SISSA Neuroscience department

The Neuroscience Department of the International School for Advanced Studies (SISSA; https://www.sissa.it/research/neuroscience) invites expressions of interest from scientists from various fields of Neuroscience for multiple tenure-track positions with anticipated start in 2025. Ongoing neuroscience research at SISSA includes cognitive neuroscience, computational and theoretical neuroscience, systems neuroscience, molecular and cellular research as well as genomics and genetics. The Department intends to potentiate its activities in these fields and to strengthen cross-field interactions. Expressions of interest from scientists in any of these fields are welcome. The working and teaching language of SISSA is English. This is an equal opportunity career initiative and we encourage applications from qualified women, racial and ethnic minorities, and persons with disabilities. Candidates should have a PhD in a relevant field and a proven record of research achievements. A clear potential to promote and lead research activities, and a specific interest in training and supervising PhD students is essential. Interested colleagues should present an original and innovative plan for their independent future research. We encourage both proposals within existing fields at SISSA as well as novel ideas outside of those or spanning various topics and methodologies of Neuroscience. SISSA is an international school promoting basic and applied research in Neuroscience, Mathematics and Physics and dedicated to the training of PhD students. Lab space and other resources will be commensurate with the appointment. Shared facilities include cell culture rooms, viral vector facilities, confocal microscopes, animal facilities, molecular and biochemical facilities, human cognition labs with EEG, TMS, and eye tracking systems, mechatronics workshop, and computing facilities. Agreements with national and international MRI scanning facilities are also in place. SISSA encourages fruitful exchanges between neuroscientists and other researchers including data scientists, physicists and mathematicians. Interested colleagues are invited to send a single pdf file including a full CV, a brief description of past and future research interests (up to 1,000 words), and the names of three referees to neuro.search@sissa.it. Selected candidates will be invited for an online or in-person seminar and 1- on-1 meetings in summer/autumn 2024. Deadline: A first evaluation round will consider all applications submitted before 15 May 2024. Later applications might be considered if no suitable candidates have been identified yet.

Prof. Amir Raz

Sleep expert with a Ph.D. degree in Neuroscience, Psychology, Biomedical Engineering or similar.

Dr. Demian Battaglia/Dr. Romain Goutagny

The postdoc position is under the joint co-mentoring of Dr. Demian Battaglia and Dr. Romain Goutagny at the University of Strasbourg, France, in the Functional System's Dynamics team – FunSy. The position starts as soon as possible and can last up to two years. The job offer is funded by the French ANR 'HippoComp' project, which focuses on the complexity of hippocampal oscillations and the hypothesis that such complexity can serve as a computational resource. The team performs electrophysiological recordings in the hippocampus and cortex during spatial navigation and memory tasks in mice (wild type and mutant developing various neuropathologies) and have access to vast data through local and international cooperation. They use a large spectrum of computational tools ranging from time-series and network analyses, information theory, and machine-learning to multi-scale computational modeling.

Bruno A. Olshausen

The Helen Wills Neuroscience Institute together with the Department of Statistics at UC Berkeley is conducting a faculty search in the area of computational or theoretical neuroscience. This is an ideal opportunity for computational/theoretical neuroscientists who are engaged in both model and theory development and collaborative work with experimentalists.

Jorge Jaramillo

The Grossman Center for Quantitative Biology and Human Behavior at the University of Chicago seeks outstanding applicants for multiple postdoctoral positions in computational and theoretical neuroscience. Appointees will join as Grossman Center Postdoctoral Fellows, with the freedom to work with any of its faculty members. We especially welcome applicants who develop computational models and machine learning analysis methods to study the brain at the circuits, systems, or cognitive levels. The current faculty members of the Grossman Center to work with are: Brent Doiron, Jorge Jaramillo, and Ramon Nogueira. Appointees will have access to state-of-the-art facilities and multiple opportunities for collaboration with exceptional experimental labs within the Department of Neurobiology, as well as other labs from the departments of Physics, Computer Sciences, and Statistics. The Grossman Center offers competitive postdoctoral salaries in the vibrant and international city of Chicago, and a rich intellectual environment that includes the Argonne National Laboratory and the Data Science Institute. The Grossman Center is currently engaged in a major expansion that includes the incorporation of several new faculty members in the next few years.

