Abstract: Object processing pathways, such as the primate ventral stream, face the challenge of building representations that are increasingly selective for the identity of visual objects while becoming gradually more tolerant (or invariant) to changes in their appearance (resulting, e.g., from translation, rotation, scaling). A key question in vision sciences is to understand how such transformation-tolerant object representations are formed. A leading hypothesis is that invariance is learned in an unsupervised way by exploiting the spatiotemporal continuity of visual experience, i.e., the natural tendency of different object views to occur nearby in time. This would allow visual neurons to become tuned to the content of dynamic visual scenes that varies more slowly over time (i.e., object identity) while discarding other faster-varying, lower-level visual attributes (e.g., local features). In this seminar, I will present both causal and functional evidence, based on recordings from rat visual cortex, supporting this hypothesis.

In a first study, we focused on the properties of two classes of neurons in primary visual cortex (V1): simple cells, which encode edge orientation in a position-sensitive manner and complex cells, which also encode orientation but in a position-invariant way. We found that degrading the temporal continuity of visual experience during early postnatal life leads to a sizable reduction of the number of complex cells and to an impairment of their functional properties while fully sparing the development of simple cells. This causally implicates adaptation to the temporal structure of the visual input in the development of transformation tolerance but not of shape tuning, thus tightly constraining computational models of unsupervised cortical learning.

In a second study, we tested the hypothesis that the temporal persistence (i.e., slowness) of neuronal responses to temporally structured dynamic stimuli increases along the visual cortical hierarchy. By probing the rat analog of the ventral stream with movies, we uncovered a hierarchy of temporal scales, with deeper areas encoding visual information more persistently. Furthermore, the impact of intrinsic dynamics on the stability of stimulus representations grew gradually along the hierarchy. This suggests that building representations that become progressively slower along the cortical processing hierarchy is indeed an objective that could guide the learning of invariance.

About the speaker: Davide Zoccolan is a professor of neurophysiology at the Scuola Internazionale Superiore di Studi Avanzati (SISSA) of Trieste. He received his M.S. in physics at the University of Turin in 1997 and his Ph.D. in biophysics at SISSA in 2002. He worked as a postdoctoral associate at the Massachusetts Institute of Technology with James DiCarlo and Tomaso Poggio and as a postdoctoral fellow at Harvard University with David Cox. In 2009, he established the SISSA Visual Neuroscience Lab, where he studies the neuronal basis of high-level visual functions using a combination of psychophysics and electrophysiology in rats, as well as computational modeling.

Abstract: To make contextually appropriate predictions in a stochastic environment, the brain needs to take uncertainty into account. Prediction error neurons have been identified in layer 2/3 of diverse brain areas. How uncertainty modulates prediction error activity and hence learning is, however, unclear. Here, we use a normative approach to derive how prediction errors should be modulated by uncertainty and postulate that such uncertainty-weighted prediction errors (UPE) are represented by layer 2/3 pyramidal neurons. We further hypothesise that the layer 2/3 circuit calculates the UPE through the subtractive and divisive inhibition by different inhibitory cell types. We ascribe different roles to somatostatin-positive (SST), and parvalbumin-positive (PV) interneurons. By implementing the calculation of UPEs in a microcircuit model, we show that different cell types in cortical circuits can compute means, variances and UPEs with local activity-dependent plasticity rules. Finally, we show that the resulting UPEs enable adaptive learning rates.

About the speaker: Katharina Wilmes is an advanced postdoc in Computational Neuroscience Group at the University of Bern. She studied Cognitive Sciences and then did her PhD in Computational Neuroscience with Susanne Schreiber and Henning Sprekeler at the Institute for Theoretical Biology, Humboldt University of Berlin and the Bernstein Center for Computational Neuroscience. After a postdoctoral stay at the Imperial College London in the group of Claudia Clopath, she moved to Switzerland to work between theory and experiment at the University of Bern.

Abstract: Bayesian Active Learning (BAL) is an efficient framework for learning the parameters of a model, in which input stimuli are selected to maximize the mutual information between the observations and the unknown parameters. However, the applicability of BAL to experiments is limited as it requires performing high-dimensional integrations and optimizations in real time: current methods are either too time consuming, or only applicable to specific models. Here, we propose an Efficient Sampling-Based Bayesian Active Learning (ESB-BAL) framework, which is efficient enough to be used in real-time biological experiments. We apply our method to the problem of estimating the parameters of a chemical synapse from the postsynaptic responses to evoked presynaptic action potentials. Using synthetic data and synaptic whole-cell patch-clamp recordings, we show that our method can improve the precision of model-based inferences, thereby paving the way towards more systematic and efficient experimental designs in physiology.

