Winter&Spring 2023

20th January 2023

Claudia Clopath

1:00 PM online: https://bristol-ac-uk.zoom.us/j/94138286231?pwd=MlRURE1SWjR6OTZCR1Fnak9QbGxhUT09

Meeting ID: 941 3828 6231 Passcode: 277162

Theory of neural perturbome

To unravel the functional properties of the brain, we need to untangle how neurons interact with each other and coordinate in large-scale recurrent networks. One way to address this question is to measure the functional influence of individual neurons on each other by perturbing them in vivo. Application of such single-neuron perturbations in mouse visual cortex has recently revealed feature-specific suppression between excitatory neurons, despite the presence of highly specific excitatory connectivity, which was deemed to underlie feature-specific amplification. Here, we studied which connectivity profiles are consistent with these seemingly contradictory observations, by modeling the effect of single-neuron perturbations in large-scale neuronal networks. Our numerical simulations and mathematical analysis revealed that, contrary to the prima facie assumption, neither inhibition dominance nor broad inhibition alone were sufficient to explain the experimental findings; instead, strong and functionally specific excitatory–inhibitory connectivity was necessary, consistent with recent findings in the primary visual cortex of rodents. Such networks had a higher capacity to encode and decode natural images, and this was accompanied by the emergence of response gain nonlinearities at the population level. Our study provides a general computational framework to investigate how single-neuron perturbations are linked to cortical connectivity and sensory coding and paves the road to map the perturbome of n euronal networks in future studies.


13th January 2023

Kyle Wedgewood 

1:00 PM BIOMED BLDG C44 and online: https://bristol-ac-uk.zoom.us/j/94138286231?pwd=MlRURE1SWjR6OTZCR1Fnak9QbGxhUT09

Meeting ID: 941 3828 6231 Passcode: 277162

Closed-Loop Interrogation of the Dynamics of Neuroendocrine Cells

This talk will discuss how mathematical modelling can be embedded within experiment protocols to study electrical behaviour in neurons and neuroendocrine cells in which delays play an important role. We discuss three examples, the first of which explores the capability of a neuron that is synaptically coupled to itself, to store and repeat patterns of precisely timed spikes, which we regard as single cell ‘memories’. Drawing on analogies from semiconductor lasers, we append a delayed self-coupling term to the oft studied Morris-Lecar model of neuronal excitability and use bifurcation analysis to predict the number and type of memories the neuron can store. These results highlight the delay period as an important period parameter controlling the storage capacity of the cell. We then use the dynamic clamp protocol to introduce self-coupling to a mammalian cell and confirm the existence of the spiking patterns predicted by the model analysis. The second example covers preliminary work of investigating the origin of pulsatile secretion in corticotrophs in the pituitary gland. Such pulsatility has previously been conjectured to be strongly coupled to the delay period between secretion from the corticotrophs and feedback from the adrenal glands. Here, we combine Ca2+ imaging, mathematical modelling and dynamic perfusion to explore how delays influence behaviour of this combined system. The final example will explore how techniques combining control theory and bifurcation analysis with dynamic clamp can be used to probe single cell electrical excitability.


6th January 2023

Anne Skeldon (Professor of Mathematics)

1:00 PM BIOMED BLDG C44 and online: https://bristol-ac-uk.zoom.us/j/91296637128?pwd=U1lkcEVobkZQVlUrTGIwUWNDUkNJdz09

Meeting ID: 912 9663 7128 Passcode: 017491

Sleep regulation: physiological mechanisms and the design of light interventions for improved sleep

In this talk I will give a brief overview of the fundamental mechanisms that are believed to underpin sleep-wake regulation (sleep homeostasis, circadian rhythmicity, light) and high-level phenomenological and neuronal models that capture these mechanisms. Using data collected in 20 people living with schizophrenia and 21 healthy (unemployed) controls, I will then discuss how data and models can be used to uncover the relative contributions of physiological and environmental factors driving different sleep phenotypes. The talk will highlight how personalised models could be used to co-design light interventions with patients.


