Silvio P. Sabatini

The position is a full-time PhD studentship for a period of 3 years, starting on Nov 1st, 2023. The research project is titled 'Early vision function in silico networks of LIF neurons'. The project aims to develop an 'artificial observer' composed of an active event-based camera feeding a neuromorphic multi-layer network of leaky integrate and fire (LIF) neurons. The system should provide the inference engines for relating visual representations to performance on perceptual judgement tasks. Multiple and varying parameters captured under complex, real-life conditions should be comparatively assessed in silicon and human observers. The research will be conducted at the Bioengineering/PSPC labs of DIBRIS.

Yashar Ahmadian

The postdoc will work on a collaborative project between the labs of Yashar Ahmadian at the Computational and Biological Learning Lab (CBL), and Zoe Kourtzi at the Psychology Department, both at the University of Cambridge. The project investigates the computational principles and circuit mechanisms underlying human visual perceptual learning, particularly the role of adaptive changes in the balance of cortical excitation and inhibition resulting from perceptual learning. The postdoc will be based in CBL, with free access to the Kourtzi lab in the Psychology department.

Prof. Dr. Tobias Rose

The selected candidate will investigate the 'Encoding of Landmark Stability and Stability of Landmark Encoding'. You will study visual landmark encoding at the intersection of hippocampal, thalamic, and cortical inputs to retrosplenial cortex. You will use cutting-edge miniature two-photon Ca2+ imaging, enabling you to longitudinally record activity in defined, large neuronal populations and long-range afferents in freely moving animals. You will carry out rigorous neuronal and behavioral analyses within the confines of automatized closed-loop tasks tailored for visual navigation. This will involve the application of advanced tools for dense behavioral quantification, including multi-angle videography, inertial motion sensing, and egocentric recording with head-mounted cameras for the reconstruction of retinal input. Our aim is to gain a comprehensive understanding of the immediate and sustained multi-area neuronal representation of visual landmarks during unrestricted behavior. We aim to elucidate the mechanisms through which stable visual landmarks are encoded and the processes by which these representations are stabilized to facilitate robust allocentric navigation.

Max Garagnani

The project involves implementing a brain-realistic neurocomputational model able to exhibit the spontaneous emergence of cognitive function from a uniform neural substrate, as a result of unsupervised, biologically realistic learning. Specifically, it will focus on modelling the emergence of unexpected (i.e., non stimulus-driven) action decisions using neo-Hebbian reinforcement learning. The final deliverable will be an artificial brain-like cognitive architecture able to learn to act as humans do when driven by intrinsic motivation and spontaneous, exploratory behaviour.

Vinita Samarasinghe

The position is part of the Collaborative Research Center “Extinction Learning” (SFB 1280) and studies the principles underlying spatial learning and its extinction with reinforcement learning models. A particular focus is the role of episodic-like memory in learning and extinction processes. The research group is highly dynamic and uses diverse computational modeling approaches including biological neural networks, cognitive modeling, and machine learning to investigate learning and memory in humans and animals.

Rainer Stiefelhagen

The Cooperative Graduate School Accessibility through AI-based Assistive Technology (KATE - www.kate.kit.edu) is a new cooperative and interdisciplinary graduate school between the Karlsruhe Institute of Technology (KIT) and the Hochschule Karlsruhe (HKA). The program revolves around investigating state-of-the-art artificial intelligence methods in order to improve the autonomy and participation of persons with special needs. Different dissertation topics ranging from AI-based methods for text, audio, and multimedia document processing, AI methods for interactive training and assistance systems, to investigating the consequences and ethical, legal, social, and societal implications of AI systems for people with disabilities will be offered. The sponsored persons will work on a selected topic scientifically in depth within the framework of their doctorate and will receive an overall view of all relevant topics - including medical causes as well as their effects, the needs of the target groups, AI-based approaches, ethics, technology assessment, and societal aspects - through the exchange within the doctoral college for this purpose.

Lorenzo Fontolan

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.

