Self
self
Choice between methamphetamine and food is modulated by reinforcement interval and central drug metabolism
The Brain Prize winners' webinar
This webinar brings together three leaders in theoretical and computational neuroscience—Larry Abbott, Haim Sompolinsky, and Terry Sejnowski—to discuss how neural circuits generate fundamental aspects of the mind. Abbott illustrates mechanisms in electric fish that differentiate self-generated electric signals from external sensory cues, showing how predictive plasticity and two-stage signal cancellation mediate a sense of self. Sompolinsky explores attractor networks, revealing how discrete and continuous attractors can stabilize activity patterns, enable working memory, and incorporate chaotic dynamics underlying spontaneous behaviors. He further highlights the concept of object manifolds in high-level sensory representations and raises open questions on integrating connectomics with theoretical frameworks. Sejnowski bridges these motifs with modern artificial intelligence, demonstrating how large-scale neural networks capture language structures through distributed representations that parallel biological coding. Together, their presentations emphasize the synergy between empirical data, computational modeling, and connectomics in explaining the neural basis of cognition—offering insights into perception, memory, language, and the emergence of mind-like processes.
Sensory cognition
This webinar features presentations from SueYeon Chung (New York University) and Srinivas Turaga (HHMI Janelia Research Campus) on theoretical and computational approaches to sensory cognition. Chung introduced a “neural manifold” framework to capture how high-dimensional neural activity is structured into meaningful manifolds reflecting object representations. She demonstrated that manifold geometry—shaped by radius, dimensionality, and correlations—directly governs a population’s capacity for classifying or separating stimuli under nuisance variations. Applying these ideas as a data analysis tool, she showed how measuring object-manifold geometry can explain transformations along the ventral visual stream and suggested that manifold principles also yield better self-supervised neural network models resembling mammalian visual cortex. Turaga described simulating the entire fruit fly visual pathway using its connectome, modeling 64 key cell types in the optic lobe. His team’s systematic approach—combining sparse connectivity from electron microscopy with simple dynamical parameters—recapitulated known motion-selective responses and produced novel testable predictions. Together, these studies underscore the power of combining connectomic detail, task objectives, and geometric theories to unravel neural computations bridging from stimuli to cognitive functions.
How do we sleep?
There is no consensus on if sleep is for the brain, body or both. But the difference in how we feel following disrupted sleep or having a good night of continuous sleep is striking. Understanding how and why we sleep will likely give insights into many aspects of health. In this talk I will outline our recent work on how the prefrontal cortex can signal to the hypothalamus to regulate sleep preparatory behaviours and sleep itself, and how other brain regions, including the ventral tegmental area, respond to psychosocial stress to induce beneficial sleep. I will also outline our work on examining the function of the glymphatic system, and whether clearance of molecules from the brain is enhanced during sleep or wakefulness.
Bernstein Conference 2024
Each year the Bernstein Network invites the international computational neuroscience community to the annual Bernstein Conference for intensive scientific exchange:contentReference[oaicite:8]{index=8}. Bernstein Conference 2024, held in Frankfurt am Main, featured discussions, keynote lectures, and poster sessions, and has established itself as one of the most renowned conferences worldwide in this field:contentReference[oaicite:9]{index=9}:contentReference[oaicite:10]{index=10}.
Prosocial Learning and Motivation across the Lifespan
2024 BACN Early-Career Prize Lecture Many of our decisions affect other people. Our choices can decelerate climate change, stop the spread of infectious diseases, and directly help or harm others. Prosocial behaviours – decisions that help others – could contribute to reducing the impact of these challenges, yet their computational and neural mechanisms remain poorly understood. I will present recent work that examines prosocial motivation, how willing we are to incur costs to help others, prosocial learning, how we learn from the outcomes of our choices when they affect other people, and prosocial preferences, our self-reports of helping others. Throughout the talk, I will outline the possible computational and neural bases of these behaviours, and how they may differ from young adulthood to old age.
