Dr. Jessica Ausborn’s group at Drexel University College of Medicine, in the Department of Neurobiology & Anatomy has a postdoctoral position available for an exciting new research project involving computational models of sensorimotor integration based on neural and behavior data in Drosophila. The interdisciplinary collaboration with the experimental group of Dr. Katie von Reyn (School of Biomedical Engineering) will involve a variety of computational techniques including the development of biophysically detailed and more abstract mathematical models together with machine learning and data science techniques to identify and describe the algorithms computed in neuronal pathways that perform sensorimotor transformations. The Ausborn laboratory is part of an interdisciplinary group of Drexel’s Neuroengineering program that includes computational and experimental investigators. This collaborative, interdisciplinary environment enables us to probe biological systems in a way that would not be possible with either an exclusively experimental or computational approach. Applicants should forward a cover letter, curriculum vitae, statement of research interests, and contact information of three references to Jessica Ausborn (ja696@drexel.edu). Salary will be commensurate with experience based on NIH guidelines.

We are looking for highly motivated researchers to join our group in interdisciplinary projects that focus on the development of computational models to understand how linguistic information is represented in the human brain. Computational encoding models in combination with deep learning-based machine learning techniques will be developed, compared, and applied to identify linguistic representations in the brain. The projects are conducted in collaboration with UC Berkeley.

We are looking for a highly motivated researcher to join our group in interdisciplinary projects that focus on the development of computational models to understand how linguistic information is represented in the human brain during language comprehension. Computational encoding models in combination with deep learning-based machine learning techniques will be developed, compared, and applied to identify linguistic representations in the brain across languages.

We are looking for a highly motivated researcher to join our group in interdisciplinary projects that focus on the development of computational models to understand how linguistic information is represented in the human brain during multi-modal language comprehension. Computational encoding models in combination with deep learning-based machine learning techniques will be developed, compared, and applied to identify linguistic representations in the brain. The projects are conducted in collaboration with UC Berkeley.

We are announcing one or more 2-year postdoc positions in identification and analysis of lexical semantic change using computational models applied to diachronic texts. Our languages change over time. As a consequence, words may look the same, but have different meanings at different points in time, a phenomenon called lexical semantic change (LSC). To facilitate interpretation, search, and analysis of old texts, we build computational methods for automatic detection and characterization of LSC from large amounts of text. Our outputs will be used by the lexicographic R&D unit that compiles the Swedish Academy dictionaries, as well as by researchers from the humanities and social sciences that include textual analysis as a central methodological component. The Change is Key! program and the Towards Computational Lexical Semantic Change Detection research project offer a vibrant research environment for this exciting and rapidly growing cutting-edge research field in NLP. There is a unique opportunity to contribute to the field of LSC, but also to humanities and social sciences through our active collaboration with international researchers in historical linguistics, analytical sociology, gender studies, conceptual history, and literary studies.

An ERC-funded postdoctoral position is available in the Cossart lab at the Mediterranean Institute of Neurobiology (INSERM, Aix-Marseille University, Marseille, France) to work in a collaborative, interdisciplinary, and friendly environment. The Cossart lab aims at understanding memory circuits in the brain and describing how they develop in health and disease. The candidate will apply their skills to extract information from our datasets, build computational models to make predictions, and work in close collaboration with experimentalists. The candidate will be co-supervised by Dr. Lorenzo Fontolan, a computational neuroscientist who recently started his research group at Inmed.

A three-year post-doctoral position in theoretical neuroscience is open to explore the mechanisms of interaction between interoceptive cardiac and exteroceptive tactile inputs at the cortical level. We aim to develop data-based computational models of cardiac and somatosensory cortical circuit dynamics. Building on these models we will determine the conditions under which interactions between exteroceptive and interoceptive inputs occur and which underlying mechanisms (e.g., phase-resetting, gating, phasic arousal,..) best explain experimental data.

The position will be part of my group, and the project is in collaboration with the Moser lab (KISN). The position will be for 2 years. The successful candidate will combine the analysis of large-scale neural population recordings obtained with Neuropixels probes (the data will be collected by other team members) with the development of computational models.