Boris Gutkin

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 and validate a computational model of cardiac and of a somatosensory cortical circuit dynamics in order to determine the conditions under which interactions between exteroceptive and interoceptive inputs occur and which underlying mechanism (e.g., phase-resetting, gating, phasic arousal,..) best explain experimental data. The postdoctoral fellow will be based at the Group for Neural Theory at LNC2, in Boris Gutkin’s team with strong interactions with Catherine Tallon-Baudry’s team. LNC2 is located in the center of Paris within the Cognitive Science Department at Ecole Normale Supérieure, with numerous opportunities to interact with the Paris scientific community at large, in a stimulating and supportive work environment. Group for Neural Theory provides a rich environment and local community for theoretical neuroscience. Lab life is in English, speaking French is not a requirement. Salary according to experience and French rules. Starting date is first semester 2024.

Geoffrey J Goodhill

An NIH-funded collaboration between David Prober (Caltech), Thai Truong (USC) and Geoff Goodhill (Washington University in St Louis) aims to gain new insight into the neural circuits underlying sleep, through a combination of whole-brain neural recordings in zebrafish and theoretical/computational modeling. A postdoc position is available in the Goodhill lab to contribute to the modeling and computational analysis components. Using novel 2-photon imaging technologies Prober and Truong are recording from the entire larval zebrafish brain at single-neuron resolution continuously for long periods of time, examining neural circuit activity during normal day-night cycles and in response to genetic and pharmacological perturbations. The Goodhill lab is analyzing the resulting huge datasets using a variety of sophisticated computational approaches, and using these results to build new theoretical models that reveal how neural circuits interact to govern sleep.

Professor Geoffrey J Goodhill

The Department of Neuroscience at Washington University School of Medicine is currently recruiting investigators with the passion to create knowledge, pursue bold visions, and challenge canonical thinking as we expand into our new 600,000 sq ft purpose-built neurosciences research building. We are now seeking a tenure-track investigator at the level of Assistant Professor to develop an innovative research program in Theoretical/Computational Neuroscience. The successful candidates will join a thriving theoretical/computational neuroscience community at Washington University, including the new Center for Theoretical and Computational Neuroscience. In addition, the Department also has world-class research strengths in systems, circuits and behavior, cellular and molecular neuroscience using a variety of animal models including worms, flies, zebrafish, rodents and non-human primates. We are particularly interested in outstanding researchers who are both creative and collaborative.

Carl Rasmussen, Bernhard Schölkopf

The University of Cambridge Machine Learning Group and the Max Planck Institute for Intelligent Systems Empirical Inference Department in Tübingen are two of the world’s leading centres for machine learning research. In 2014, we launched a new and exciting initiative whereby a small group of select PhD candidates are jointly supervised at both institutions. The principal supervisors are Carl Rasmussen, Neil Lawrence, Ferenc Huszar, Jose Miguel Hernandez-Lobato, David Krueger, Adrian Weller and Rika Antonova at Cambridge University, and Bernhard Schölkopf and other research group leaders at the Max Planck Institute in Tübingen. This program is specific for candidates whose research interests are well-matched to both the principal supervisors in Cambridge and the MPI for Intelligent Systems in Tuebingen. The overall duration of the PhD will be four years, with roughly three years spent at one location, and one year spent at the other location, including initial coursework at the University of Cambridge. Successful PhDs will be officially granted by the University of Cambridge.

I-Chun Lin

The Gatsby Unit is seeking applications for a postdoctoral training fellowship under Dr Agostina Palmigiano, focused on developing theoretical approaches to investigate the mechanisms underlying sensory, motor, or cognitive computations. Responsibilities include the primary execution of the project, opportunities for co-supervision of students, presentation of results at conferences and seminars, and publication in suitable media. The post is initially funded for 2 years with the possibility of a one-year extension.

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.

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.

Professor Geoffrey J Goodhill

The Department of Neuroscience at Washington University School of Medicine is seeking a tenure-track investigator at the level of Assistant Professor to develop an innovative research program in Theoretical/Computational Neuroscience. The successful candidate will join a thriving theoretical/computational neuroscience community at Washington University, including the new Center for Theoretical and Computational Neuroscience. In addition, the Department also has world-class research strengths in systems, circuits and behavior, cellular and molecular neuroscience using a variety of animal models including worms, flies, zebrafish, rodents and non-human primates. The Department’s focus on fundamental neuroscience, outstanding research support facilities, and the depth, breadth and collegiality of our culture provide an exceptional environment to launch your independent research program.