 

Winter&Spring 2021/2022


10th June 2022

Carsen Stringer (Group Leader, HHMI Janelia Research Campus)

1:00 PM Onlinehttps://bristol-ac-uk.zoom.us/j/92144801092?pwd=YVNKeGFFRW1kb09iQnU0MkRIRzV5UT09

Meeting ID: 921 4480 1092  Passcode: 225286

Making sense of large-scale neural and behavioral data 

Large-scale neural recordings contain high-dimensional structure that cannot be easily captured by existing data visualization methods. We therefore developed an embedding algorithm called Rastermap, which captures complex temporal and highly nonlinear relationships between neurons, and provides useful visualizations by assigning each neuron to a location in the embedding space. We applied Rastermap to a variety of datasets, including spontaneous neural activity, neural activity during a virtual reality task, widefield neural imaging data from a 2AFC task, and artificial neural activity from an agent playing atari games. We found within these datasets unique subpopulations of neurons encoding abstract elements of decision-making, the environment and behavioral states. To interrogate behavioral representations in the mouse brain, we developed a fast deep-learning model for tracking 13 distinct points on the mouse face recorded from arbitrary camera angles. The model was just as accurate as state-of-the-art pose estimation tools while being several times faster, making it a powerful tool for closed-loop behavioral experiments. Next, we aligned facial key points across mice in order to train a universal model to predict neural activity from behavior. The universal mouse model could predict neural activity as well as a model fit to a single mouse, showing that neural representations of behaviors are conserved across mice. The latent states extracted from the universal model contained interpretable mouse behaviors.

6th May 2022

Paul Cisek (Professor, University of Montréal)

2PM BIOMED BLDG C44 and onlinehttps://bristol-ac-uk.zoom.us/j/92144801092?pwd=YVNKeGFFRW1kb09iQnU0MkRIRzV5UT09

Meeting ID: 921 4480 1092  Passcode: 225286

Neural dynamics of embodied decisions

Decision-making is not a single thing, but rather a variety of processes that have been gradually elaborated and refined over the brain’s long evolutionary history. In the first part of my talk, I will briefly describe that history, emphasizing the kinds of decision processes that dominated animal behavior for hundreds of millions of years. In particular, I will focus on theories of how animals make real-time decisions between action opportunities available in a dynamically changing world. I will then summarize experimental studies suggesting that these kinds of action decisions unfold in parallel across cortical and subcortical circuits, laying the foundations for more advanced aspects of primate behavior.


29th April 2022

Karan Grewal (Research Scientist, Numenta)

 4:15 PM BIOMED BLDG C44 and onlinehttps://bristol-ac-uk.zoom.us/j/92144801092?pwd=YVNKeGFFRW1kb09iQnU0MkRIRzV5UT09

Meeting ID: 921 4480 1092 Passcode: 225286

Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environment

A key challenge for AI is to build systems that must adapt to changing task contexts and learn continuously. Although standard deep learning systems achieve state of the art results on static benchmarks, they often struggle in dynamic scenarios where the task changes over time, and this phenomenon is known as catastrophic forgetting. In this talk, I will discuss how biophysical properties of dendrites in the brain and local inhibitory systems enable networks to dynamically restrict and route information in a context-specific manner. Specifically, I will highlight the performance of a deep learning architecture that embodies properties of dendrites on two separate benchmarks requiring task-based adaptation: Meta-World (a multi-task reinforcement learning environment where a robotic agent must learn to solve a variety of manipulation tasks simultaneously) and permutedMNIST (a continual learning benchmark in which the model’s prediction task changes throughout training). Analysis on both benchmarks demonstrates the emergence of overlapping but distinct and sparse subnetworks, allowing the system to fluidly learn multiple tasks with minimal forgetting. This work sheds light on how biological properties of neurons can inform deep learning systems to address dynamic scenarios that are typically impossible for traditional ANNs to solve.


22nd April 2022

H Freyja Ólafsdóttir (Assistant Professor of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour) 

1PM BIOMED BLDG C44 and onlinehttps://bristol-ac-uk.zoom.us/j/92144801092?pwd=YVNKeGFFRW1kb09iQnU0MkRIRzV5UT09