The multi-phase plasticity supporting winner effect
Aggression is an innate behavior across animal species. It is essential for competing for food, defending territory, securing mates, and protecting families and oneself. Since initiating an attack requires no explicit learning, the neural circuit underlying aggression is believed to be genetically and developmentally hardwired. Despite being innate, aggression is highly plastic. It is influenced by a wide variety of experiences, particularly winning and losing previous encounters. Numerous studies have shown that winning leads to an increased tendency to fight while losing leads to flight in future encounters. In the talk, I will present our recent findings regarding the neural mechanisms underlying the behavioral changes caused by winning.
Learning representations of specifics and generalities over time
There is a fundamental tension between storing discrete traces of individual experiences, which allows recall of particular moments in our past without interference, and extracting regularities across these experiences, which supports generalization and prediction in similar situations in the future. One influential proposal for how the brain resolves this tension is that it separates the processes anatomically into Complementary Learning Systems, with the hippocampus rapidly encoding individual episodes and the neocortex slowly extracting regularities over days, months, and years. But this does not explain our ability to learn and generalize from new regularities in our environment quickly, often within minutes. We have put forward a neural network model of the hippocampus that suggests that the hippocampus itself may contain complementary learning systems, with one pathway specializing in the rapid learning of regularities and a separate pathway handling the region’s classic episodic memory functions. This proposal has broad implications for how we learn and represent novel information of specific and generalized types, which we test across statistical learning, inference, and category learning paradigms. We also explore how this system interacts with slower-learning neocortical memory systems, with empirical and modeling investigations into how the hippocampus shapes neocortical representations during sleep. Together, the work helps us understand how structured information in our environment is initially encoded and how it then transforms over time.
A Comprehensive Overview of Large Language Models
Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond. This success of LLMs has led to a large influx of research contributions in this direction. These works encompass diverse topics such as architectural innovations, better training strategies, context length improvements, fine-tuning, multi-modal LLMs, robotics, datasets, benchmarking, efficiency, and more. With the rapid development of techniques and regular breakthroughs in LLM research, it has become considerably challenging to perceive the bigger picture of the advances in this direction. Considering the rapidly emerging plethora of literature on LLMs, it is imperative that the research community is able to benefit from a concise yet comprehensive overview of the recent developments in this field. This article provides an overview of the existing literature on a broad range of LLM-related concepts. Our self-contained comprehensive overview of LLMs discusses relevant background concepts along with covering the advanced topics at the frontier of research in LLMs. This review article is intended to not only provide a systematic survey but also a quick comprehensive reference for the researchers and practitioners to draw insights from extensive informative summaries of the existing works to advance the LLM research.
Brain-heart interactions at the edges of consciousness
Various clinical cases have provided evidence linking cardiovascular, neurological, and psychiatric disorders to changes in the brain-heart interaction. Our recent experimental evidence on patients with disorders of consciousness revealed that observing brain-heart interactions helps to detect residual consciousness, even in patients with absence of behavioral signs of consciousness. Those findings support hypotheses suggesting that visceral activity is involved in the neurobiology of consciousness and sum to the existing evidence in healthy participants in which the neural responses to heartbeats reveal perceptual and self-consciousness. Furthermore, the presence of non-linear, complex, and bidirectional communication between brain and heartbeat dynamics can provide further insights into the physiological state of the patient following severe brain injury. These developments on methodologies to analyze brain-heart interactions open new avenues for understanding neural functioning at a large-scale level, uncovering that peripheral bodily activity can influence brain homeostatic processes, cognition, and behavior.