Where are you Moving? Assessing Precision, Accuracy, and Temporal Dynamics in Multisensory Heading Perception Using Continuous Psychophysics

On finding what you’re (not) looking for: prospects and challenges for AI-driven discovery

Recent high-profile scientific achievements by machine learning (ML) and especially deep learning (DL) systems have reinvigorated interest in ML for automated scientific discovery (eg, Wang et al. 2023). Much of this work is motivated by the thought that DL methods might facilitate the efficient discovery of phenomena, hypotheses, or even models or theories more efficiently than traditional, theory-driven approaches to discovery. This talk considers some of the more specific obstacles to automated, DL-driven discovery in frontier science, focusing on gravitational-wave astrophysics (GWA) as a representative case study. In the first part of the talk, we argue that despite these efforts, prospects for DL-driven discovery in GWA remain uncertain. In the second part, we advocate a shift in focus towards the ways DL can be used to augment or enhance existing discovery methods, and the epistemic virtues and vices associated with these uses. We argue that the primary epistemic virtue of many such uses is to decrease opportunity costs associated with investigating puzzling or anomalous signals, and that the right framework for evaluating these uses comes from philosophical work on pursuitworthiness.

Human Echolocation for Localization and Navigation – Behaviour and Brain Mechanisms

Visual-vestibular cue comparison for perception of environmental stationarity

Note the later time!

How fly neurons compute the direction of visual motion

Detecting the direction of image motion is important for visual navigation, predator avoidance and prey capture, and thus essential for the survival of all animals that have eyes. However, the direction of motion is not explicitly represented at the level of the photoreceptors: it rather needs to be computed by subsequent neural circuits, involving a comparison of the signals from neighboring photoreceptors over time. The exact nature of this process represents a classic example of neural computation and has been a longstanding question in the field. Much progress has been made in recent years in the fruit fly Drosophila melanogaster by genetically targeting individual neuron types to block, activate or record from them. Our results obtained this way demonstrate that the local direction of motion is computed in two parallel ON and OFF pathways. Within each pathway, a retinotopic array of four direction-selective T4 (ON) and T5 (OFF) cells represents the four Cartesian components of local motion vectors (leftward, rightward, upward, downward). Since none of the presynaptic neurons is directionally selective, direction selectivity first emerges within T4 and T5 cells. Our present research focuses on the cellular and biophysical mechanisms by which the direction of image motion is computed in these neurons.

The physics of sentience

The balanced brain: two-photon microscopy of inhibitory synapse formation

Coordination between excitatory and inhibitory synapses (providing positive and negative signals respectively) is required to ensure proper information processing in the brain. Many brain disorders, especially neurodevelopental disorders, are rooted in a specific disturbance of this coordination. In my research group we use a combination of two-photon microscopy and electrophisiology to examine how inhibitory synapses are fromed and how this formation is coordinated with nearby excitatroy synapses.

Self-perception: mechanosensation and beyond

Brain-organ communications play a crucial role in maintaining the body's physiological and psychological homeostasis, and are controlled by complex neural and hormonal systems, including the internal mechanosensory organs. However, the progress has been slow due to technical hurdles: the sensory neurons are deeply buried inside the body and are not readily accessible for direct observation, the projection patterns from different organs or body parts are complex rather than converging into dedicate brain regions, the coding principle cannot be directly adapted from that learned from conventional sensory pathways. Our lab apply the pipeline of "biophysics of receptors-cell biology of neurons-functionality of neural circuits-animal behaviors" to explore the molecular and neural mechanisms of self-perception. In the lab, we mainly focus on the following three questions: 1, The molecular and cellular basis for proprioception and interoception. 2, The circuit mechanisms of sensory coding and integration of internal and external information. 3, The function of interoception in regulating behavior homeostasis.