Meeting ID: 921 4480 1092 Passcode: 225286

Hippocampal-entorhinal circuits for spatial memoryThe hippocampus is important for spatial and episodic memory. Place cells – the principal cell of the hippocampus – represent information about an animal’s spatial location. Yet, during sleep and rest place cells spontaneously recapitulate (‘replay’) past trajectories. Replay has been hypothesised to serve a variety of functions in memory. In my talk I will describe recent work I carried out which showed replay may support a dual function: underpinning both spatial planning as well as the consolidation of new memories. Namely, we found during rest periods place and grid cells, from the deep medial entorhinal cortex (dMEC, the principal cortical output region of the hippocampus), replayed coherently. Importantly, putative dMEC replay lagged place cell replay by ~11ms; suggesting the replay coordination may reflect consolidation. Moreover, in a separate study we found replay occurring just before movement to or upon arrival at a reward site preferentially depicted locations and trajectories consistent with the animals’ current task demands; perhaps indicative of spatial planning. However, we also found replay could dynamically ‘switch’ between a planning and consolidation mode, in relation to engagement with task demands, and we found planning-like replay predicted the accuracy of imminent spatial decision. Finally, I will discuss unpublished work showing how the formation of hippocampal-dMEC cell assemblies during encoding periods may underlie hippocampal-dMEC replay coordination and on-going work where we employ an ontogenetic approach to elucidating the neural circuit mechanisms of spatial memory.


8th April 2022

Mark D Humphries (Professor of Computational Neuroscience, University of Nottingham)

1PM BIOMED BLDG C44 and onlinehttps://bristol-ac-uk.zoom.us/j/92144801092?pwd=YVNKeGFFRW1kb09iQnU0MkRIRzV5UT09

Meeting ID: 921 4480 1092 Passcode: 225286

Simultaneous and separable latent encoding of arm movement direction and kinematics in motor cortexLittle is known about if and how multiple features of movement are simultaneously encoded by population activity in motor cortex. Using neural activity from dorsal premotor cortex (PMd) and motor cortex (M1) as monkeys performed a sequential arm movement task, in this talk I will show that the direction and kinematics of arm movements are simultaneously but separably encoded in the low-dimensional trajectories of population activity. Trajectories of population activity encoded the direction of arm movement, with the distances between neural trajectories proportional to the difference in angle between the directions they encoded. By contrast, different durations of arm movements in the same direction were encoded by the how long the neural trajectory took to traverse. A recurrent neural network (RNN) model of our results suggested the direction and duration could be independently controlled by respectively rotating the inputs to motor cortex and scaling the effective neuron time constant within motor cortex. Our results propose a mechanism for the simultaneous yet independent control of multiple arm movement features by motor cortex.


1st April 2022

Youssef Mohamed (Doctoral Student, KTH Royal Institute of Technology)

1PM BIOMED BLDG C44 and onlinehttps://bristol-ac-uk.zoom.us/j/92144801092?pwd=YVNKeGFFRW1kb09iQnU0MkRIRzV5UT09

Meeting ID: 921 4480 1092 Passcode: 225286

Human-Robot Interaction and Social Robotics

How robots perceive humans is as important as how humans perceive robots. Hence, developing systems that are able to understand and rationalize our actions, is the first step to creating more socially aware robots. Nonetheless, doing so can be “complicated” as human actions can sometimes be illogical but patterns can be detected and inferences can be made using AI.​ In this seminar we will be discussing the state-of-the-art approaches of detecting human intentions and internal states and how those systems are able to do so. Furthermore, we will also bring up some of the moral and societal concerns that those systems invoke. 


25th March 2022

Dr Alexandra Keinath (Postdoctoral Fellow, McGill University)

2PM BIOMED BLDG C44 and onlinehttps://bristol-ac-uk.zoom.us/j/92144801092?pwd=YVNKeGFFRW1kb09iQnU0MkRIRzV5UT09

Meeting ID: 921 4480 1092 Passcode: 225286

Dynamic maps of a dynamic world: how hippocampal and entorhinal representations cope with an ever-changing experience of an unstable world 

Extensive research has revealed that the hippocampus and entorhinal cortex maintain a rich representation of space through the coordinated activity of place cells, grid cells, and other spatial cell types. Frequently described as a ‘cognitive map’ or a ‘hippocampal map’, these maps are thought to support episodic memory through their instantiation and retrieval. Though often a useful and intuitive metaphor, a map typically evokes a static representation of the external world. However, the world itself as well as our experience of it are intrinsically dynamic. Here I will present three projects where we address how hippocampal and entorhinal representations adapt to, incorporate, and overcome these dynamics. In the first project, I will describe how boundaries dynamically anchor entorhinal grid cells and human spatial memory alike when the shape of a familiar environment is changed. In the second project, I will describe how the hippocampus maintains a representation of the recent past even in the absence of disambiguating sensory and explicit task demands, a representation which causally depends on intrinsic hippocampal circuitry. In the third project, I will describe how the hippocampus preserves a stable representation of context despite ongoing representational changes across a timescale of weeks. Together, these projects yield new insight into the dynamic and adaptive nature of our hippocampal and entorhinal representations and set the stage for exciting future work building on these techniques and paradigms.