About the speaker: Jean-Pascal Pfister is an associate professor at the Department of Physiology (University of Bern) and at the Institute of Neuroinformatics (University of Zurich / ETH). He is heading the theoretical neuroscience group and aims at discovering fundamental computational principles that govern brain dynamics. During his PhD at EPFL, he developed several models of spike-timing dependent plasticity (including the “triplet model”). During his post-doc in Cambridge (UK) and his sabbatical in Harvard he developed statistical models of synaptic dynamics, neuronal dynamics and neural network dynamics.

Abstract: So-called spontaneous activity is a central hallmark of most nervous systems. Such non-causal firing is contrary to the tenet of spikes as a means of communication, and its purpose remains unclear. We propose that non-input driven firing can serve as a release valve to protect neurons from the toxic conditions arising in mitochondria from lower-than-baseline energy consumption. We built a set of models that incorporate homeostatic control of metabolic products--ATP, ADP, and reactive oxygen species, among others--by changes in firing. Our theory accounts for key features of neuronal activity observed in many studies that range from ion channels function all the way to resting state dynamics. We propose an integrated, crucial role for metabolic spiking that links metabolic homeostasis and neuronal function, and makes testable predictions. Finally, we link the hallmark symptom of Parkinson's Disease--the absence of dopamine in the striatum--with failure to initiate metabolic spikes.

About the speaker: Tim Vogels is a Professor of Theoretical Neuroscience and research leader at the Institute of Science and Technology (IST) Austria. He studied Physics at the Technical University of Berlin and received a PhD in neuroscience from Brandeis University in 2007. After a postdoctoral stay at Columbia University and Ecole Polytechnique Federale de Lausanne (EPFL) he arrived in Oxford in 2013, where he led a research group in Theoretical and Computational Neuroscience at the Centre for Neural Circuits and Behaviour (CNCB), part of the Department of Physiology, Anatomy and Genetics (DPAG), University of Oxford. Together with Rafal Bogacz, Prof Vogels co-organized the NeuroTheory initiative.

Abstract: The cerebellum has a distinctive circuit architecture comprising the majority of neurons in the brain. Marr-Albus theory and more recent extensions demonstrate the utility of this architecture for particular types of learning tasks related to the separation of input patterns. However, it is unclear how the circuit architecture facilitates known functional roles of the cerebellum. In particular, the cerebellum is critically involved in refining motor plans even during ongoing execution of the associated movement. Why would a cerebellar-like circuit architecture be effective at this type of ‘online’ learning problem? We build a mathematical theory, reinforced with computer simulations, that captures some of the particular difficulties associated with online learning tasks. For instance, synaptic plasticity responsible for learning during a movement only has access to a narrow time window of recent movement errors, whereas it ideally depends upon the entire trajectory of errors, from the movement’s start to its finish. The theory then demonstrates how the distinctive input expansion in the cerebellum, where mossy fibre signals are recorded in a much larger number of granule cells, mitigates the impact of such difficulties. As such, the energy cost of this large, seemingly redundantly connected circuit might be an inevitable cost of precise, fast, motor learning.

Abstract: Tremendous strides have been made in determining genes that substantially increase risk for autism spectrum disorders (ASD). However, we still struggle to understand how specific genes lead to variations in behavior, let alone to the complex changes seen in ASD. A major impasse for our understanding has been inconsistent and subtle behavioral phenotypes in a variety of rodent models of ASD. I will present a different approach for behavioral phenotyping. Using a high-throughput and automated behavioral system, we have developed a novel data-driven method to determine differences in the way groups of animals learn. Instead of applying analyses based off strong assumptions about the causes of behavior, we take a more agnostic approach, allowing the data to inform how groups of animals differ. Applying this method to Scn2a heterozygous rats we find multiple consistent differences in the way they learn compared to wild-type littermates. Scn2a expresses a sodium channel, and it is a high-risk ASD gene. The richer behavioral phenotyping provides far better substrate to determine how a specific gene leads to neuropsychiatric disease.

About the speaker: David Kastner is an Instructor and a Physician Scientist Scholar Program Fellow in the Department of Psychiatry and Behavioral Sciences at the University of California, San Francisco. He earned his MD-PhD from Stanford University, where he studied neural computations performed by the retina. He spent a year in Switzerland at EPFL as a Fulbright Scholar, modeling synaptic level memory consolidation and reconsolidation. He did psychiatry residency and post-doctoral training at UCSF, studying inter-animal variability in spatial learning. His laboratory studies how animals learn, and how that learning changes in the context of neuro-psychiatric disorders, including autism spectrum disorders. The laboratory employs a variety of techniques from high-throughput automated behavior, computational modeling, large-scale electrophysiology, and brain lesioning to determine the computational principles that transform neural activity into behavior.