Research Fellow position: This project explores individuality of neural circuits and neural activity in C. elegans brain, based on whole-brain-activity data and information about the C. elegans connectome (neural circuit wiring data). The project combines data driven approaches from AI on the one hand, and whole-brain computational modelling on the other. PhD opening: How do worms move in 3D? To address this question, we have built a 3D imaging system and have collected hours of footage. Prior work has focused on developing machine vision methods to reconstruct postures and trajectories; characterising postures and locomotion behaviours; and characterising and modelling locomotion strategies and foraging behaviours. This PhD can build on these foundations to perform exciting innovative experiments, and/or to build computational models of worm locomotion.

We are looking for a highly motivated researcher to join our group for an ERC-project that focuses on the development of computational models to understand how linguistic information is represented in the human brain. Computational encoding models in combination with large language models will be developed, compared, and applied to identify linguistic representations in the brain. We are a small yet dynamic international team, driven by motivation and collaboration within a supportive environment.

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.

We invite applications for key roles in the TUNASBE project, funded by the Swiss SNSF, which aims to improve AI’s ability to understand human states, activities, and complex social behaviors in natural settings. The project focuses on interpreting non-verbal cues—such as gaze, gestures, and attention—through multi-task learning models that capture the subtleties of human interactions. The project will be supported by two PhD students and one postdoctoral researcher, to lead the design and development of computational models, focusing on gaze, attention, and multi-cue integration for human-centric computer vision analysis of social behaviors at the subject and scene levels in general settings.

Within the last decade, Deep Artificial Neural Networks (DNNs) have emerged as powerful computer vision systems that match or exceed human performance on many benchmark tasks such as image classification. But whether current DNNs are suitable computational models of the human visual system remains an open question: While DNNs have proven to be capable of predicting neural activations in primate visual cortex, psychophysical experiments have shown behavioral differences between DNNs and human subjects, as quantified by error consistency. Error consistency is typically measured by briefly presenting natural or corrupted images to human subjects and asking them to perform an n-way classification task under time pressure. But for how long should stimuli ideally be presented to guarantee a fair comparison with DNNs? Here we investigate the influence of presentation time on error consistency, to test the hypothesis that higher-level processing drives behavioral differences. We systematically vary presentation times of backward-masked stimuli from 8.3ms to 266ms and measure human performance and reaction times on natural, lowpass-filtered and noisy images. Our experiment constitutes a fine-grained analysis of human image classification under both image corruptions and time pressure, showing that even drastically time-constrained humans who are exposed to the stimuli for only two frames, i.e. 16.6ms, can still solve our 8-way classification task with success rates way above chance. We also find that human-to-human error consistency is already stable at 16.6ms.

Neurodegenerative diseases involve accumulation of aberrant proteins in the brain, leading to brain damage and progressive cognitive and behavioral dysfunction. Many gaps exist in our understanding of how these diseases initiate and how they progress through the brain. However, evidence has accumulated supporting the hypothesis that aberrant proteins can be transported using the brain’s intrinsic network architecture — in other words, using the brain’s natural communication pathways. This theory forms the basis of connectome-based computational models, which combine real human data and theoretical disease mechanisms to simulate the progression of neurodegenerative diseases through the brain. In this talk, I will first review work leading to the development of connectome-based models, and work from my lab and others that have used these models to test hypothetical modes of disease progression. Second, I will discuss the future and potential of connectome-based models to achieve clinically useful individual-level predictions, as well as to generate novel biological insights into disease progression. Along the way, I will highlight recent work by my lab and others that is already moving the needle toward these lofty goals.

To effectively move in complex and changing environments, animals must control locomotor speed and gait, while precisely coordinating and adapting limb movements to the terrain. The underlying neuronal control is facilitated by circuits in the spinal cord, which integrate supraspinal commands and afferent feedback signals to produce coordinated rhythmic muscle activations necessary for stable locomotion. I will present a series of computational models investigating dynamics of central neuronal interactions as well as a neuromechanical model that integrates neuronal circuits with a model of the musculoskeletal system. These models closely reproduce speed-dependent gait expression and experimentally observed changes following manipulation of multiple classes of genetically-identified neuronal populations. I will discuss the utility of these models in providing experimentally testable predictions for future studies.