A Better Method to Quantify Perceptual Thresholds : Parameter-free, Model-free, Adaptive procedures

The ‘quantification’ of perception is arguably both one of the most important and most difficult aspects of perception study. This is particularly true in visual perception, in which the evaluation of the perceptual threshold is a pillar of the experimental process. The choice of the correct adaptive psychometric procedure, as well as the selection of the proper parameters, is a difficult but key aspect of the experimental protocol. For instance, Bayesian methods such as QUEST, require the a priori choice of a family of functions (e.g. Gaussian), which is rarely known before the experiment, as well as the specification of multiple parameters. Importantly, the choice of an ill-fitted function or parameters will induce costly mistakes and errors in the experimental process. In this talk we discuss the existing methods and introduce a new adaptive procedure to solve this problem, named, ZOOM (Zooming Optimistic Optimization of Models), based on recent advances in optimization and statistical learning. Compared to existing approaches, ZOOM is completely parameter free and model-free, i.e. can be applied on any arbitrary psychometric problem. Moreover, ZOOM parameters are self-tuned, thus do not need to be manually chosen using heuristics (eg. step size in the Staircase method), preventing further errors. Finally, ZOOM is based on state-of-the-art optimization theory, providing strong mathematical guarantees that are missing from many of its alternatives, while being the most accurate and robust in real life conditions. In our experiments and simulations, ZOOM was found to be significantly better than its alternative, in particular for difficult psychometric functions or when the parameters when not properly chosen. ZOOM is open source, and its implementation is freely available on the web. Given these advantages and its ease of use, we argue that ZOOM can improve the process of many psychophysics experiments.

Setting network states via the dynamics of action potential generation

To understand neural computation and the dynamics in the brain, we usually focus on the connectivity among neurons. In contrast, the properties of single neurons are often thought to be negligible, at least as far as the activity of networks is concerned. In this talk, I will contradict this notion and demonstrate how the biophysics of action-potential generation can have a decisive impact on network behaviour. Our recent theoretical work shows that, among regularly firing neurons, the somewhat unattended homoclinic type (characterized by a spike onset via a saddle homoclinic orbit bifurcation) particularly stands out: First, spikes of this type foster specific network states - synchronization in inhibitory and splayed-out/frustrated states in excitatory networks. Second, homoclinic spikes can easily be induced by changes in a variety of physiological parameters (like temperature, extracellular potassium, or dendritic morphology). As a consequence, such parameter changes can even induce switches in network states, solely based on a modification of cellular voltage dynamics. I will provide first experimental evidence and discuss functional consequences of homoclinic spikes for the design of efficient pattern-generating motor circuits in insects as well as for mammalian pathologies like febrile seizures. Our analysis predicts an interesting role for homoclinic action potentials as an integral part of brain dynamics in both health and disease.

A model of colour appearance based on efficient coding of natural images

An object’s colour, brightness and pattern are all influenced by its surroundings, and a number of visual phenomena and “illusions” have been discovered that highlight these often dramatic effects. Explanations for these phenomena range from low-level neural mechanisms to high-level processes that incorporate contextual information or prior knowledge. Importantly, few of these phenomena can currently be accounted for when measuring an object’s perceived colour. Here we ask to what extent colour appearance is predicted by a model based on the principle of coding efficiency. The model assumes that the image is encoded by noisy spatio-chromatic filters at one octave separations, which are either circularly symmetrical or oriented. Each spatial band’s lower threshold is set by the contrast sensitivity function, and the dynamic range of the band is a fixed multiple of this threshold, above which the response saturates. Filter outputs are then reweighted to give equal power in each channel for natural images. We demonstrate that the model fits human behavioural performance in psychophysics experiments, and also primate retinal ganglion responses. Next we systematically test the model’s ability to qualitatively predict over 35 brightness and colour phenomena, with almost complete success. This implies that contrary to high-level processing explanations, much of colour appearance is potentially attributable to simple mechanisms evolved for efficient coding of natural images, and is a basis for modelling the vision of humans and other animals.