18th March 2022

Dr Petra Fischer (Lecturer, University of Bristol)

1PM BIOMED BLDG C44

Coordination of neural activity during action choice

Life is a series of action choices. One can ignore external events or react to them. I will discuss two projects that show cortical gamma synchronization during rapid evaluation of external events and adjustment of an ongoing action. Our behavioural task is simple, but can be used as a powerful tool to understand how neural activity is coordinated within cortico-subcortical circuits to produce fundamental components of flexible behaviour. I will discuss the implications of brief periods of neural synchronization for coordination of neural activity and ideas for future projects.


11th March 2022

Darinka Trübutschek (Research associate, the Max Planck Institute for Empirical Aesthetics)

1PM Meeting ID: 921 4480 1092 Passcode: 225286

https://bristol-ac-uk.zoom.us/j/92144801092?pwd=YVNKeGFFRW1kb09iQnU0MkRIRzV5UT09

Decoding our thoughts: Tracking the contents of non-conscious working memoryOur daily and intellectual lives depend on our ability to hold information in mind for immediate use. Despite a rich research history, cracking the neuro-cognitive code of such working memory remains one of the most important challenges for neuroscience to date. According to the pre-dominant theoretical stance, maintaining information in working memory requires conscious, effortful activity sustained over the entire delay period.However, this might reflect only the tip-of-the-iceberg. Recent studies have shed doubt on these long-standing assumptions showing either that (1) even subliminal stimuli may be stored for several seconds in non-conscious working memory and that (2) memories might also be retained in the absence of accompanying neural activity in activity-silent working memory by means of slowly decaying synaptic changes.During this talk, I will present evidence from a series of behavioral and magnetoencephalography experiments that help to reconcile these diverging frameworks. Specifically, while the short-term maintenance of information may be entirely decoupled from both conscious experience and persistent neural activity, the hallmark feature of working memory – the ability to manipulate information – requires both. As such, these findings provide strong evidence against a genuinely non-conscious ‘working’ memory, and instead point towards the existence of an activity-silent short-term memory. Moreover, they offer a theoretical framework of how both active and/or conscious and activity-silent and/or non-conscious brain processes may interact to support working memory.


25th February 2022

Dr Charlotte Horne (Senior Research Associate, University of Bristol)

1PM BIOMED BLDG C44

The role of cognitive control in treatment-resistant schizophrenia and the relationship with sleep loss

In part 1 of the talk, I will present recent work completed during my post-doc at King’s College London. Approximately one third of patients with schizophrenia fail to respond to antipsychotic medication – termed ‘treatment resistance’ – and the underlying mechanisms remain unclear. Here, I present data suggesting poor cognitive control may be a mechanism underlying treatment resistant schizophrenia. We investigated effective connectivity within a network of interacting regions responsible for cognitive control, sensory and reward processing using Dynamic Causal Modelling (DCM) whilst patients performed an fMRI reward learning task. Treatment-resistant patients had reduced top-down connectivity compared to treatment-responsive patients which may be underpinned by a glutamatergic abnormality (as measured using MR spectroscopy). Our findings suggest that treatment resistance may represent a subtype of schizophrenia with a distinct underlying mechanism that is not targeted by current medication.

In part 2 of the talk, I will (informally) present some of my current interests around the causal role of sleep loss on the development of mental illness (e.g. depressive and psychotic symptoms). In particular the effects of sleep/circadian disruption on cognitive control and what makes some people more vulnerable to these effects. I hope to create more of an informal discussion around these ideas as I am really keen to hear more about similar research taking place in Bristol and to collaborate.


18th February 2022

Karan Grewal (Research Scientist, Numenta)