The eye is a multi-component biological system, where mechanics, optics, transport phenomena and chemical reactions are strictly interlaced, characterized by the typical bio-variability in sizes and material properties. The eye’s response to external action is patient-specific and it can be predicted only by a customized approach, that accounts for the multiple physics and for the intrinsic microstructure of the tissues, developed with the aid of forefront means of computational biomechanics. Our activity in the last years has been devoted to the development of a comprehensive model of the cornea that aims at being entirely patient-specific. While the geometrical aspects are fully under control, given the sophisticated diagnostic machinery able to provide a fully three-dimensional images of the eye, the major difficulties are related to the characterization of the tissues, which require the setup of in-vivo tests to complement the well documented results of in-vitro tests. The interpretation of in-vivo tests is very complex, since the entire structure of the eye is involved and the characterization of the single tissue is not trivial. The availability of micromechanical models constructed from detailed images of the eye represents an important support for the characterization of the corneal tissues, especially in the case of pathologic conditions. In this presentation I will provide an overview of the research developed in our group in terms of computational models and experimental approaches developed for the human cornea.

In this talk, we will examine human behavior from the perspective of the choices we make every day. We will study the role of the brain in enabling these decisions and discuss some simple computational models of decision making and the neural basis. Towards the end, we will have a short, interactive session to engage in some easy decisions that will help us discover our own biases.

One elementary brain function that underlies many of our cognitive behaviors is the ability to maintain parametric information briefly in mind, in the time scale of seconds, to span delays between sensory information and actions. This component of working memory is fragile and quickly degrades with delay length. Under the assumption that behavioral delay-dependencies mark core functions of the working memory system, our goal is to find a neural circuit model that represents their neural mechanisms and apply it to research on working memory deficits in neuropsychiatric disorders. We have constrained computational models of spatial working memory with delay-dependent behavioral effects and with neural recordings in the prefrontal cortex during visuospatial working memory. I will show that a simple bump attractor model with weak inhomogeneities and short-term plasticity mechanisms can link neural data with fine-grained behavioral output in a trial-by-trial basis and account for the main delay-dependent limitations of working memory: precision, cardinal repulsion biases and serial dependence. I will finally present data from participants with neuropsychiatric disorders that suggest that serial dependence in working memory is specifically altered, and I will use the model to infer the possible neural mechanisms affected.

Large-scale numerical simulations of brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. Similarly, spiking neural networks are also gaining traction in machine learning with the promise that neuromorphic hardware will eventually make them much more energy efficient than classical ANNs. In this session, we will present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale spiking neuronal networks to address the challenge of efficient simulations. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. GeNN was originally developed as a pure C++ and CUDA library but, subsequently, we have added a Python interface and OpenCL backend. We will briefly cover the history and basic philosophy of GeNN and show some simple examples of how it is used and how it interacts with other Open Source frameworks such as Brian2GeNN and PyNN.

Conventionally, neurons are thought to be cellular units that process synaptic inputs into synaptic spikes. However, it is well known that neurons can also spike spontaneously and display a rich repertoire of firing properties with no apparent functional relevance e.g. in in vitro cortical slice preparations. In this talk, I will propose a hypothesis according to which intrinsic excitability in neurons may be a survival mechanism to minimize toxic byproducts of the cell’s energy metabolism. In neurons, this toxicity can arise when mitochondrial ATP production stalls due to limited ADP. Under these conditions, electrons deviate from the electron transport chain to produce reactive oxygen species, disrupting many cellular processes and challenging cell survival. To mitigate this, neurons may engage in ADP-producing metabolic spikes. I will explore the validity of this hypothesis using computational models that illustrate the implications of synaptic and metabolic spiking, especially in the context of substantia nigra pars compacta dopaminergic neurons and their degeneration in Parkinson's disease.

In behaving rodents, CA1 pyramidal neurons receive spatially-tuned depolarizing synaptic input while traversing a specific location within an environment called its place. Midbrain dopamine neurons participate in reinforcement learning, and bursts of action potentials riding a depolarizing wave of synaptic input signal rewards and reward expectation. Interestingly, slice electrophysiology in vitro shows that both types of cells exhibit a pronounced reduction in firing rate (adaptation) and even cessation of firing during sustained depolarization. We included a five state Markov model of NaV1.6 (for CA1) and NaV1.2 (for dopamine neurons) respectively, in computational models of these two types of neurons. Our simulations suggest that long-term inactivation of this channel is responsible for the adaptation in CA1 pyramidal neurons, in response to triangular depolarizing current ramps. We also show that the differential contribution of slow inactivation in two subpopulations of midbrain dopamine neurons can account for their different dynamic ranges, as assessed by their responses to similar depolarizing ramps. These results suggest long-term inactivation of the sodium channel is a general mechanism for adaptation.