Lyle Muller

This position will involve collaboration between our laboratory and researchers with expertise in advanced methods of brain imaging (Mark Schnitzer, Stanford), neuroengineering (Duygu Kuzum, UCSD), theoretical neuroscience (Todd Coleman, Stanford), and neurophysiology of visual perception (John Reynolds, Salk Institute for Biological Studies). In collaboration with this multi-disciplinary team, this researcher will apply new signal processing techniques for multisite spatiotemporal data to understand cortical dynamics during visual perception. This project will also involve development of spiking network models to understand the mechanisms underlying observed activity patterns. The project may include intermittent travel between labs to present results and facilitate collaborative work.

N/A

The Neuroscience Department of the International School for Advanced Studies (SISSA) invites expressions of interest from scientists for multiple tenure-track positions in various fields of Neuroscience with anticipated start in 2025. The Department aims to enhance its activities in cognitive neuroscience, computational and theoretical neuroscience, systems neuroscience, molecular and cellular research, genomics, and genetics, and to strengthen cross-field interactions. The working and teaching language at SISSA is English. This is an equal opportunity career initiative.

N/A

The PostDoctoral researcher will conduct research activities in modelling and simulation of reward-modulated prosocial behavior and decision-making. The position is part of a larger effort to uncover the computational and mechanistic bases of prosociality and empathy at the behavioral and circuit levels. The role involves working at the interface between experimental data (animal behavior and electrophysiology) and theoretical modelling, with an emphasis on Multi-Agent Reinforcement Learning and neural population dynamics.

Jorge Jaramillo

We are looking for an outstanding applicant to develop large-scale circuit models for decision making within a collaborative consortium that includes the Allen Institute for Neural Dynamics, New York University, and the University of Chicago. This ambitious NIH-funded project requires the creativity and expertise to integrate multimodal data sets (e.g., connectivity, large-scale neural recordings, behavior) into a comprehensive modeling framework. The successful applicant will join Jorge Jaramillo’s Distributed Neural Dynamics and Control Lab at the Grossman Center at the University of Chicago. Throughout the course of the postdoctoral training, there will be opportunities to visit the other sites in Seattle (Karel Svoboda) and New York (Adam Carter, Xiao-Jing Wang) for additional training and collaboration opportunities. Appointees will join as Grossman Center Postdoctoral Fellows at the University of Chicago and will have access to state-of-the-art facilities and additional opportunities for collaboration with exceptional experimental labs within the Department of Neurobiology, as well as other labs from the departments of Physics, Computer Sciences, and Statistics. The Grossman Center offers competitive postdoctoral salaries in the vibrant and international city of Chicago, and a rich intellectual environment that includes the Argonne National Laboratory and the Data Science Institute. Postdoctoral fellows will also have the possibility to work in additional projects with other Grossman Center faculty members.

Jorge Jaramillo

We are looking for an outstanding applicant to develop large-scale circuit models for decision making within a collaborative consortium that includes the Allen Institute for Neural Dynamics, New York University, and the University of Chicago. This ambitious NIH-funded project requires the creativity and expertise to integrate multimodal data sets (e.g., connectivity, large-scale neural recordings, behavior) into a comprehensive modeling framework. The successful applicant will join Jorge Jaramillo’s Distributed Neural Dynamics and Control Lab at the Grossman Center at the University of Chicago. Throughout the course of the postdoctoral training, there will be opportunities to visit the other sites in Seattle (Karel Svoboda) and New York (Adam Carter, Xiao-Jing Wang) for additional training and collaboration opportunities. Appointees will join as Grossman Center Postdoctoral Fellows at the University of Chicago and will have access to state-of-the-art facilities and additional opportunities for collaboration with exceptional experimental labs within the Department of Neurobiology, as well as other labs from the departments of Physics, Computer Sciences, and Statistics. The Grossman Center offers competitive postdoctoral salaries in the vibrant and international city of Chicago, and a rich intellectual environment that includes the Argonne National Laboratory and the Data Science Institute. Postdoctoral fellows will also have the possibility to work in additional projects with other Grossman Center faculty members.

Alberto Bacci

The successful candidate will work on inhibitory circuits of the prefrontal cortex of mice. In particular, they will study the properties and plasticity of synapses connecting a rich diversity of prefrontal cortical neuron subtypes. The candidate will also perform and analyze electrophysiological recordings in vivo, using high-density Neuropixels probes. This project is part of an ERA-Net NEURON international consortium and focuses on the rich diversity of GABAergic interneurons and their impact on the functional states of prefrontal cortical networks in healthy and diseased states.