4PM Meeting ID: 921 4480 1092 Passcode: 225286

https://bristol-ac-uk.zoom.us/j/92144801092?pwd=YVNKeGFFRW1kb09iQnU0MkRIRzV5UT09

Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environment

A key challenge for AI is to build systems that must adapt to changing task contexts and learn continuously. Although standard deep learning systems achieve state of the art results on static benchmarks, they often struggle in dynamic scenarios where the task changes over time, and this phenomenon is known as catastrophic forgetting. In this talk, I will discuss how biophysical properties of dendrites in the brain and local inhibitory systems enable networks to dynamically restrict and route information in a context-specific manner. Specifically, I will highlight the performance of a deep learning architecture that embodies properties of dendrites on two separate benchmarks requiring task-based adaptation: Meta-World (a multi-task reinforcement learning environment where a robotic agent must learn to solve a variety of manipulation tasks simultaneously) and permutedMNIST (a continual learning benchmark in which the model’s prediction task changes throughout training). Analysis on both benchmarks demonstrates the emergence of overlapping but distinct and sparse subnetworks, allowing the system to fluidly learn multiple tasks with minimal forgetting. This work sheds light on how biological properties of neurons can inform deep learning systems to address dynamic scenarios that are typically impossible for traditional ANNs to solve.


11th February 2022

Dr Lillian J Brady (Postdoctoral Research Fellow, Vanderbilt University)

3PM Meeting ID: 937 0313 1980 Passcode: 422725

https://bristol-ac-uk.zoom.us/j/93703131980?pwd=MFBNbDU4bFcxeTRRL3NxdlU2SVZ4UT09

Sex differences in cholinergic regulation of dopamine release through nicotinic receptors mediate sexually dimorphic behavior

Dopamine release dynamics in the mesolimbic dopamine pathway, which connects the ventral tegmental area (VTA) to the nucleus accumbens (NAc), are an essential component of the process that controls motivation and reward-seeking behavior in Substance Use Disorder. In the NAc specifically, tonic and phasic dopamine release is known to play a critical role in converting information about environmental reward-predictive cues to anticipated rewarding outcomes and is heavily modulated by the activity of cholinergic (ChAT) interneurons signaling through nicotinic acetylcholine receptors (nAChRs). Using operant conditioning and fast-scan cyclic voltammetry with pharmacology we defined sex differences in ChAT regulation of dopamine release underlying sex-specific motivational strategies for non-drug rewards. We find critical differences in cholinergic regulation of dopamine terminals that underlies distinct differences in behavioral strategies between males and females.


26th November 2021

Dr Emma Cahill (Lecturer, University of Bristol)

1PM BIOMED BLDG C44

BLA, BLA, BLA… what about the BNST!?: Role of the amygdala and extended amygdala circuits for the detection of threat cues in rats

Learning which threats to avoid is key for survival. We can model how a rodent learns about threats using pavlovian associative conditioning tasks, where the rodent learns that a specific conditioned stimulus was predictive of an aversive event. The imminence of a threat is assessed by either the threat cue physical proximity as close/distant, or by a psychological prediction of its likelihood as a recognisable predictable/unpredictable event. There is a wealth of data regarding the neurochemical mechanisms that take place within the amygdala nuclei, basolateral (BLA) and Central (CeN) for this associative learning to occur. However, the so-called extended amygdala, the bed nuclei of the stria terminalis (BNST), seem to play a role specifically in the detection of ambiguous threat cues, more akin to anxiety-like responses. In this talk, I will present some recent data on the activation of the amygdala and extended amygdala after modifications of the threat cue. In one set of experiments, the predictability of the CS-US relationship was modified by changing the reinforcement contingency during training. In a second set of experiments, the salience of the CS was modified at test to reduce its detectability. There remain many open questions regarding under what conditions the specific subnuclei of the BNST become recruited and how they regulate interconnected amygdala circuits to influence responding to threats.


19th November 2021

Professor Anne Collins (Assistant Professor, University of California, Berkeley) 

3PM Meeting ID: 937 0313 1980 Passcode: 422725

https://bristol-ac-uk.zoom.us/j/93703131980?pwd=MFBNbDU4bFcxeTRRL3NxdlU2SVZ4UT09

Executive contributions to reinforcement learning computations in humans

The study of the neural processes that support reinforcement learning has been greatly successful. It has characterized a simple brain network (including cortico-basal ganglia loops and dopaminergic signalling) that enables animals to learn to make valuable choices, using valanced outcomes. However, increasing evidence shows that the story is more complex in humans, where additional processes also contribute importantly to learning. In this talk, I will show three examples of how prefrontal-dependent executive processes are essential to reinforcement learning in humans, operating both in parallel to the brain’s reinforcement learning network, as well as feeding this network information.