An investigation of perceptual biases in spiking recurrent neural networks trained to discriminate time intervals

Magnitude estimation and stimulus discrimination tasks are affected by perceptual biases that cause the stimulus parameter to be perceived as shifted toward the mean of its distribution. These biases have been extensively studied in psychophysics and, more recently and to a lesser extent, with neural activity recordings. New computational techniques allow us to train spiking recurrent neural networks on the tasks used in the experiments. This provides us with another valuable tool with which to investigate the network mechanisms responsible for the biases and how behavior could be modeled. As an example, in this talk I will consider networks trained to discriminate the durations of temporal intervals. The trained networks presented the contraction bias, even though they were trained with a stimulus sequence without temporal correlations. The neural activity during the delay period carried information about the stimuli of the current trial and previous trials, this being one of the mechanisms that originated the contraction bias. The population activity described trajectories in a low-dimensional space and their relative locations depended on the prior distribution. The results can be modeled as an ideal observer that during the delay period sees a combination of the current and the previous stimuli. Finally, I will describe how the neural trajectories in state space encode an estimate of the interval duration. The approach could be applied to other cognitive tasks.

The Standard Model of the Retina

The science of the retina has reached an interesting stage of completion. There exists now a consensus standard model of this neural system - at least in the minds of many researchers - that serves as a baseline against which to evaluate new claims. The standard model links phenomena from molecular biophysics, cell biology, neuroanatomy, synaptic physiology, circuit function, and visual psychophysics. It is further supported by a normative theory explaining what the purpose is of processing visual information this way. Most new reports of retinal phenomena fit squarely within the standard model, and major revisions seem increasingly unlikely. Given that our understanding of other brain circuits with comparable complexity is much more rudimentary, it is worth considering an example of what success looks like. In this talk I will summarize what I think are the ingredients that led to this mature understanding of the retina. Equally important, a number of practices and concepts that are currently en vogue in neuroscience were not needed or indeed counterproductive. I look forward to debating how these lessons might extend to other areas of brain research.

Emergence of homochirality in large molecular systems

The question of the origin of homochirality of living matter, or the dominance of one handedness for all molecules of life across the entire biosphere, is a long-standing puzzle in the research on the Origin of Life. In the fifties, Frank proposed a mechanism to explain homochirality based on the properties of a simple autocatalytic network containing only a few chemical species. Following this work, chemists struggled to find experimental realizations of this model, possibly due to a lack of proper methods to identify autocatalysis [1]. In any case, a model based on a few chemical species seems rather limited, because prebiotic earth is likely to have consisted of complex ‘soups’ of chemicals. To include this aspect of the problem, we recently proposed a mechanism based on certain features of large out-of-equilibrium chemical networks [2]. We showed that a phase transition towards an homochiral state is likely to occur as the number of chiral species in the system becomes large or as the amount of free energy injected into the system increases. Through an analysis of large chemical databases, we showed that there is no need for very large molecules for chiral species to dominate over achiral ones; it already happens when molecules contain about 10 heavy atoms. We also analyzed the various conventions used to measure chirality and discussed the relative chiral signs adopted by different groups of molecules [3]. We then proposed a generalization of Frank’s model for large chemical networks, which we characterized using random matrix theory. This analysis includes sparse networks, suggesting that the emergence of homochirality is a robust and generic transition. References: [1] A. Blokhuis, D. Lacoste, and P. Nghe, PNAS (2020), 117, 25230. [2] G. Laurent, D. Lacoste, and P. Gaspard, PNAS (2021) 118 (3) e2012741118. [3] G. Laurent, D. Lacoste, and P. Gaspard, Proc. R. Soc. A 478:20210590 (2022).

Homeostatic Plasticity in Health and Disease

Dr. Davis will present a summary regarding the identification and characterization of mechanisms of homeostatic plasticity as they relate to the control of synaptic transmission. He will then provide evidence of translation to the mammalian neuromuscular junction and central synapses, and provide tangible links to the etiology of neurological disease.

Retinal neurogenesis and lamination: What to become, where to become it and how to move from there!

The vertebrate retina is an important outpost of the central nervous system, responsible for the perception and transmission of visual information. It consists of five different types of neurons that reproducibly laminate into three layers, a process of crucial importance for the organ’s function. Unsurprisingly, impaired fate decisions as well as impaired neuronal migrations and lamination lead to impaired retinal function. However, how processes are coordinated at the cellular and tissue level and how variable or robust retinal formation is, is currently still underexplored. In my lab, we aim to shed light on these questions from different angles, studying on the one hand differentiation phenomena and their variability and on the other hand the downstream migration and lamination phenomena. We use zebrafish as our main model system due to its excellent possibilities for live imaging and quantitative developmental biology. More recently we also started to use human retinal organoids as a comparative system. We further employ cross disciplinary approaches to address these issues combining work of cell and developmental biology, biomechanics, theory and computer science. Together, this allows us to integrate cell with tissue-wide phenomena and generate an appreciation of the reproducibility and variability of events.

