neocortex by providing top-down inputs to primary sensory cortex.


December 8th – Andrew Jarman (University of Edinburgh)

LT1, 43 Woodland Road

Mechanotransduction mechanisms in Drosophila mechanosensory neurons

We are interested in both the development and the physiology of mechanosensory neurons, primarily in Drosophila. In this presentation I will concentrate on physiology. I will initially talk about our attempts to define the mechanotransduction process in poorly studied larval stretch receptors that resemble mammalian muscle spindles. This will include some basic modelling, as well as electrophysiology and optogenetics. As time allows, I will also discuss a different class of mechanosensory neuron, required for hearing and proprioception. These neurons appear to generate active movements in response to sound (analogous to the movements of our inner ear hair cells that form the ‘cochlear amplifier’). I will discuss the possible molecular basis of this process.


December 15th – Jess Gaunt (University of Bristol)

LT1, 43 Woodland Road

Exploring changes in the CA1 translatome during associative recognition memory formation
Gene expression is required for long term changes at the synapse underlying memory formation. Messenger RNAs undergoing translation into protein in specific cell types can be profiled using Translating Ribosome Affinity Purification (TRAP; Heiman et al, 2008; Doyle et al, 2008). Profiling the translatome instead of the transcriptome (as in standard RNAseq experiments) enables protein abundance to be estimated more accurately, has greater temporal specificity, and is sensitive to changes in both transcription and translation.
To investigate genome-wide gene expression in rats during associative recognition memory formation, a novel TRAP virus was targeted to neurons in CA1 of the hippocampus using intracerebral injections and combined with Next Generation Sequencing (NGS) andPaired Viewing (PV), a within-subjects paradigm to induce differential gene expression in CA1 when novel and familiar configurations of stimuli are presented (Wan et al, 1999). Leveraging recent developments in bioinformatics, changes in the translatome in the hours following PV were investigated. This talk will discuss making reliable inferences from multi-factor NGS data and interesting features of this complex dataset.

Summer 2017

May 19th – Jonathan Hanley (University of Bristol)

F40, Biomedical Sciences Building

Tuning trafficking and translation with dynamic protein interactions

Synaptic plasticity involves numerous changes to the molecular machinery at synapses. These include alterations in the number and subtype of AMPA receptors expressed at synapses by regulated trafficking, changes in dendritic spine morphology, and modulation of local protein synthesis by fine-tuning translation in dendrites. Dynamic protein-protein interactions are essential components of all of these processes.

In this presentation, I’ll aim to discuss our recent work on how specific protein interactions are regulated in response to the induction of synaptic plasticity to bring about changes in AMPAR trafficking and microRNA-mediated translational repression.


June 2nd – Neil Marrion (University of Bristol)

F40, Biomedical Sciences Building

How does Glutamate leave a vesicle?

The release of neurotransmitter is fundamental to communication between cells.  The excitatory neurotransmitter that mediates rapid transmission in our brains is glutamate.  The synaptic delay between presynaptic activation and the appearance of the postsynaptic response is only 200 μs, so any release mechanism has to be fast.  The standard release mechanism for vesicular glutamate is by ‘full fusion’, a process where the vesicle incorporates fully into the terminal membrane to dump its contents into the synaptic cleft.  This is energetically not favourable and an alternative release mechanism was identified to challenge this.  This alternative mechanism is termed ‘kiss-and-run’, where the vesicle remains intact and simply releases its contents into the synaptic cleft via a narrow pore.  This talk will present a hypothesis and data that support the proposal that ‘kiss-and-run’ exocytosis occurs in the mammalian brain and is the dominant mechanism for glutamate release.


June 9th – Chris Miall (University of Birmingham)

SM3, Maths

The cerebellum as a flexible predictor for motor control and for cognition

The talk will describe my team’s research showing that the cerebellum has an important role in predicting the sensory consequences of actions, a role evidenced by behavioural tasks, by electrophysiology, by interventions including brain stimulation and lesions, and by functional imaging. We have recently explored how these theoretical ideas can be extended to cognitive domains, and I will argue that the human cerebellum is involved in predictive language processing, and may be a general purpose predictor support all cognitive functions.


June 30th – Claire Hales (University of Bristol)

SM3, Maths

Diffusion modelling of ambiguous decision making in rodents

Affective states alter cognitive processes. The measurable consequences of these alterations are referred to as affective biases. In both humans and animals, decision making about ambiguous cues is modified by changes in affective state. This is known as judgement or interpretation bias, and can be measured in rodents using a two-choice ambiguous cue interpretation task. In order to further investigate the decision making processes that underlie judgement biases measured in this task, I apply the Ratcliff diffusion model to rodent behavioural data. I will present results showing how ambiguous cue interpretation is altered by variety of affective state manipulations – both pharmacological and psychosocial, and spanning acute and chronic timescales – as well as the different diffusion model parameters that are altered in judgement biases caused by these various manipulations.