Maximilian Riesenhuber, PhD

We have an opening for a postdoc position investigating the neural bases of deep multimodal learning in the brain. The project involves EEG and laminar 7T imaging (in collaboration with Dr. Peter Bandettini’s lab at NIMH) to test computational hypotheses for how the brain learns multimodal concept representations. Responsibilities of the postdoc include running EEG and fMRI experiments, data analysis and manuscript preparation. Georgetown University has a vibrant neuroscience community with over fifty labs participating in the Interdisciplinary Program in Neuroscience and a number of relevant research centers, including the new Center for Neuroengineering (cne.georgetown.edu). Interested candidates should submit a CV, a brief (1 page) statement of research interests, representative reprints, and the names and contact information of three references to Interfolio via https://apply.interfolio.com/148520. Faxed, emailed, or mailed applications will not be accepted. Questions about the position can be directed to Maximilian Riesenhuber (mr287@georgetown.edu).

Jean-Pascal Pfister

The Theoretical Neuroscience Group of the University of Bern is seeking applications for a PhD position, funded by a Swiss National Science Foundation grant titled “Why Spikes?”. This project aims at answering a nearly century-old question in Neuroscience: “What are spikes good for?”. Indeed, since the discovery of action potentials by Lord Adrian in 1926, it has remained largely unknown what the benefits of spiking neurons are, when compared to analog neurons. Traditionally, it has been argued that spikes are good for long-distance communication or for temporally precise computation. However, there is no systematic study that quantitatively compares the communication as well as the computational benefits of spiking neuron w.r.t analog neurons. The aim of the project is to systematically quantify the benefits of spiking at various levels by developing and analyzing appropriate mathematical models. The PhD student will be supervised by Prof. Jean-Pascal Pfister (Theoretical Neuroscience Group, Department of Physiology, University of Bern). The project will involve close collaborations within a highly motivated team as well as regular exchange of ideas with the other theory groups at the institute.

N/A

The Grossman Center for Quantitative Biology and Human Behavior at the University of Chicago seeks outstanding applicants for multiple postdoctoral positions in computational and theoretical neuroscience. We especially welcome applicants who develop mathematical approaches, computational models, and machine learning methods to study the brain at the circuits, systems, or cognitive levels. The current faculty members of the Grossman Center to work with are: Brent Doiron’s lab investigates how the cellular and synaptic circuitry of neuronal circuits supports the complex dynamics and computations that are routinely observed in the brain. Jorge Jaramillo’s lab investigates how subcortical structures interact with cortical circuits to subserve cognitive processes such as memory, attention, and decision making. Ramon Nogueira’s lab investigates the geometry of representations as the computational support of cognitive processes like abstraction in noisy artificial and biological neural networks. Marcella Noorman’s lab investigates how properties of synapses, neurons, and circuits shape the neural dynamics that enable flexible and efficient computation. Samuel Muscinelli’s lab studies how the anatomy of brain circuits both governs learning and adapts to it. We combine analytical theory, machine learning, and data analysis, in close collaboration with experimentalists. Appointees will have access to state-of-the-art facilities and multiple opportunities for collaboration with exceptional experimental labs within the Neuroscience Institute, as well as other labs from the departments of Physics, Computer Sciences, and Statistics. The Grossman Center offers competitive postdoctoral salaries in the vibrant and international city of Chicago, and a rich intellectual environment that includes the Argonne National Laboratory and UChicago’s Data Science Institute. The Neuroscience Institute is currently engaged in a major expansion that includes the incorporation of several new faculty members in the next few years.

Convergent large-scale network and local vulnerabilities underlie brain atrophy across Parkinson’s disease stages

AutoMIND: Deep inverse models for revealing neural circuit invariances

Simulating Thought Disorder: Fine-Tuning Llama-2 for Synthetic Speech in Schizophrenia

Brain Emulation Challenge Workshop

Brain Emulation Challenge workshop will tackle cutting-edge topics such as ground-truthing for validation, leveraging artificial datasets generated from virtual brain tissue, and the transformative potential of virtual brain platforms, such as applied to the forthcoming Brain Emulation Challenge.