12th November 2021

Dr Abhishek Banerjee (Senior Lecturer, University of Newcastle)

1PM BIOMED BLDG C42

Prefrontal reprogramming of sensory cortex: Cellular and computational principles 

Animals adapt their behaviour in response to variable changes in reward reinforcement. Value-based decision-making involves multiple cognitive maps across distributed brain areas. It is less clear which brain regions are essential and how changes in neural responses flexibly re-maps guiding adaptive behaviour. In this talk, I will highlight behavioural-neural interactions between orbitofrontal and somatosensory circuits that implement flexible decision-making. I will present further evidence of how some of these functions are disrupted in autism spectrum disorders, arguing for a new conceptual framework based on computational psychiatry to understand cognitive pathophysiology in neurological disorders.


22nd October 2021

Dr Michele Veldsman (Research Scientist, University of Oxford)

1PM GEOG BLDG G.11N SR1

MRI Markers of vascular cognitive impairment

Cerebrovascular risk factors increase the likelihood of dementia. High cerebrovascular burden leads to vascular dementia, accounting for around 20% of dementia cases. Less well appreciated, is that up to 70% of patients with Alzheimer’s disease (AD) also have cerebrovascular disease pathology at post-mortem. The impact of mixed pathologies is likely greatly underestimated. Studies of neurodegenerative dementias rarely control cerebrovascular burden, beyond age and obvious magnetic resonance imaging (MRI) markers like white matter hyperintensities (WMHs). In this talk, I will show work investigating MRI markers of cerebrovascular burden in healthy ageing in 22 000 people from the UK Biobank. I will demonstrate new methods for the estimation of the spatial distribution of cerebrovascular risk-related WMHs and their impact on cognition. I will also present work looking at the importance of microstructural integrity of normal appearing white matter and integrity of grey matter in distributed brain networks for the preservation of cognitive function in healthy ageing and after ischaemic stroke. Together, I will build up a picture of the important MRI markers of cerebrovascular burden that may act as transdiagnostic markers of cognitive impairment.

Autumn 2021

All Neural Dynamics Forum talks during Autumn 2021 will take place online through Zoom unless specified otherwise, details as below.

​Meeting ID: 932 7630 6396
Password: 815933
https://zoom.us/s/93276306396


1PM September 24th – Dr Mike Ambler (Clinical Lecturer, University of Bristol)

GEOG BLDG G.11N SR1

Torpor TRAP: from mice to Mars

Torpor is a naturally occurring hypothermic, hypometabolic state employed by a wide range of species in response to a paucity of food availability. It can be brief as seen in daily heterotherms, or prolonged as seen in seasonal hibernators. Understanding the neural control of torpor might allow synthetic torpor states to be induced in species for which it is not an extant behaviour, which might have useful clinical or space travel applications. I will discuss the approach to finding the neural switch for a behaviour about which little is known. I will present data from the mouse (a daily heterotherm) in which the use of targeted recombination in active populations (TRAP) allows dissection of the role of specific hypothalamic nuclei in the induction of torpor. From this work, the preoptic area of the hypothalamus (POA) emerges as a central region capable of triggering torpor in the mouse. Finally, I will show that in the rat, which does not naturally enter torpor, chemogenetic activation of the POA induces a state with remarkable similarities to torpor.


2PM September 17th – Xiaosi Gu (Icahn School of Medicine at Mount Sinai)

The Social Brain: From Models To Mental Health

Given the complex and dynamic nature of our social relationships, the human brain needs to quickly learn and adapt to new social situations. The breakdown of any of these computations could lead to social deficits, as observed in many psychiatric disorders. In this talk, I will present our recent neurocomputational and intracranial work that attempts to model both 1) how humans dynamically adapt beliefs about other people and 2) how individuals can exert influence over social others through model-based forward thinking. Lastly, I will present our findings of how impaired social computations might manifest in different disorders such as addiction, delusion, and autism. Taken together, these findings reveal the dynamic and proactive nature of human interactions as well as the clinical significance of these high-order social processes.

Spring 2021

All Neural Dynamics Forum talks during Spring 2021 will take place online through Zoom unless specified otherwise, details as below.