Attention to visual motion: shaping sensation into perception

Evolution has endowed primates, including humans, with a powerful visual system, seemingly providing us with a detailed perception of our surroundings. But in reality the underlying process is one of active filtering, enhancement and reshaping. For visual motion perception, the dorsal pathway in primate visual cortex and in particular area MT/V5 is considered to be of critical importance. Combining physiological and psychophysical approaches we have used the processing and perception of visual motion and area MT/V5 as a model for the interaction of sensory (bottom-up) signals with cognitive (top-down) modulatory influences that characterizes visual perception. Our findings document how this interaction enables visual cortex to actively generate a neural representation of the environment that combines the high-performance sensory periphery with selective modulatory influences for producing an “integrated saliency map’ of the environment.

Towards a Theory of Microbial Ecosystems

A major unresolved question in microbiome research is whether the complex ecological patterns observed in surveys of natural communities can be explained and predicted by fundamental, quantitative principles. Bridging theory and experiment is hampered by the multiplicity of ecological processes that simultaneously affect community assembly and a lack of theoretical tools for modeling diverse ecosystems. Here, I will present a simple ecological model of microbial communities that reproduces large-scale ecological patterns observed across multiple natural and experimental settings including compositional gradients, clustering by environment, diversity/harshness correlations, and nestedness. Surprisingly, our model works despite having a “random metabolisms” and “random consumer preferences”. This raises the natural of question of why random ecosystems can describe real-world experimental data. In the second, more theoretical part of the talk, I will answer this question by showing that when a community becomes diverse enough, it will always self-organize into a stable state whose properties are well captured by a “typical random ecosystems”.

Individual differences in visual (mis)perception: a multivariate statistical approach

Common factors are omnipresent in everyday life, e.g., it is widely held that there is a common factor g for intelligence. In vision, however, there seems to be a multitude of specific factors rather than a strong and unique common factor. In my thesis, I first examined the multidimensionality of the structure underlying visual illusions. To this aim, the susceptibility to various visual illusions was measured. In addition, subjects were tested with variants of the same illusion, which differed in spatial features, luminance, orientation, or contextual conditions. Only weak correlations were observed between the susceptibility to different visual illusions. An individual showing a strong susceptibility to one visual illusion does not necessarily show a strong susceptibility to other visual illusions, suggesting that the structure underlying visual illusions is multifactorial. In contrast, there were strong correlations between the susceptibility to variants of the same illusion. Hence, factors seem to be illusion-specific but not feature-specific. Second, I investigated whether a strong visual factor emerges in healthy elderly and patients with schizophrenia, which may be expected from the general decline in perceptual abilities usually reported in these two populations compared to healthy young adults. Similarly, a strong visual factor may emerge in action video gamers, who often show enhanced perceptual performance compared to non-video gamers. Hence, healthy elderly, patients with schizophrenia, and action video gamers were tested with a battery of visual tasks, such as a contrast detection and orientation discrimination task. As in control groups, between-task correlations were weak in general, which argues against the emergence of a strong common factor for vision in these populations. While similar tasks are usually assumed to rely on similar neural mechanisms, the performances in different visual tasks were only weakly related to each other, i.e., performance does not generalize across visual tasks. These results highlight the relevance of an individual differences approach to unravel the multidimensionality of the visual structure.