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.

Ever since Bliss and Lømo discovered the phenomenon of long-term potentiation (LTP) in rabbit dentate gyrus in the 1960s, Hebb’s rule—neurons that fire together wire together—gained popularity to explain learning and memory. Accumulating evidence, however, suggests that neural activity is homeostatically regulated. Homeostatic mechanisms are mostly interpreted to stabilize network dynamics. However, recent theoretical work has shown that linking the activity of a neuron to its connectivity within the network provides a robust alternative implementation of Hebb’s rule, although entirely based on negative feedback. In this setting, both natural and artificial stimulation of neurons can robustly trigger network rewiring. We used computational models of plastic networks to simulate the complex temporal dynamics of network rewiring in response to external stimuli. In parallel, we performed optogenetic stimulation experiments in the mouse anterior cingulate cortex (ACC) and subsequently analyzed the temporal profile of morphological changes in the stimulated tissue. Our results suggest that the new theoretical framework combining neural activity homeostasis and structural plasticity provides a consistent explanation of our experimental observations.

When we remember a stimulus we rarely maintain a full fidelity representation of the observed item. Our working memory instead maintains a mixture of the observed feature values and categorical/gist information. I will discuss evidence from computational models supporting a mix of categorical and fine-detail information in working memory. Having established the need for two memory formats in working memory, I will discuss whether categorical and fine-detailed information for a stimulus are represented separately or as a single unified representation. Computational models of these two potential cognitive structures make differing predictions about the pattern of responses in visual working memory recall tests. The present study required participants to remember the orientation of stimuli for later reproduction. The pattern of responses are used to test the competing representational structures and to quantify the relative amount of fine-detailed and categorical information maintained. The effects of set size, encoding time, serial order, and response order on memory precision, categorical information, and guessing rates are also explored. (This is a 60 min talk).

How our senses work both separately and together involves rich computational problems. I will discuss the spatial and representational problems faced by the visual and auditory system, focusing on two issues. 1. How does the brain correct for discrepancies in the visual and auditory spatial reference frames? I will describe our recent discovery of a novel type of otoacoustic emission, the eye movement related eardrum oscillation, or EMREO (Gruters et al, PNAS 2018). 2. How does the brain encode more than one stimulus at a time? I will discuss evidence for neural time-division multiplexing, in which neural activity fluctuates across time to allow representations to encode more than one simultaneous stimulus (Caruso et al, Nat Comm 2018). These findings all emerged from experimentally testing computational models regarding spatial representations and their transformations within and across sensory pathways. Further, they speak to several general problems confronting modern neuroscience such as the hierarchical organization of brain pathways and limits on perceptual/cognitive processing.

Neuromodulators control information processing in cortical microcircuits by regulating the cellular and synaptic physiology of neurons. Computational models and detailed simulations of neocortical microcircuitry offer a unifying framework to analyze the role of neuromodulators on network activity. In the present study, to get a deeper insight in the organization of the cortical neuropil for modeling purposes, we quantify the fiber length per cortical volume and the density of varicosities for catecholaminergic, serotonergic and cholinergic systems using immunocytochemical staining and stereological techniques. The data obtained are integrated into a biologically detailed digital reconstruction of the rodent neocortex (Markram et al, 2015) in order to model the influence of modulatory systems on the activity of the somatosensory cortex neocortical column. Simulations of ascending modulation of network activity in our model predict the effects of increasing levels of neuromodulators on diverse neuron types and synapses and reveal a spectrum of activity states. Low levels of neuromodulation drive microcircuit activity into slow oscillations and network synchrony, whereas high neuromodulator concentrations govern fast oscillations and network asynchrony. The models and simulations thus provide a unifying in silico framework to study the role of neuromodulators in reconfiguring network activity.