Are integrative, multidisciplinary, and pragmatic models possible? The #PsychMapping experience
This presentation delves into the necessity for simplified models in the field of psychological sciences to cater to a diverse audience of practitioners. We introduce the #PsychMapping model, evaluate its merits and limitations, and discuss its place in contemporary scientific culture. The #PsychMapping model is the product of an extensive literature review, initially within the realm of sport and exercise psychology and subsequently encompassing a broader spectrum of psychological sciences. This model synthesizes the progress made in psychological sciences by categorizing variables into a framework that distinguishes between traits (e.g., body structure and personality) and states (e.g., heart rate and emotions). Furthermore, it delineates internal traits and states from the externalized self, which encompasses behaviour and performance. All three components—traits, states, and the externalized self—are in a continuous interplay with external physical, social, and circumstantial factors. Two core processes elucidate the interactions among these four primary clusters: external perception, encompassing the mechanism through which external stimuli transition into internal events, and self-regulation, which empowers individuals to become autonomous agents capable of exerting control over themselves and their actions. While the model inherently oversimplifies intricate processes, the central question remains: does its pragmatic utility outweigh its limitations, and can it serve as a valuable tool for comprehending human behaviour?
Of glia and macrophages, signaling hubs in development and homeostasis
We are interested in the biology of macrophages, which represent the first line of defense against pathogens. In Drosophila, the embryonic hemocytes arise from the mesoderm whereas glial cells arise from multipotent precursors in the neurogenic region. These cell types represent, respectively, the macrophages located outside and within the nervous system (similar to vertebrate microglia). Thus, despite their different origin, hemocytes and glia display common functions. In addition, both cell types express the Glide/Gcm transcription factor, which plays an evolutionarily conserved role as an anti-inflammatory factor. Moreover, embryonic hemocytes play an evolutionarily conserved and fundamental role in development. The ability to migrate and to contact different tissues/organs most likely allow macrophages to function as signaling hubs. The function of macrophages beyond the recognition of the non-self calls for revisiting the biology of these heterogeneous and plastic cells in physiological and pathological conditions across evolution.
The Role of Spatial and Contextual Relations of real world objects in Interval Timing
In the real world, object arrangement follows a number of rules. Some of the rules pertain to the spatial relations between objects and scenes (i.e., syntactic rules) and others about the contextual relations (i.e., semantic rules). Research has shown that violation of semantic rules influences interval timing with the duration of scenes containing such violations to be overestimated as compared to scenes with no violations. However, no study has yet investigated whether both semantic and syntactic violations can affect timing in the same way. Furthermore, it is unclear whether the effect of scene violations on timing is due to attentional or other cognitive accounts. Using an oddball paradigm and real-world scenes with or without semantic and syntactic violations, we conducted two experiments on whether time dilation will be obtained in the presence of any type of scene violation and the role of attention in any such effect. Our results from Experiment 1 showed that time dilation indeed occurred in the presence of syntactic violations, while time compression was observed for semantic violations. In Experiment 2, we further investigated whether these estimations were driven by attentional accounts, by utilizing a contrast manipulation of the target objects. The results showed that an increased contrast led to duration overestimation for both semantic and syntactic oddballs. Together, our results indicate that scene violations differentially affect timing due to violation processing differences and, moreover, their effect on timing seems to be sensitive to attentional manipulations such as target contrast.
Incorporating visual evidence and counter-evidence to estimate self-movement
Are integrative, multidisciplinary, and pragmatic models possible? The #PsychMapping experience
This presentation delves into the necessity for simplified models in the field of psychological sciences to cater to a diverse audience of practitioners. We introduce the #PsychMapping model, evaluate its merits and limitations, and discuss its place in contemporary scientific culture. The #PsychMapping model is the product of an extensive literature review, initially within the realm of sport and exercise psychology and subsequently encompassing a broader spectrum of psychological sciences. This model synthesizes the progress made in psychological sciences by categorizing variables into a framework that distinguishes between traits (e.g., body structure and personality) and states (e.g., heart rate and emotions). Furthermore, it delineates internal traits and states from the externalized self, which encompasses behaviour and performance. All three components—traits, states, and the externalized self—are in a continuous interplay with external physical, social, and circumstantial factors. Two core processes elucidate the interactions among these four primary clusters: external perception, encompassing the mechanism through which external stimuli transition into internal events, and self-regulation, which empowers individuals to become autonomous agents capable of exerting control over themselves and their actions. While the model inherently oversimplifies intricate processes, the central question remains: does its pragmatic utility outweigh its limitations, and can it serve as a valuable tool for comprehending human behaviour?