How much depth do you see? It depends…

NMC4 Short Talk: Sensory intermixing of mental imagery and perception

Several lines of research have demonstrated that internally generated sensory experience - such as during memory, dreaming and mental imagery - activates similar neural representations as externally triggered perception. This overlap raises a fundamental challenge: how is the brain able to keep apart signals reflecting imagination and reality? In a series of online psychophysics experiments combined with computational modelling, we investigated to what extent imagination and perception are confused when the same content is simultaneously imagined and perceived. We found that simultaneous congruent mental imagery consistently led to an increase in perceptual presence responses, and that congruent perceptual presence responses were in turn associated with a more vivid imagery experience. Our findings can be best explained by a simple signal detection model in which imagined and perceived signals are added together. Perceptual reality monitoring can then easily be implemented by evaluating whether this intermixed signal is strong or vivid enough to pass a ‘reality threshold’. Our model suggests that, in contrast to self-generated sensory changes during movement, our brain does not discount self-generated sensory signals during mental imagery. This has profound implications for our understanding of reality monitoring and perception in general.

Spatial summation for motion detection

Retinoblastoma: Canadian global leadership

Nonequilibrium self-assembly and time-irreversibility in living systems

Far-from-equilibrium processes constantly dissipate energy while converting a free-energy source to another form of energy. Living systems, for example, rely on an orchestra of molecular motors that consume chemical fuel to produce mechanical work. In this talk, I will describe two features of life, namely, time-irreversibility, and nonequilibrium self-assembly. Time irreversibility is the hallmark of nonequilibrium dissipative processes. Detecting dissipation is essential for our basic understanding of the underlying physical mechanism, however, it remains a challenge in the absence of observable directed motion, flows, or fluxes. Additional difficulty arises in complex systems where many internal degrees of freedom are inaccessible to an external observer. I will introduce a novel approach to detect time irreversibility and estimate the entropy production from time-series measurements, even in the absence of observable currents. This method can be implemented in scenarios where only partial information is available and thus provides a new tool for studying nonequilibrium phenomena. Further, I will explore the added benefits achieved by nonequilibrium driving for self-assembly, identify distinctive collective phenomena that emerge in a nonequilibrium self-assembly setting, and demonstrate the interplay between the assembly speed, kinetic stability, and relative population of dynamical attractors.

Ultrasound imaging in neuroscience

Physics and the Origin of Life: From Chirality to Membranes to Information

Diverse physical processes played important roles in the origin of life. I will review the origin of molecular homochirality, the growth of protocell membranes, and potential roles for liquid-liquid phase separation. I will then discuss the concept of functional information and its quantitative relationship with molecular function.

Neural dynamics of probabilistic information processing in humans and recurrent neural networks

In nature, sensory inputs are often highly structured, and statistical regularities of these signals can be extracted to form expectation about future sensorimotor associations, thereby optimizing behavior. One of the fundamental questions in neuroscience concerns the neural computations that underlie these probabilistic sensorimotor processing. Through a recurrent neural network (RNN) model and human psychophysics and electroencephalography (EEG), the present study investigates circuit mechanisms for processing probabilistic structures of sensory signals to guide behavior. We first constructed and trained a biophysically constrained RNN model to perform a series of probabilistic decision-making tasks similar to paradigms designed for humans. Specifically, the training environment was probabilistic such that one stimulus was more probable than the others. We show that both humans and the RNN model successfully extract information about stimulus probability and integrate this knowledge into their decisions and task strategy in a new environment. Specifically, performance of both humans and the RNN model varied with the degree to which the stimulus probability of the new environment matched the formed expectation. In both cases, this expectation effect was more prominent when the strength of sensory evidence was low, suggesting that like humans, our RNNs placed more emphasis on prior expectation (top-down signals) when the available sensory information (bottom-up signals) was limited, thereby optimizing task performance. Finally, by dissecting the trained RNN model, we demonstrate how competitive inhibition and recurrent excitation form the basis for neural circuitry optimized to perform probabilistic information processing.

Toward Naturalistic Paradigms of Agency

Voluntary control of behavior requires the ability to dynamically integrate internal states and external evidence to achieve one’s goals. However, neuroscientific studies of intentional action and critical philosophical commentary of that research have taken a rather narrow turn in recent years, focussing on the neural precursors of spontaneous simple actions as potential realizers of intentions. In this session, we show how the debate can benefit from incorporating other types of experimental approaches, focussing on agency in dynamic contexts.