​Meeting ID: 932 7630 6396
Password: 815933
https://zoom.us/s/93276306396


1PM June 25th – Professor Steve Coombes (University of Nottingham)
* Note: Forum will take place in person outdoors at Royal Fort Gardens 

Next generation neural field modelling

Neural mass models have been actively used since the 1970s to model the coarse-grained activity of large populations of neurons and synapses.  They have proven especially fruitful for understanding brain rhythms.  However, although motivated by neurobiological considerations they are phenomenological in nature, and cannot hope to recreate some of the rich repertoire of responses seen in real neuronal tissue.  In this talk I will discuss a simple spiking neuron network model that has recently been shown to admit to an exact mean-field description for synaptic interactions.  This has many of the features of a neural mass model coupled to an additional dynamical equation that describes the evolution of population synchrony.  I will show that this next generation neural mass model is ideally suited to understanding beta-rebound. This is readily observed in MEG recordings whereby motor action causes a drop in the beta power band attributed to a loss of network synchrony.  Existing neural mass models are unable to capture this phenomenon (event related de-synchrony) since they do not track any notion of network coherence (only firing rate).  I will spend the latter part of my talk discussing patterns and waves in a spatially continuous non-local extension of this model, highlighting its usefulness for large scale cortical modelling.


1PM June 11th – Dr Michael Proulx (Reader in Psychology, University of Bath)

Understanding spatial cognition through virtual and augmented reality

Spatial knowledge is key for most everything we do. The study of spatial cognition is now able to take advantage of advances in computational modelling, Virtual Reality and Augmented Reality with important implications for theory and application. I will explore these issues through a few case studies of our research, including: the use of eye-tracking with interactive virtual environments; visual impairments and pain to explore multisensory experiences of space; and using motion-tracking and augmented reality to assess the presentation of visual information in tactile or auditory displays to the blind or blindfolded. These approaches and immersive technologies hold great potential for advancements in fundamental and translational neuroscience.


1PM June 4th – Professor Peter Ashwin (Professor of Mathematics, University of Exeter)

Excitable Networks in Continuous Time Recurrent Neural Networks

Continuous time recurrent neural networks (CTRNN) are systems of coupled ordinary differential equations that are simple enough to be insightful for describing learning and computation, from both biological and machine learning viewpoints. We describe a direct constructive method of realising finite state input-dependent computations on an arbitrary directed graph. The constructed system has an excitable network attractor whose dynamics. The resulting CTRNN has intermittent dynamics: trajectories spend long periods of time close to steady-state, with rapid transitions between states. Depending on parameters, transitions between states can either be excitable (inputs or noise needs to exceed a threshold to induce the transition), or spontaneous (transitions occur without input or noise). In the excitable case, we show the threshold for excitability can be made arbitrarily sensitive.


1PM May 28th – Professor Andre Fenton (Professor of Neural Science, New York University)

Memory, learning to learn, and control of cognitive representations

Biological neural networks can represent information in the collective action potential discharge of neurons, and store that information amongst the synaptic connections between the neurons that both comprise the network and govern its function. The strength and organization of synaptic connections adjust during learning, but many cognitive neural systems are multifunctional, making it unclear how continuous activity alternates between the transient and discrete cognitive functions like encoding current information and recollecting past information, without changing the connections amongst the neurons. This lecture will first summarize our investigations of the molecular and biochemical mechanisms that change synaptic function to persistently store spatial memory in the rodent hippocampus. I will then report on how entorhinal cortex-hippocampus circuit function changes during cognitive training that creates memory, as well as learning to learn in mice. I will then describe how the hippocampus system operates like a competitive winner-take-all network, that, based on the dominance of its current inputs, self organizes into either the encoding or recollection information processing modes. We find no evidence that distinct cells are dedicated to those two distinct functions, rather activation of the hippocampus information processing mode is controlled by a subset of dentate spike events within the network of learning-modified, entorhinal-hippocampus excitatory and inhibitory synapses.


1PM May 14th – Dr Sarah Morgan (Senior Research Associate, University of Cambridge Brain Mapping Unit)

What can brain MRI tell us about schizophrenia?

Schizophrenia is an extremely debilitating disease, which affects approximately 1% of the population during their lifetime. However, to date there are no known biomarkers for schizophrenia, the biological mechanisms underpinning the disease are unclear, and there has been correspondingly little progress on new therapeutics. In this talk, I will discuss how MRI brain imaging can begin to address these challenges, using data from the Psyscan project (http://psyscan.eu/). In the first part of the talk, I will show how morphometric similarity mapping and imaging transcriptomics can shed fresh light on structural brain differences in schizophrenia (Morgan et al, PNAS 2019). In the second part, I will examine the extent to which MRI data can be used to distinguish patients with schizophrenia from healthy volunteers. We find that fMRI can do this with high accuracy, based on a reproducible pattern of cortical features associated with neurodevelopment (Morgan*, Young* et al, BP:CNNI 2020). Overall, we begin to see how MRI can give us a more integrative understanding of schizophrenia, which might inform future treatments.