I am interested in developing a neurally mechanistic understanding of how primate brains represent the world through its visual system and how such representations enable a remarkable set of intelligent behaviors. In this talk, I will primarily highlight aspects of my current research that focuses on dissecting the brain circuits that support core object recognition behavior (primates’ ability to categorize objects within hundreds of milliseconds) in non-human primates. On the one hand, my work empirically examines how well computational models of the primate ventral visual pathways embed knowledge of the visual brain function (e.g., Bashivan*, Kar*, DiCarlo, Science, 2019). On the other hand, my work has led to various functional and architectural insights that help improve such brain models. For instance, we have exposed the necessity of recurrent computations in primate core object recognition (Kar et al., Nature Neuroscience, 2019), one that is strikingly missing from most feedforward artificial neural network models. Specifically, we have observed that the primate ventral stream requires fast recurrent processing via ventrolateral PFC for robust core object recognition (Kar and DiCarlo, Neuron, 2021). In addition, I have been currently developing various chemogenetic strategies to causally target specific bidirectional neural circuits in the macaque brain during multiple object recognition tasks to further probe their relevance during this behavior. I plan to transform these data and insights into tangible progress in neuroscience via my collaboration with various computational groups and building improved brain models of object recognition. I hope to end the talk with a brief glimpse of some of my planned future work!

Many microorganisms and cells function in complex (non-Newtonian) fluids, which are mixtures of different materials and exhibit both viscous and elastic stresses. For example, mammalian sperm swim through cervical mucus on their journey through the female reproductive tract, and they must penetrate the viscoelastic gel outside the ovum to fertilize. In micro-scale swimming the dynamics emerge from the coupled interactions between the complex rheology of the surrounding media and the passive and active body dynamics of the swimmer. We use computational models of swimmers in viscoelastic fluids to investigate and provide mechanistic explanations for emergent swimming behaviors. I will discuss how flexible filaments (such as flagella) can store energy from a viscoelastic fluid to gain stroke boosts due to fluid elasticity. I will also describe 3D simulations of model organisms such as C. Reinhardtii and mammalian sperm, where we use experimentally measured stroke data to separate naturally coupled stroke and fluid effects. We explore why strokes that are adapted to Newtonian fluid environments might not do well in viscoelastic environments.

Behavioural measurements are often overlooked by computational neuroscientists, who prefer to focus on electrophysiological recordings or neuroimaging data. This attitude is largely due to perceived lack of depth/richness in relation to behavioural datasets. I will show how contemporary psychophysics can deliver extremely rich and highly constraining datasets that naturally interface with computational modelling. More specifically, I will demonstrate how psychophysics can be used to guide/constrain/refine computational models, and how models can be exploited to design/motivate/interpret psychophysical experiments. Examples will span a wide range of topics (from feature detection to natural scene understanding) and methodologies (from cascade models to deep learning architectures).

Genetic mutations of the SCN1A gene, the voltage gated sodium channel NaV1.1, cause well-defined epilepsies, including the severe developmental and epileptic encephalopathy Dravet syndrome and genetic epilepsy with febrile seizures plus (GEFS+), as well as a severe form of migraine with aura, familial hemiplegic migraine (FHM). More recently, they have been identified in an extremely severe early infantile encephalopathy. Functional studies and animal models have contributed to disclose pathological mechanisms, which can be often linked to a straightforward loss- vs gain- of channel function. However, although this simple dichotomy is pertinent and useful, detailed pathological mechanisms in neuronal circuits can be more complex, sometimes because of unexpected homeostatic or pathologic responses. I will compare pathological mechanisms of epilepsy and migraine mutations studied with cellular, animal and computational models, highlighting a novel homeostatic response implemented by CCK-positive GABAergic neurons in a mouse model of Dravet syndrome, which may be boosted in therapeutic approaches.

Pain is a major clinical problem affecting 1 in 5 people in the world. There are unresolved questions that urgently require answers to treat pain effectively, a crucial one being how the feeling of pain arises from brain activity. Computational models of pain consider how the brain processes noxious information and allow mapping neural circuits and networks to cognition and behaviour. To date, they have generally have assumed two largely independent processes: perceptual and/or predictive inference, typically modelled as an approximate Bayesian process, and action control, typically modelled as a reinforcement learning process. However, inference and control are intertwined in complex ways, challenging the clarity of this distinction. I will discuss how they may comprise a parallel hierarchical architecture that combines pain inference, information-seeking, and adaptive value-based control. Finally, I will discuss whether and how these learning processes might contribute to chronic pain.

The idea that behavior results from a dynamic interplay between automatic and controlled processing underlies much of decision science, but has also generated considerable controversy. In this talk, I will highlight behavioral and neural data showing how recently-developed computational models of decision making can be used to shed new light on whether, when, and how decisions result from distinct processes operating at different timescales. Across diverse domains ranging from altruism to risky choice biases and self-regulation, our work suggests that a model of prioritized attentional sampling and evidence accumulation may provide an alternative explanation for many phenomena previously interpreted as supporting dual process models of choice. However, I also show how some features of the model might be taken as support for specific aspects of dual-process models, providing a way to reconcile conflicting accounts and generating new predictions and insights along the way.