Active dynamics and tunable mechanics of actin-microtubule composites

Controlling flows and defects in biomolecular active liquid crystals

Analyzing Retinal Disease Using Electron Microscopic Connectomics

John DowlingJohn E. Dowling received his AB and PhD from Harvard University. He taught in the Biology Department at Harvard from 1961 to 1964, first as an Instructor, then as assistant professor. In 1964 he moved to Johns Hopkins University, where he held an appointment as associate professor of Ophthalmology and Biophysics. He returned to Harvard as professor of Biology in 1971, was the Maria Moors Cabot Professor of Natural Sciences from 1971-2001, Harvard College professor from 1999-2004 and is presently the Gordon and Llura Gund Professor of Neurosciences. Dowling was chairman of the Biology Department at Harvard from 1975 to 1978 and served as associate dean of the faculty of Arts and Sciences from 1980 to 1984. He was Master of Leverett House at Harvard from 1981-1998 and currently serves as president of the Corporation of The Marine Biological Laboratory in Woods Hole. He is a Fellow of the American Academy of Arts and Sciences, a member of the National Academy of Sciences and a member of the American Philosophical Society. Awards that Dowling received include the Friedenwald Medal from the Association of Research in Ophthalmology and Vision in 1970, the Annual Award of the New England Ophthalmological Society in 1979, the Retinal Research Foundation Award for Retinal Research in 1981, an Alcon Vision Research Recognition Award in 1986, a National Eye Institute's MERIT award in 1987, the Von Sallman Prize in 1992, The Helen Keller Prize for Vision Research in 2000 and the Llura Ligget Gund Award for Lifetime Achievement and Recognition of Contribution to the Foundation Fighting Blindness in 2001. He was granted an honorary MD degree by the University of Lund (Sweden) in 1982 and an honorary Doctor of Laws degree from Dalhousie University (Canada) in 2012. Dowling's research interests have focused on the vertebrate retina as a model piece of the brain. He and his collaborators have long been interested in the functional organization of the retina, studying its synaptic organization, the electrical responses of the retinal neurons, and the mechanisms underlying neurotransmission and neuromodulation in the retina. Dowling became interested in zebrafish as a system in which one could explore the development and genetics of the vertebrate retina about 20 years ago. Part of his research team has focused on retinal development in zebrafish and the role of retinoic acid in early eye and photoreceptor development. A second group has developed behavioral tests to isolate mutations, both recessive and dominant, specific to the visual system.

Locomotion of Helicobacter pylori: Cell geometry and active confinement

How Chromosomes align on the spindle

Interpreting the Mechanisms and Meaning of Sensorimotor Beta Rhythms with the Human Neocortical Neurosolver (HNN) Neural Modeling Software

Electro- and magneto-encephalography (EEG/MEG) are the leading methods to non-invasively record human neural dynamics with millisecond temporal resolution. However, it can be extremely difficult to infer the underlying cellular and circuit level origins of these macro-scale signals without simultaneous invasive recordings. This limits the translation of E/MEG into novel principles of information processing, or into new treatment modalities for neural pathologies. To address this need, we developed the Human Neocortical Neurosolver (HNN: https://hnn.brown/edu ), a new user-friendly neural modeling tool designed to help researchers and clinicians interpret human imaging data. A unique feature of HNN’s model is that it accounts for the biophysics generating the primary electric currents underlying such data, so simulation results are directly comparable to source localized data. HNN is being constructed with workflows of use to study some of the most commonly measured E/MEG signals including event related potentials, and low frequency brain rhythms. In this talk, I will give an overview of this new tool and describe an application to study the origin and meaning of 15-29Hz beta frequency oscillations, known to be important for sensory and motor function. Our data showed that in primary somatosensory cortex these oscillations emerge as transient high power ‘events’. Functionally relevant differences in averaged power reflected a difference in the number of high-power beta events per trial (“rate”), as opposed to changes in event amplitude or duration. These findings were consistent across detection and attention tasks in human MEG, and in local field potentials from mice performing a detection task. HNN modeling led to a new theory on the circuit origin of such beta events and suggested beta causally impacts perception through layer specific recruitment of cortical inhibition, with support from invasive recordings in animal models and high-resolution MEG in humans. In total, HNN provides an unpresented biophysically principled tool to link mechanism to meaning of human E/MEG signals.

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