Characterising Representations of Goal Obstructiveness and Uncertainty Across Behavior, Physiology, and Brain Activity Through a Video Game Paradigm
The nature of emotions and their neural underpinnings remain debated. Appraisal theories such as the component process model propose that the perception and evaluation of events (appraisal) is the key to eliciting the range of emotions we experience. Here we study whether the framework of appraisal theories provides a clearer account for the differentiation of emotional episodes and their functional organisation in the brain. We developed a stealth game to manipulate appraisals in a systematic yet immersive way. The interactive nature of video games heightens self-relevance through the experience of goal-directed action or reaction, evoking strong emotions. We show that our manipulations led to changes in behaviour, physiology and brain activations.
Modeling the Navigational Circuitry of the Fly
Navigation requires orienting oneself relative to landmarks in the environment, evaluating relevant sensory data, remembering goals, and convert all this information into motor commands that direct locomotion. I will present models, highly constrained by connectomic, physiological and behavioral data, for how these functions are accomplished in the fly brain.
Neural Mechanisms of Subsecond Temporal Encoding in Primary Visual Cortex
Subsecond timing underlies nearly all sensory and motor activities across species and is critical to survival. While subsecond temporal information has been found across cortical and subcortical regions, it is unclear if it is generated locally and intrinsically or if it is a read out of a centralized clock-like mechanism. Indeed, mechanisms of subsecond timing at the circuit level are largely obscure. Primary sensory areas are well-suited to address these question as they have early access to sensory information and provide minimal processing to it: if temporal information is found in these regions, it is likely to be generated intrinsically and locally. We test this hypothesis by training mice to perform an audio-visual temporal pattern sensory discrimination task as we use 2-photon calcium imaging, a technique capable of recording population level activity at single cell resolution, to record activity in primary visual cortex (V1). We have found significant changes in network dynamics through mice’s learning of the task from naive to middle to expert levels. Changes in network dynamics and behavioral performance are well accounted for by an intrinsic model of timing in which the trajectory of q network through high dimensional state space represents temporal sensory information. Conversely, while we found evidence of other temporal encoding models, such as oscillatory activity, we did not find that they accounted for increased performance but were in fact correlated with the intrinsic model itself. These results provide insight into how subsecond temporal information is encoded mechanistically at the circuit level.
Trends in NeuroAI - SwiFT: Swin 4D fMRI Transformer
Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). Title: SwiFT: Swin 4D fMRI Transformer Abstract: Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches for fMRI analysis utilize hand-crafted features, but the process of feature extraction risks losing essential information in fMRI scans. To address this challenge, we present SwiFT (Swin 4D fMRI Transformer), a Swin Transformer architecture that can learn brain dynamics directly from fMRI volumes in a memory and computation-efficient manner. SwiFT achieves this by implementing a 4D window multi-head self-attention mechanism and absolute positional embeddings. We evaluate SwiFT using multiple large-scale resting-state fMRI datasets, including the Human Connectome Project (HCP), Adolescent Brain Cognitive Development (ABCD), and UK Biobank (UKB) datasets, to predict sex, age, and cognitive intelligence. Our experimental outcomes reveal that SwiFT consistently outperforms recent state-of-the-art models. Furthermore, by leveraging its end-to-end learning capability, we show that contrastive loss-based self-supervised pre-training of SwiFT can enhance performance on downstream tasks. Additionally, we employ an explainable AI method to identify the brain regions associated with sex classification. To our knowledge, SwiFT is the first Swin Transformer architecture to process dimensional spatiotemporal brain functional data in an end-to-end fashion. Our work holds substantial potential in facilitating scalable learning of functional brain imaging in neuroscience research by reducing the hurdles associated with applying Transformer models to high-dimensional fMRI. Speaker: Junbeom Kwon is a research associate working in Prof. Jiook Cha’s lab at Seoul National University. Paper link: https://arxiv.org/abs/2307.05916
Multisensory integration in peripersonal space (PPS) for action, perception and consciousness
Note the later time in the USA!