Behavior arises from the coordinated activity of numerous distinct brain regions. Modern experimental tools allow access to neural populations brain-wide, yet understanding such large-scale datasets necessitates scalable computational models to extract meaningful features of inter-region communication. In this talk, I will introduce Current-Based Decomposition (CURBD), an approach for inferring multi-region interactions using data-constrained recurrent neural network models. I will first show that CURBD accurately isolates inter-region currents in simulated networks with known dynamics. I will then apply CURBD to understand the brain-wide flow of information leading to behavioral state transitions in larval zebrafish. These examples will establish CURBD as a flexible, scalable framework to infer brain-wide interactions that are inaccessible from experimental measurements alone.

Current computational models of visual salience accurately predict the distribution of fixations on isolated visual stimuli. It is not known, however, whether the global salience of a stimulus, that is its effectiveness in the competition for attention with other stimuli, is a function of the local salience or an independent measure. Further, do task and familiarity with the competing images influence eye movements? In this talk, I will present the analysis of a computational model of the global salience of natural images. We trained a machine learning algorithm to learn the direction of the first saccade of participants who freely observed pairs of images. The pairs balanced the combinations of new and already seen images, as well as task and task-free trials. The coefficients of the model provided a reliable measure of the likelihood of each image to attract the first fixation when seen next to another image, that is their global salience. For example, images of close-up faces and images containing humans were consistently looked first and were assigned higher global salience. Interestingly, we found that global salience cannot be explained by the feature-driven local salience of images, the influence of task and familiarity was rather small and we reproduced the previously reported left-sided bias. This computational model of global salience allows to analyse multiple other aspects of human visual perception of competing stimuli. In the talk, I will also present our latest results from analysing the saccadic reaction time as a function of the global salience of the pair of images.

To organize various experiences in a coherent mental representation, we need to properly estimate the duration and temporal order of different events. Yet, our perception of time is noisy and vulnerable to various illusions. Studying these illusions can elucidate the mechanism by which the brain perceives time. In this talk, I will review a few studies on how the brain perceives duration of events and the temporal order between self-generated motion and sensory feedback. Combined with computational models at different levels, these experiments illustrated that the brain incorporates the prior knowledge of the statistical distribution of the duration of stimuli and the decay of memory when estimating duration of an individual event, and adjusts its perception of temporal order to changes in the statistics of the environment.

Intrinsically disordered proteins (IDPs), lack of a well-defined folded structure, have been recently shown to be critical to forming membrane-less organelles via liquid-liquid phase separation (LLPS). Due to the flexible conformations of IDPs, it could be challenging to investigate IDPs with solely experimental techniques. Computational models can therefore provide complementary views at several aspects, including the fundamental physics underlying LLPS and the sequence determinants contributing to LLPS. In this presentation, I will start with our coarse-grained computational framework that can help generate sequence dependent phase diagrams. The coarse-grained model further led to the development of a polymer model with empirical parameters to quickly predict LLPS of IDPs. At last, I will show our preliminary efforts on addressing molecular interactions within LLPS of IDPs using all-atom explicit-solvent simulations.

Visual processing in the retina has been studied in great detail at all levels such that a comprehensive picture of the retina's cell types and the many neural circuits they form is emerging. However, the currently best performing models of retinal function are black-box CNN models which are agnostic to such biological knowledge. Here, I present two of our recent attempts to develop computational models of processing in the inner retina, which both respect biophysical and anatomical constraints yet provide accurate predictions of retinal activity

Theoretical and computational models of neural systems have been traditionally focused on small neural circuits, given the lack of reliable data on large-scale brain structures. The situation has started to change in recent years, with novel recording technologies and large organized efforts to describe the brain at a larger scale. In this talk, Professor Mejias from the University of Amsterdam will review his recent work on developing anatomically constrained computational models of large-scale cortical networks of monkeys, and how this approach can help to answer important questions in large-scale neuroscience. He will focus on three main aspects: (i) the emergence of functional interactions in different frequency regimes, (ii) the role of balance for efficient large-scale communication, and (iii) new paradigms of brain function, such as working memory, in large-scale networks.