Spring 2017

January 27th – Robert Schmidt (University of Sheffield)

F40, Biomedical Sciences Building
Sensorimotor processing in the basal ganglia leads to transient beta oscillations during behaviour
The basal ganglia have been implicated in motor control including the initiation and suppression of actions. In Parkinson’s disease neural processing in the basal ganglia is impaired, and patients suffer from severe motor symptoms. Signatures of the impaired neural processing include oscillations in the beta band (~13-30 Hz), but beta oscillations have actually also been found in the basal ganglia of healthy animals and may reflect the utilisation of sensory cues for behaviour. To see whether healthy and pathological beta oscillations share neural mechanisms we combined computational models for beta oscillations in Parkinson’s disease with input patterns derived from single unit recordings in healthy rats. We found that movement-related increases in striatal activity lead to transient beta oscillations in the model with a time course that closely matches the experimentally measured oscillations. In addition, our model can account for further unintuitive aspects of beta modulation including beta phase resets following sensory cues and correlations with reaction time. Overall, our model can explain how sensorimotor neural activity in the basal ganglia leads to transient beta oscillations during behaviour.



February 10th – Rosalyn Moran (University of Bristol)

SM4, Maths

Neural Dynamics in Dynamic Causal Modeling
Dynamic Causal Modeling or DCM for electrophysiological responses uses neural mass models and mean field approaches to mimic the population activity observed in local field potentials, and their non-invasive source localised EEG and MEG correlates. In this talk I will describe the generative models underlying DCM for M/EEG and describe the inversion procedures which allows DCMs to be fit to subject specific data. I will outline recent validation studies in autoimmune NMDA encephalitis. Specifically I will demonstrate how DCM’s connectivity estimates, which are based explicitly on ion-channel dynamics, can be recovered to show particular deficits in NMDA-mediated connectivity in these interesting patients. I will finally show some examples of DCM applied to understanding neural hierarchical processing changes with age.


February 17th – Aleks Domanski (University of Bristol)

SM4, Maths

Decoding Hippocampal-Frontal Cortical cell assemblies during spatial working memory
Functionally specialised brain regions such as the hippocampus (HP) and medial prefrontal cortex (mPFC) communicate under cognitive demand (such as during spatial working memory) to share information required for the successful completion of a behavioural task. The circuit mechanisms underlying this process remain poorly resolved, however population coding by “cell assemblies” – transiently synchronised groups of neurons – is favoured as a biologically-plausible candidate.
Here, using rat neurophysiology I will introduce a novel method for detecting cell assemblies from parallel single-unit recordings and present evidence that the activity of inter-structural cell assemblies provides superior and broader information encoding of task-related variables compared to individual single units. Moreover, cell assembly activity is stable in rest periods flanking the behavioural task in expert-performing rats. Together, our work argues that HP-mPFC cell assemblies provide a robust channel binding the specialised information encoded by each brain area.


February 24th – Tony Pickering (University of Bristol)

SM3, Maths

Noradrenaline / Norepinephrine : Facts / Alternate Facts


It is known that noradrenaline is an important neuromodulator in the brain; implicated in processes ranging from alerting, arousal and salience detection through novelty learning and emotional recall and including sensory discrimination, motor control and pain regulation.  Much of this appears to be attributable to the actions of a small cluster of neurones in the pons known as the locus coeruleus (LC).  We have been focussing on the role of the LC in the regulation of pain and have developed some novel tools and approaches to interventionally assess its function both in rodents and in man.  In so doing we have stumbled into the often apparently contradictory world of LC neurobiology and Nor-adrenaline/epinephrine (the clue is in the name(s)).  High profile papers are currently being published on these topics.  I will present the facts / the alternate facts (and their cognate models) and you can be the experts / members of the press who can pull me up on their interpretation.  It’s gonna be so great. (byo wall).

The contrasting views include:

  • LC is a primitive relic / important determinant of complex behaviour in vertebrates.
  • Acts via Noradrenaline / Dopamine release
  • Each neuron projects to the whole brain / to a discrete territory.
  • Volume transmission vs focussed synaptic release.
  • Signalling saliency to the cortex / preventing salient events from reaching the cortex.
  • Whether a modular organising principle can help account for these apparently opposing characteristics.


March 17th – Nina Kazanina (University of Bristol)

SM3, Maths


March 23rd – Krasimira Tsaneva Atanasova (University of Exeter)

SM3, Maths

Socio-motor biomarkers in schizophrenia
In an effort to establish reliable indicators of schizophrenia we have developed a test that could detect deficits in movement and social interactions, both characteristics of the disorder. We asked people to perform movements alone, and to mirror the movements of a computer avatar or a humanoid robot. Using statistical learning techniques we were able to distinguish people with schizophrenia from healthy participants with accuracy and specificity slightly better than clinical interviews and comparable to tests based on much more expensive neuroimaging methods. This methodology could help with diagnosis of schizophrenia and to monitor patients’ responses to treatment, but needs to be tested further before being potentially widely applied in clincal practice.