BrainLM Journal Club
Connor Lane will lead a journal club on the recent BrainLM preprint, a foundation model for fMRI trained using self-supervised masked autoencoder training. Preprint: https://www.biorxiv.org/content/10.1101/2023.09.12.557460v1 Tweeprint: https://twitter.com/david_van_dijk/status/1702336882301112631?t=Q2-U92-BpJUBh9C35iUbUA&s=19
Internet interventions targeting grief symptoms
Web-based self-help interventions for coping with prolonged grief have established their efficacy. However, few programs address recent losses and investigate the effect of self-tailoring of the content. In an international project, the text-based self-help program LIVIA was adapted and complemented with an Embodied Conversational Agent, an initial risk assessment and a monitoring tool. The new program SOLENA was evaluated in three trials in Switzerland, the Netherlands and Portugal. The aim of the trials was to evaluate the clinical efficacy for reducing grief, depression and loneliness and to examine client satisfaction and technology acceptance. The talk will present the SOLENA program and report results of the Portuguese and Dutch trial as well as preliminary results of the Swiss RCT. The ongoing Swiss trial compares a standardised to a self-tailored delivery format and analyses clinical outcomes, the helpfulness of specific content and the working alliance. Finally, lessons learned in the development and evaluation of a web-based self-help intervention for older adults will be discusses.
Self as Processes (BACN Mid-career Prize Lecture 2023)
An understanding of the self helps explain not only human thoughts, feelings, attitudes but also many aspects of everyday behaviour. This talk focuses on a viewpoint - self as processes. This viewpoint emphasizes the dynamics of the self that best connects with the development of the self over time and its realist orientation. We are combining psychological experiments and data mining to comprehend the stability and adaptability of the self across various populations. In this talk, I draw on evidence from experimental psychology, cognitive neuroscience, and machine learning approaches to demonstrate why and how self-association affects cognition and how it is modulated by various social experiences and situational factors
Doubting the neurofeedback double-blind do participants have residual awareness of experimental purposes in neurofeedback studies?
Neurofeedback provides a feedback display which is linked with on-going brain activity and thus allows self-regulation of neural activity in specific brain regions associated with certain cognitive functions and is considered a promising tool for clinical interventions. Recent reviews of neurofeedback have stressed the importance of applying the “double-blind” experimental design where critically the patient is unaware of the neurofeedback treatment condition. An important question then becomes; is double-blind even possible? Or are subjects aware of the purposes of the neurofeedback experiment? – this question is related to the issue of how we assess awareness or the absence of awareness to certain information in human subjects. Fortunately, methods have been developed which employ neurofeedback implicitly, where the subject is claimed to have no awareness of experimental purposes when performing the neurofeedback. Implicit neurofeedback is intriguing and controversial because it runs counter to the first neurofeedback study, which showed a link between awareness of being in a certain brain state and control of the neurofeedback-derived brain activity. Claiming that humans are unaware of a specific type of mental content is a notoriously difficult endeavor. For instance, what was long held as wholly unconscious phenomena, such as dreams or subliminal perception, have been overturned by more sensitive measures which show that degrees of awareness can be detected. In this talk, I will discuss whether we will critically examine the claim that we can know for certain that a neurofeedback experiment was performed in an unconscious manner. I will present evidence that in certain neurofeedback experiments such as manipulations of attention, participants display residual degrees of awareness of experimental contingencies to alter their cognition.