Predicting traveling waves: a new mathematical technique to link the structure of a network to the specific patterns of neural activity

Use case determines the validity of neural systems comparisons

Deep learning provides new data-driven tools to relate neural activity to perception and cognition, aiding scientists in developing theories of neural computation that increasingly resemble biological systems both at the level of behavior and of neural activity. But what in a deep neural network should correspond to what in a biological system? This question is addressed implicitly in the use of comparison measures that relate specific neural or behavioral dimensions via a particular functional form. However, distinct comparison methodologies can give conflicting results in recovering even a known ground-truth model in an idealized setting, leaving open the question of what to conclude from the outcome of a systems comparison using any given methodology. Here, we develop a framework to make explicit and quantitative the effect of both hypothesis-driven aspects—such as details of the architecture of a deep neural network—as well as methodological choices in a systems comparison setting. We demonstrate via the learning dynamics of deep neural networks that, while the role of the comparison methodology is often de-emphasized relative to hypothesis-driven aspects, this choice can impact and even invert the conclusions to be drawn from a comparison between neural systems. We provide evidence that the right way to adjudicate a comparison depends on the use case—the scientific hypothesis under investigation—which could range from identifying single-neuron or circuit-level correspondences to capturing generalizability to new stimulus properties

Localisation of Seizure Onset Zone in Epilepsy Using Time Series Analysis of Intracranial Data

There are over 30 million people with drug-resistant epilepsy worldwide. When neuroimaging and non-invasive neural recordings fail to localise seizure onset zones (SOZ), intracranial recordings become the best chance for localisation and seizure-freedom in those patients. However, intracranial neural activities remain hard to visually discriminate across recording channels, which limits the success of intracranial visual investigations. In this presentation, I present methods which quantify intracranial neural time series and combine them with explainable machine learning algorithms to localise the SOZ in the epileptic brain. I present the potentials and limitations of our methods in the localisation of SOZ in epilepsy providing insights for future research in this area.

Modelling the fruit fly brain and body

Through recent advances in microscopy, we now have an unprecedented view of the brain and body of the fruit fly Drosophila melanogaster. We now know the connectivity at single neuron resolution across the whole brain. How do we translate these new measurements into a deeper understanding of how the brain processes sensory information and produces behavior? I will describe two computational efforts to model the brain and the body of the fruit fly. First, I will describe a new modeling method which makes highly accurate predictions of neural activity in the fly visual system as measured in the living brain, using only measurements of its connectivity from a dead brain [1], joint work with Jakob Macke. Second, I will describe a whole body physics simulation of the fruit fly which can accurately reproduce its locomotion behaviors, both flight and walking [2], joint work with Google DeepMind.

Reimagining the neuron as a controller: A novel model for Neuroscience and AI

We build upon and expand the efficient coding and predictive information models of neurons, presenting a novel perspective that neurons not only predict but also actively influence their future inputs through their outputs. We introduce the concept of neurons as feedback controllers of their environments, a role traditionally considered computationally demanding, particularly when the dynamical system characterizing the environment is unknown. By harnessing a novel data-driven control framework, we illustrate the feasibility of biological neurons functioning as effective feedback controllers. This innovative approach enables us to coherently explain various experimental findings that previously seemed unrelated. Our research has profound implications, potentially revolutionizing the modeling of neuronal circuits and paving the way for the creation of alternative, biologically inspired artificial neural networks.

Neuromodulation of striatal D1 cells shapes BOLD fluctuations in anatomically connected thalamic and cortical regions

Understanding how macroscale brain dynamics are shaped by microscale mechanisms is crucial in neuroscience. We investigate this relationship in animal models by directly manipulating cellular properties and measuring whole-brain responses using resting-state fMRI. Specifically, we explore the impact of chemogenetically neuromodulating D1 medium spiny neurons in the dorsomedial caudate putamen (CPdm) on BOLD dynamics within a striato-thalamo-cortical circuit in mice. Our findings indicate that CPdm neuromodulation alters BOLD dynamics in thalamic subregions projecting to the dorsomedial striatum, influencing both local and inter-regional connectivity in cortical areas. This study contributes to understanding structure–function relationships in shaping inter-regional communication between subcortical and cortical levels.