March 31st – Julijana Gjorgjieva (The Max Planck Institute for Brain Research)

SM3, Maths

Optimal coding in sensory populations

At the output of the retina, some ganglion cell classes consist of paired cell types that have very similar spatiotemporal response properties, except that one type is excited by light increments (ON), the other type by light decrements (OFF). Besides the retina, the ON-OFF dichotomy is also observed in other sensory modalities, which include invertebrate motion vision, thermosensation, chemosensation, audition, and electrolocaton in electric fish. The commonality in the splitting of sensory signals across neural systems and species suggests a strong evolutionary pressure driving the emergence of opposite response types to positive and negative stimuli. Why have the ON and OFF channels dierged in different species and sensory modalities? I will discuss how ON-OFF organization at the level of sensory neuronal populations emerges from an efficient coding strategy to maximize information transfer of incoming stimuli. Our work underscores the significance of external stimulus statistics and neural noise in governing how populations organize to efficiently encode and decode sensory information.


April 7th – Tom Jahans-Price (University of Oxford)

SM3, Maths

Glutamatergic dysfunction leads to a hyper-dopaminergic phenotype: a possible cause of aberrant salience
Current thinking suggests that psychosis is a disorder of aberrant salience. This describes when a stimulus continues to grab inappropriately high levels of attention, and it is thought to be mediated via elevated dopamine (DA) levels, which have been robustly demonstrated in schizophrenia. However, the causes of this DA dysregulation are generally unspecified. Recent large scale GWAS meta-analyses have established genome-wide significant association to schizophrenia for the Gria1 locus which codes for the GluA1 subunit of the AMPA glutamate receptor. GluA1 KO mice have previously been studied in relation to schizophrenia but, notably, striatal whole tissue levels of dopamine and its metabolites appear normal in these animals. However, we might not expect to see changes in dopamine activity in anaesthetised animals, or in a home-cage environment. Indeed, changes in phasic DA responses are likely to be both behaviour-dependent and stimulus-specific.
To test this possibility we have recorded phasic DA signals with high temporal resolution, in freely moving, behaving wild-type and GluA1 KO mice, using fast-scan cyclic voltammetry (FSCV). I will present data showing that phasic dopamine signals in response to neutral light stimuli fail to habituate in Gria1-/- mice, resulting in a behaviourally relevant, hyper-dopaminergic phenotype in these animals. This parallels previous behavioural data from these mice. In addition, phasic dopamine responses to unsignalled rewards were also significantly enhanced in the knockout mice. This provides evidence for behaviourally-relevant hyper-dopaminergic responses in a genetically modified mouse model of glutamatergic dysfunction relevant to schizophrenia.

April 28th – Cian O’Donnell (University of Bristol)

SM3, Maths

Memory processing by molecular signals in neurons

The dominant models of long-term memory formation are based on Hebbian plasticity rules. These rules are usually given solely in terms of electrical spiking activity of pre- and post-synaptic neurons. However, in real cells, expression and consolidation of synaptic plasticity involves a complicated cascade of biochemical signals, protein synthesis, and gene transcription. Surprisingly little is understood about the computational function of these molecular signals for memory. In this talk I will present our recent work on how spatial patterning of protein synthesis in dendrites allows brains to choose what to remember and what to forget. Based on these results, we also propose a novel model for memory generalisation during sleep.


May 5th – Angela Roberts (University of Cambridge)

SM3, Maths

A multidimensional approach to the study of positive and negative emotion regulation in the marmoset prefrontal cortex

Dysregulated emotions are a core feature of many neuropsychiatric disorders involving altered activity in limbic emotional circuitry that includes the amygdala, hippocampus and prefrontal cortex (PFC).  Front line treatments include drugs that target the serotonin system and more recently the glutamate system, but how they work, and in which patient, is poorly understood. Moreover, at least 40% of patients are resistant to such treatments emphasizing the need to understand better the neural circuits and cognitive and behavioural processes that underlie the regulation of positive and negative emotion. The prefrontal cortex is at its most highly developed in primates and so to further our understanding of its regulation of amygdala-dependent emotional learning and expression we have developed models of negative and positive emotional learning and expression in a new world primate, the common marmoset. Since emotional states are composed of both physiological and behavioural components we use an automated telemetry system to allow the simultaneous measurement of behavioural and cardiovascular emotional responses e.g. heart rate and blood pressure, in freely moving marmosets. We employ three main experimental strategies. The first, to determine the effects of localized prefrontal manipulations on emotional states, their impact on activity in downstream targets using fluorodeoxyglucose microPET and their sensitivity to drugs targeting the glutamate and serotonin system. Second, we have initiated a neuroimaging program to characterize the development of prefrontal circuits across childhood and adolescence in the marmoset since the majority of anxiety and mood disorders have their onset during these critical periods of development in humans. The third, to study the impact of known behavioural and genetic risk factors for mood and anxiety disorders, i.e. trait anxiety and a polymorphism in the upstream promotor region of the serotonin transporter gene, on these prefrontal circuits using microPET, structural mri, microdialysis and post mortem mRNA analysis. In this presentation I will illustrate these approaches in the study of anxiety and anhedonia.

Recent review: Shiba et al (2016) Frontiers in Systems Neuroscience 10:12.