Quasicriticality and the quest for a framework of neuronal dynamics
Critical phenomena abound in nature, from forest fires and earthquakes to avalanches in sand and neuronal activity. Since the 2003 publication by Beggs & Plenz on neuronal avalanches, a growing body of work suggests that the brain homeostatically regulates itself to operate near a critical point where information processing is optimal. At this critical point, incoming activity is neither amplified (supercritical) nor damped (subcritical), but approximately preserved as it passes through neural networks. Departures from the critical point have been associated with conditions of poor neurological health like epilepsy, Alzheimer's disease, and depression. One complication that arises from this picture is that the critical point assumes no external input. But, biological neural networks are constantly bombarded by external input. How is then the brain able to homeostatically adapt near the critical point? We’ll see that the theory of quasicriticality, an organizing principle for brain dynamics, can account for this paradoxical situation. As external stimuli drive the cortex, quasicriticality predicts a departure from criticality while maintaining optimal properties for information transmission. We’ll see that simulations and experimental data confirm these predictions and describe new ones that could be tested soon. More importantly, we will see how this organizing principle could help in the search for biomarkers that could soon be tested in clinical studies.
Beyond Volition
Voluntary actions are actions that agents choose to make. Volition is the set of cognitive processes that implement such choice and initiation. These processes are often held essential to modern societies, because they form the cognitive underpinning for concepts of individual autonomy and individual responsibility. Nevertheless, psychology and neuroscience have struggled to define volition, and have also struggled to study it scientifically. Laboratory experiments on volition, such as those of Libet, have been criticised, often rather naively, as focussing exclusively on meaningless actions, and ignoring the factors that make voluntary action important in the wider world. In this talk, I will first review these criticisms, and then look at extending scientific approaches to volition in three directions that may enrich scientific understanding of volition. First, volition becomes particularly important when the range of possible actions is large and unconstrained - yet most experimental paradigms involve minimal response spaces. We have developed a novel paradigm for eliciting de novo actions through verbal fluency, and used this to estimate the elusive conscious experience of generativity. Second, volition can be viewed as a mechanism for flexibility, by promoting adaptation of behavioural biases. This view departs from the tradition of defining volition by contrasting internally-generated actions with externally-triggered actions, and instead links volition to model-based reinforcement learning. By using the context of competitive games to re-operationalise the classic Libet experiment, we identified a form of adaptive autonomy that allows agents to reduce biases in their action choices. Interestingly, this mechanism seems not to require explicit understanding and strategic use of action selection rules, in contrast to classical ideas about the relation between volition and conscious, rational thought. Third, I will consider volition teleologically, as a mechanism for achieving counterfactual goals through complex problem-solving. This perspective gives a key role in mediating between understanding and planning on the one hand, and instrumental action on the other hand. Taken together, these three cognitive phenomena of generativity, flexibility, and teleology may partly explain why volition is such an important cognitive function for organisation of human behaviour and human flourishing. I will end by discussing how this enriched view of volition can relate to individual autonomy and responsibility.
A sense without sensors: how non-temporal stimulus features influence the perception and the neural representation of time
Any sensory experience of the world, from the touch of a caress to the smile on our friend’s face, is embedded in time and it is often associated with the perception of the flow of it. The perception of time is therefore a peculiar sensory experience built without dedicated sensors. How the perception of time and the content of a sensory experience interact to give rise to this unique percept is unclear. A few empirical evidences show the existence of this interaction, for example the speed of a moving object or the number of items displayed on a computer screen can bias the perceived duration of those objects. However, to what extent the coding of time is embedded within the coding of the stimulus itself, is sustained by the activity of the same or distinct neural populations and subserved by similar or distinct neural mechanisms is far from clear. Addressing these puzzles represents a way to gain insight on the mechanism(s) through which the brain represents the passage of time. In my talk I will present behavioral and neuroimaging studies to show how concurrent changes of visual stimulus duration, speed, visual contrast and numerosity, shape and modulate brain’s and pupil’s responses and, in case of numerosity and time, influence the topographic organization of these features along the cortical visual hierarchy.