Abstract: We postulate that three fundamental elements underline a decision making process: perception of time passing, information processing in multiple time scales and reward maximisation. We build a simple reinforcement learning agent upon these principles that we train on a Shadlen-like experimental setup. Our results, similar to the experimental data, demonstrate three emerging signatures. (1) Signal neutrality: insensitivity to the signal coherence in the interval preceding the decision. (2) Scalar property: the mean of the response times varies glaringly for different signal coherences, yet the shape of the distributions stays almost unchanged. (3) Collapsing boundaries: the ``effective" decision-making boundary changes over time in a manner reminiscent of the theoretical optimal. Removing the perception of time or the multiple timescales from the model does not preserve the distinguishing signatures. Our results suggest an alternative explanation for signal neutrality. We propose that it is not part of motor planning. It is part of the decision-making process and emerges from information processing on multiple time scales.

About the speaker: Professor Eleni Vasilaki is visiting professor at the Institute of Neuroinformatics (UZH and ETHZ). She is Chair of Bioinspired Machine Learning and the head of the Machine Learning Group in the Department of Computer Science at the University of Sheffield, UK. Inspired by biology, Prof. Vasilaki and her team design novel machine learning techniques with a focus on reinforcement learning and reservoir computing. She also works closely with material scientists and engineers to design hardware that computes in a brain-like manner. More at https://www.gleichstellung.uzh.ch/de/projekte/gastprofessur_inge_strauch/eleni_vasilaki.html

Abstract: One of the fundamental functions of the brain is to flexibly plan and control movement production at different timescales in order to efficiently shape structured behaviors. I will present research elucidating how these complex computations are performed in the mammalian brain, with an emphasis on autonomous motor control. After briefly mentioning research on the mechanisms underlying high-level planning, I will focus on the efficient interface of these high-level control commands with motor cortical dynamics to drive muscles. I will notably take advantage of the fact that the anatomy of the circuits underlying the latter computation is better known. Specifically, I will show how these architectural constraints lead to a principled understanding of how the combination of hardwired circuits and strategically positioned plastic connections located within loops can create a form of efficient modularity. I will show that this modular architecture can balance two different objectives: first, supporting the flexible recombination of an extensible library of re-usable motor primitives; and second, promoting the efficient use of neural resources by taking advantage of shared connections between modules. I will finally show that these insights are relevant for designing artificial neural networks able to flexibly and robustly compose hierarchical continuous behaviors from a library of motor primitives.

About the speaker: Laureline's research uses a multidisciplinary approach to investigate the network mechanisms of neural computations. Her Ph.D. was co-advised by Angelo Arleo at University Pierre and Marie Curie (France) and Wulfram Gerstner at Ecole Polytechnique Federale de Lausanne (Switzerland), focusing on both model-driven data analysis and theory of neural network dynamics. She is now a senior post-doc at the Center for Theoretical Neuroscience at Columbia University, working with Sean Escola and Larry Abbott on principles of computation in neural networks.

Abstract: Humans have a remarkable ability to remember the images that they have seen, even after seeing thousands, each only once and only for a few seconds. In this talk, I will describe our recent work focused on the neural mechanisms that support visual familiarity memory. In the first part of the talk, I will describe the correlates of the natural variation with which some images are inherently more memorable than others, both the brain as well as deep neural networks trained to categorize objects. In the second part of the talk, I will describe how these results challenge current proposals about how visual familiarity is signaled in the brain, as well as evidence in support of a novel theory about how familiarity is decoded to drive behavior.

Abstract: Biological as well as artificial networks show amazing information processing properties. A popular hypothesis is that neural networks profit from operating close to a continuous phase transition, because at a phase transitions, several computational properties are maximized. We show that maximizing these properties is advantageous for some tasks – but not for others. We then show how homeostatic plasticity enables us to tune networks away or towards a phase transition, and thereby adapt the network to task requirements. Thereby we shed light on the operation of biological neural networks, and inform the design and self-organization of artificial ones. – In a second part of the talk, we address the spread of SARS-CoV-2 in Germany. We quantify how governmental policies and the concurrent behavioral changes led to a transition from exponential growth to decline of novel case numbers. We conclude with discussing potential scenarios of the SARS-CoV-2 dynamics for the months to come.