Tracking subjects' strategies in behavioural choice experiments at trial resolution

Psychology and neuroscience are increasingly looking to fine-grained analyses of decision-making behaviour, seeking to characterise not just the variation between subjects but also a subject's variability across time. When analysing the behaviour of each subject in a choice task, we ideally want to know not only when the subject has learnt the correct choice rule but also what the subject tried while learning. I introduce a simple but effective Bayesian approach to inferring the probability of different choice strategies at trial resolution. This can be used both for inferring when subjects learn, by tracking the probability of the strategy matching the target rule, and for inferring subjects use of exploratory strategies during learning. Applied to data from rodent and human decision tasks, we find learning occurs earlier and more often than estimated using classical approaches. Around both learning and changes in the rewarded rules the exploratory strategies of win-stay and lose-shift, often considered complementary, are consistently used independently. Indeed, we find the use of lose-shift is strong evidence that animals have latently learnt the salient features of a new rewarded rule. Our approach can be extended to any discrete choice strategy, and its low computational cost is ideally suited for real-time analysis and closed-loop control.

Bio-realistic multiscale modeling of cortical circuits

A central question in neuroscience is how the structure of brain circuits determines their activity and function. To explore this systematically, we developed a 230,000-neuron model of mouse primary visual cortex (area V1). The model integrates a broad array of experimental data:Distribution and morpho-electric properties of different neuron types in V1.

Diffuse coupling in the brain - A temperature dial for computation

The neurobiological mechanisms of arousal and anesthesia remain poorly understood. Recent evidence highlights the key role of interactions between the cerebral cortex and the diffusely projecting matrix thalamic nuclei. Here, we interrogate these processes in a whole-brain corticothalamic neural mass model endowed with targeted and diffusely projecting thalamocortical nuclei inferred from empirical data. This model captures key features seen in propofol anesthesia, including diminished network integration, lowered state diversity, impaired susceptibility to perturbation, and decreased corticocortical coherence. Collectively, these signatures reflect a suppression of information transfer across the cerebral cortex. We recover these signatures of conscious arousal by selectively stimulating the matrix thalamus, recapitulating empirical results in macaque, as well as wake-like information processing states that reflect the thalamic modulation of largescale cortical attractor dynamics. Our results highlight the role of matrix thalamocortical projections in shaping many features of complex cortical dynamics to facilitate the unique communication states supporting conscious awareness.

Cognitive Computational Neuroscience 2023

CCN is an annual conference that serves as a forum for cognitive science, neuroscience, and artificial intelligence researchers dedicated to understanding the computations that underlie complex behavior.

Interacting spiral wave patterns underlie complex brain dynamics and are related to cognitive processing

The large-scale activity of the human brain exhibits rich and complex patterns, but the spatiotemporal dynamics of these patterns and their functional roles in cognition remain unclear. Here by characterizing moment-by-moment fluctuations of human cortical functional magnetic resonance imaging signals, we show that spiral-like, rotational wave patterns (brain spirals) are widespread during both resting and cognitive task states. These brain spirals propagate across the cortex while rotating around their phase singularity centres, giving rise to spatiotemporal activity dynamics with non-stationary features. The properties of these brain spirals, such as their rotational directions and locations, are task relevant and can be used to classify different cognitive tasks. We also demonstrate that multiple, interacting brain spirals are involved in coordinating the correlated activations and de-activations of distributed functional regions; this mechanism enables flexible reconfiguration of task-driven activity flow between bottom-up and top-down directions during cognitive processing. Our findings suggest that brain spirals organize complex spatiotemporal dynamics of the human brain and have functional correlates to cognitive processing.

Bernstein Student Workshop Series

The Bernstein Student Workshop Series is an initiative of the student members of the Bernstein Network. It provides a unique opportunity to enhance the technical exchange on a peer-to-peer basis. The series is motivated by the idea of bridging the gap between theoretical and experimental neuroscience by bringing together methodological expertise in the network. Unlike conventional workshops, a talented junior scientist will first give a tutorial about a specific theoretical or experimental technique, and then give a talk about their own research to demonstrate how the technique helps to address neuroscience questions. The workshop series is designed to cover a wide range of theoretical and experimental techniques and to elucidate how different techniques can be applied to answer different types of neuroscience questions. Combining the technical tutorial and the research talk, the workshop series aims to promote knowledge sharing in the community and enhance in-depth discussions among students from diverse backgrounds.