The sense of agency as an explorative role in our perception and action
The sense of agency refers to the subjective feeling of controlling one's own behavior and, through them, external events. Why is this subjective feeling important for humans? Is it just a by-product of our actions? Previous studies have shown that the sense of agency can affect the intensity of sensory input because we predict the input from our motor intention. However, my research has found that the sense of agency plays more roles than just predictions. It enhances perceptual processes of sensory input and potentially helps to harvest more information about the link between the external world and the self. Furthermore, our recent research found both indirect and direct evidence that the sense of agency is important for people's exploratory behaviors, and this may be linked to proximal exploitations of one's control in the environment. In this talk, I will also introduce the paradigms we use to study the sense of agency as a result of perceptual processes, and our findings of individual differences in this sense and the implications.
Understanding and Mitigating Bias in Human & Machine Face Recognition
With the increasing use of automated face recognition (AFR) technologies, it is important to consider whether these systems not only perform accurately, but also equitability or without “bias”. Despite rising public, media, and scientific attention to this issue, the sources of bias in AFR are not fully understood. This talk will explore how human cognitive biases may impact our assessments of performance differentials in AFR systems and our subsequent use of those systems to make decisions. We’ll also show how, if we adjust our definition of what a “biased” AFR algorithm looks like, we may be able to create algorithms that optimize the performance of a human+algorithm team, not simply the algorithm itself.
From spikes to factors: understanding large-scale neural computations
It is widely accepted that human cognition is the product of spiking neurons. Yet even for basic cognitive functions, such as the ability to make decisions or prepare and execute a voluntary movement, the gap between spikes and computation is vast. Only for very simple circuits and reflexes can one explain computations neuron-by-neuron and spike-by-spike. This approach becomes infeasible when neurons are numerous the flow of information is recurrent. To understand computation, one thus requires appropriate abstractions. An increasingly common abstraction is the neural ‘factor’. Factors are central to many explanations in systems neuroscience. Factors provide a framework for describing computational mechanism, and offer a bridge between data and concrete models. Yet there remains some discomfort with this abstraction, and with any attempt to provide mechanistic explanations above that of spikes, neurons, cell-types, and other comfortingly concrete entities. I will explain why, for many networks of spiking neurons, factors are not only a well-defined abstraction, but are critical to understanding computation mechanistically. Indeed, factors are as real as other abstractions we now accept: pressure, temperature, conductance, and even the action potential itself. I use recent empirical results to illustrate how factor-based hypotheses have become essential to the forming and testing of scientific hypotheses. I will also show how embracing factor-level descriptions affords remarkable power when decoding neural activity for neural engineering purposes.
Developmentally structured coactivity in the hippocampal trisynaptic loop
The hippocampus is a key player in learning and memory. Research into this brain structure has long emphasized its plasticity and flexibility, though recent reports have come to appreciate its remarkably stable firing patterns. How novel information incorporates itself into networks that maintain their ongoing dynamics remains an open question, largely due to a lack of experimental access points into network stability. Development may provide one such access point. To explore this hypothesis, we birthdated CA1 pyramidal neurons using in-utero electroporation and examined their functional features in freely moving, adult mice. We show that CA1 pyramidal neurons of the same embryonic birthdate exhibit prominent cofiring across different brain states, including behavior in the form of overlapping place fields. Spatial representations remapped across different environments in a manner that preserves the biased correlation patterns between same birthdate neurons. These features of CA1 activity could partially be explained by structured connectivity between pyramidal cells and local interneurons. These observations suggest the existence of developmentally installed circuit motifs that impose powerful constraints on the statistics of hippocampal output.
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.