1PM May 7th – Professor Daniel Wolpert (Professor of Neuroscience, Zuckerman Mind Brain Behaviour Institute, Columbia University)

Computational principles underlying the learning of sensorimotor repertoires

Humans spend a lifetime learning, storing and refining a repertoire of motor memories appropriate for the multitude of tasks we perform. However, it is unknown what principle underlies the way our continuous stream of sensorimotor experience is segmented into separate memories and how we adapt and use this growing repertoire. I will review our work on how humans learn to make skilled movements focussing on the role of context in activating motor memories and how statistical learning can lead to multimodal object representations. I will then present a principled theory of motor learning based on the key insight that memory creation, updating, and expression are all controlled by a single computation – contextual inference. Unlike dominant theories of single-context learning, our repertoire-learning model accounts for key features of motor learning that had no unified explanation and predicts novel phenomena, which we confirm experimentally. These results suggest that contextual inference is the key principle underlying how a diverse set of experiences is reflected in motor behavior.


2PM April 30th – Tali Sharot (UCL)

How People Decide What They Want to Know: Information-Seeking and the Human Brain

The ability to use information to adaptively guide behavior is central to intelligence. A vital research challenge is to establish how people decide what they want to know. In this talk I will present our recent research characterizing three key motives of information seeking. We find that participants automatically assess (i) how useful information is in directing action, (ii) how it will make them feel, and (iii) how it will influence their ability to predict and understand the world around them. They then integrate these assessments into a calculation of the value of information that guides information-seeking or its avoidance. These diverse influences are captured by separate brain regions along the dopamine reward pathway and are differentially modulated by pharmacological manipulation of dopamine function. The findings yield predictions about how information-seeking behavior will alter in disorders in which the reward system malfunctions. We test these predictions using a linguistic analysis of participants’ web searches ‘in the wild’ to quantify their motive for seeking information and relate those to reported psychiatric symptoms. Finally, using controlled behavioral experiments we show that the three motives for seeking information appear early in developmental following roughly linear trajectories.


2PM April 16th – Bard Ermentrout (University of Pittsburgh)

A Robust Neural Integrator Based on the Interactions of Three Time Scales

Neural integrators are circuits that are able to code analogue information such as spatial location or amplitude. Storing amplitude requires the network to have a large number of attractors. In classic models with recurrent excitation, such networks require very careful tuning to behave as integrators and are not robust to small mistuning of the recurrent weights.   In this talk, I introduce a circuit with recurrent connectivity that is subjected to a slow subthreshold oscillation (such as the theta rhythm in the hippocampus). I show that such a network can robustly maintain many discrete attracting states. Furthermore, the firing rates of the neurons in these attracting states are much closer to those seen in recordings of animals.  I show the mechanism for this can be explained by the instability regions of the Mathieu equation.  I then extend the model in various ways and, for example, show that in a spatially distributed network, it is possible to code location and amplitude simultaneously. I show that the resulting mean field equations are equivalent to a certain discontinuous differential equation.


1PM April 9th – Paul Anastasiades (University of Bristol)

Circuit organisation of the rodent prefrontal thalamo-cortical system

Interactions between the thalamus and prefrontal cortex (PFC) play a critical role in cognitive function and arousal and are disrupted in neuropsychiatric disorders. The PFC is reciprocally connected with ventromedial (VM) and mediodorsal (MD) thalamus, both higher-order nuclei with distinct properties to the classically studied sensory relay nuclei. To understand the properties of the circuits linking PFC and thalamus we use anatomical tracing, electrophysiology, optogenetics, and 2‐photon Ca2+ imaging, determining how VM and MD target specific cell types and subcellular compartments of mouse PFC. Focusing on cortical layer 1, we find thalamic nuclei target distinct sublayers, with VM engaging NDNF+ cells in L1a, and MD driving VIP+ cells in L1b. These separate populations of L1 interneurons participate in different inhibitory networks in superficial layers by targeting either PV+ or SOM+ interneurons. NDNF+ cells mediate a unique form of thalamus-evoked inhibition at PT cells, selectively blocking VM-evoked dendritic Ca2+ spikes. Together, our findings reveal how two thalamic nuclei differentially communicate with the PFC through distinct L1 micro‐circuits and how inhibition is critical for controlling PFC output back to thalamus.