Bernstein Student Workshop Series

The Bernstein Student Workshop Series is an initiative of the student members of the Bernstein Network. It provides a unique opportunity to enhance the technical exchange on a peer-to-peer basis. The series is motivated by the idea of bridging the gap between theoretical and experimental neuroscience by bringing together methodological expertise in the network. Unlike conventional workshops, a talented junior scientist will first give a tutorial about a specific theoretical or experimental technique, and then give a talk about their own research to demonstrate how the technique helps to address neuroscience questions. The workshop series is designed to cover a wide range of theoretical and experimental techniques and to elucidate how different techniques can be applied to answer different types of neuroscience questions. Combining the technical tutorial and the research talk, the workshop series aims to promote knowledge sharing in the community and enhance in-depth discussions among students from diverse backgrounds.

Bernstein Student Workshop Series

The Bernstein Student Workshop Series is an initiative of the student members of the Bernstein Network. It provides a unique opportunity to enhance the technical exchange on a peer-to-peer basis. The series is motivated by the idea of bridging the gap between theoretical and experimental neuroscience by bringing together methodological expertise in the network. Unlike conventional workshops, a talented junior scientist will first give a tutorial about a specific theoretical or experimental technique, and then give a talk about their own research to demonstrate how the technique helps to address neuroscience questions. The workshop series is designed to cover a wide range of theoretical and experimental techniques and to elucidate how different techniques can be applied to answer different types of neuroscience questions. Combining the technical tutorial and the research talk, the workshop series aims to promote knowledge sharing in the community and enhance in-depth discussions among students from diverse backgrounds.

Building System Models of Brain-Like Visual Intelligence with Brain-Score

Research in the brain and cognitive sciences attempts to uncover the neural mechanisms underlying intelligent behavior in domains such as vision. Due to the complexities of brain processing, studies necessarily had to start with a narrow scope of experimental investigation and computational modeling. I argue that it is time for our field to take the next step: build system models that capture a range of visual intelligence behaviors along with the underlying neural mechanisms. To make progress on system models, we propose integrative benchmarking – integrating experimental results from many laboratories into suites of benchmarks that guide and constrain those models at multiple stages and scales. We show-case this approach by developing Brain-Score benchmark suites for neural (spike rates) and behavioral experiments in the primate visual ventral stream. By systematically evaluating a wide variety of model candidates, we not only identify models beginning to match a range of brain data (~50% explained variance), but also discover that models’ brain scores are predicted by their object categorization performance (up to 70% ImageNet accuracy). Using the integrative benchmarks, we develop improved state-of-the-art system models that more closely match shallow recurrent neuroanatomy and early visual processing to predict primate temporal processing and become more robust, and require fewer supervised synaptic updates. Taken together, these integrative benchmarks and system models are first steps to modeling the complexities of brain processing in an entire domain of intelligence.

ISAM-NIG Webinars

Optimized Non-Invasive Brain Stimulation for Addiction Treatment

Invariant neural subspaces maintained by feedback modulation

This session is a double feature of the Cologne Theoretical Neuroscience Forum and the Institute of Neuroscience and Medicine (INM-6) Computational and Systems Neuroscience of the Jülich Research Center.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

The evolution of computation in the brain: Insights from studying the retina

The retina is probably the most accessible part of the vertebrate central nervous system. Its computational logic can be interrogated in a dish, from patterns of lights as the natural input, to spike trains on the optic nerve as the natural output. Consequently, retinal circuits include some of the best understood computational networks in neuroscience. The retina is also ancient, and central to the emergence of neurally complex life on our planet. Alongside new locomotor strategies, the parallel evolution of image forming vision in vertebrate and invertebrate lineages is thought to have driven speciation during the Cambrian. This early investment in sophisticated vision is evident in the fossil record and from comparing the retina’s structural make up in extant species. Animals as diverse as eagles and lampreys share the same retinal make up of five classes of neurons, arranged into three nuclear layers flanking two synaptic layers. Some retina neuron types can be linked across the entire vertebrate tree of life. And yet, the functions that homologous neurons serve in different species, and the circuits that they innervate to do so, are often distinct to acknowledge the vast differences in species-specific visuo-behavioural demands. In the lab, we aim to leverage the vertebrate retina as a discovery platform for understanding the evolution of computation in the nervous system. Working on zebrafish alongside birds, frogs and sharks, we ask: How do synapses, neurons and networks enable ‘function’, and how can they rearrange to meet new sensory and behavioural demands on evolutionary timescales?

