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.

https://github.com/SpikeInterface

https://www.biorxiv.org/content/10.1101/796599v1

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

Abstract:

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.


 

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