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

Nonlinear spatial integration in retinal bipolar cells shapes the encoding of artificial and natural stimuli

Vision begins in the eye, and what the “retina tells the brain” is a major interest in visual neuroscience. To deduce what the retina encodes (“tells”), computational models are essential. The most important models in the retina currently aim to understand the responses of the retinal output neurons – the ganglion cells. Typically, these models make simplifying assumptions about the neurons in the retinal network upstream of ganglion cells. One important assumption is linear spatial integration. In this talk, I first define what it means for a neuron to be spatially linear or nonlinear and how we can experimentally measure these phenomena. Next, I introduce the neurons upstream to retinal ganglion cells, with focus on bipolar cells, which are the connecting elements between the photoreceptors (input to the retinal network) and the ganglion cells (output). This pivotal position makes bipolar cells an interesting target to study the assumption of linear spatial integration, yet due to their location buried in the middle of the retina it is challenging to measure their neural activity. Here, I present bipolar cell data where I ask whether the spatial linearity holds under artificial and natural visual stimuli. Through diverse analyses and computational models, I show that bipolar cells are more complex than previously thought and that they can already act as nonlinear processing elements at the level of their somatic membrane potential. Furthermore, through pharmacology and current measurements, I illustrate that the observed spatial nonlinearity arises at the excitatory inputs to bipolar cells. In the final part of my talk, I address the functional relevance of the nonlinearities in bipolar cells through combined recordings of bipolar and ganglion cells and I show that the nonlinearities in bipolar cells provide high spatial sensitivity to downstream ganglion cells. Overall, I demonstrate that simple linear assumptions do not always apply and more complex models are needed to describe what the retina “tells” the brain.

A nonlinear shot noise model for calcium-based synaptic plasticity

Activity dependent synaptic plasticity is considered to be a primary mechanism underlying learning and memory. Yet it is unclear whether plasticity rules such as STDP measured in vitro apply in vivo. Network models with STDP predict that activity patterns (e.g., place-cell spatial selectivity) should change much faster than observed experimentally. We address this gap by investigating a nonlinear calcium-based plasticity rule fit to experiments done in physiological conditions. In this model, LTP and LTD result from intracellular calcium transients arising almost exclusively from synchronous coactivation of pre- and postsynaptic neurons. We analytically approximate the full distribution of nonlinear calcium transients as a function of pre- and postsynaptic firing rates, and temporal correlations. This analysis directly relates activity statistics that can be measured in vivo to the changes in synaptic efficacy they cause. Our results highlight that both high-firing rates and temporal correlations can lead to significant changes to synaptic efficacy. Using a mean-field theory, we show that the nonlinear plasticity rule, without any fine-tuning, gives a stable, unimodal synaptic weight distribution characterized by many strong synapses which remain stable over long periods of time, consistent with electrophysiological and behavioral studies. Moreover, our theory explains how memories encoded by strong synapses can be preferentially stabilized by the plasticity rule. We confirmed our analytical results in a spiking recurrent network. Interestingly, although most synapses are weak and undergo rapid turnover, the fraction of strong synapses are sufficient for supporting realistic spiking dynamics and serve to maintain the network’s cluster structure. Our results provide a mechanistic understanding of how stable memories may emerge on the behavioral level from an STDP rule measured in physiological conditions. Furthermore, the plasticity rule we investigate is mathematically equivalent to other learning rules which rely on the statistics of coincidences, so we expect that our formalism will be useful to study other learning processes beyond the calcium-based plasticity rule.

NMC4 Panel: The Contribution of Models vs Data

Spontaneous activity competes with externally evoked responses in sensory cortex

The interaction between spontaneously and externally evoked neuronal activity is fundamental for a functional brain. Increasing evidence suggests that bursts of high-power oscillations in the 15-30 Hz beta-band represent activation of resting state networks and can mask perception of external cues. Yet demonstration of the effect of beta power modulation on perception in real-time is missing, and little is known about the underlying mechanism. In this talk I will present the methods we developed to fill this gap together with our recent results. We used a closed-loop stimulus-intensity adjustment system based on online burst-occupancy analyses in rats involved in a forepaw vibrotactile detection task. We found that the masking influence of burst-occupancy on perception can be counterbalanced in real-time by adjusting the vibration amplitude. Offline analysis of firing-rates and local field potentials across cortical layers and frequency bands confirmed that beta-power in the somatosensory cortex anticorrelated with sensory evoked responses. Mechanistically, bursts in all bands were accompanied by transient synchronization of cell assemblies, but only beta-bursts were followed by a reduction of firing-rate. Our closed loop approach reveals that spontaneous beta-bursts reflect a dynamic state that competes with external stimuli.