Neural mechanisms underlying visual and vestibular self-motion perception
Integrative Neuromodulation: from biomarker identification to optimizing neuromodulation
Why do we make decisions impulsively blinded in an emotionally rash moment? Or caught in the same repetitive suboptimal loop, avoiding fears or rushing headlong towards illusory rewards? These cognitive constructs underlying self-control and compulsive behaviours and their influence by emotion or incentives are relevant dimensionally across healthy individuals and hijacked across disorders of addiction, compulsivity and mood. My lab focuses on identifying theory-driven modifiable biomarkers focusing on these cognitive constructs with the ultimate goal to optimize and develop novel means of neuromodulation. Here I will provide a few examples of my group’s recent work to illustrate this approach. I describe a series of recent studies on intracranial physiology and acute stimulation focusing on risk taking and emotional processing. This talk highlights the subthalamic nucleus, a common target for deep brain stimulation for Parkinson’s disease and obsessive-compulsive disorder. I further describe recent translational work in non-invasive neuromodulation. Together these examples illustrate the approach of the lab highlighting modifiable biomarkers and optimizing neuromodulation.
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.
Myelin Formation and Oligodendrocyte Biology in Epilepsy
Epilepsy is one of the most common neurological diseases according to the World Health Organization (WHO) affecting around 70 million people worldwide [WHO]. Patients who suffer from epilepsy also suffer from a variety of neuro-psychiatric co-morbidities, which they can experience as crippling as the seizure condition itself. Adequate organization of cerebral white matter is utterly important for cognitive development. The failure of integration of neurologic function with cognition is reflected in neuro-psychiatric disease, such as autism spectrum disorder (ASD). However, in epilepsy we know little about the importance of white matter abnormalities in epilepsy-associated co-morbidities. Epilepsy surgery is an important therapy strategy in patients where conventional anti-epileptic drug treatment fails . On histology of the resected brain samples, malformations of cortical development (MCD) are common among the epilepsy surgery population, especially focal cortical dysplasia (FCD) and tuberous sclerosis complex (TSC). Both pathologies are associated with constitutive activation of the mTOR pathway. Interestingly, some type of FCD is morphological similar to TSC cortical tubers including the abnormalities of the white matter. Hypomyelination with lack of myelin-producing cells, the oligodendrocytes, within the lesional area is a striking phenomenon. Impairment of the complex myelination process can have a major impact on brain function. In the worst case leading to distorted or interrupted neurotransmissions. It is still unclear whether the observed myelin pathology in epilepsy surgical specimens is primarily related to the underlying malformation process or is just a secondary phenomenon of recurrent epileptic seizures creating a toxic micro-environment which hampers myelin formation. Interestingly, mTORC1 has been implicated as key signal for myelination, thus, promoting the maturation of oligodendrocytes . These results, however, remain controversial. Regardless of the underlying pathophysiologic mechanism, alterations of myelin dynamics, depending on their severity, are known to be linked to various kinds of developmental disorders or neuropsychiatric manifestations.
Private oxytocin supply and its receptors in the hypothalamus for social avoidance learning
Many animals live in complex social groups. To survive, it is essential to know who to avoid and who to interact. Although naïve mice are naturally attracted to any adult conspecifics, a single defeat experience could elicit social avoidance towards the aggressor for days. The neural mechanisms underlying the behavior switch from social approach to social avoidance remains incompletely understood. Here, we identify oxytocin neurons in the retrochiasmatic supraoptic nucleus (SOROXT) and oxytocin receptor (OXTR) expressing cells in the anterior subdivision of ventromedial hypothalamus, ventrolateral part (aVMHvlOXTR) as a key circuit motif for defeat-induced social avoidance learning. After defeat, aVMHvlOXTR cells drastically increase their responses to aggressor cues. This response change is functionally important as optogenetic activation of aVMHvlOXTR cells elicits time-locked social avoidance towards a benign social target whereas inactivating the cells suppresses defeat-induced social avoidance. Furthermore, OXTR in the aVMHvl is itself essential for the behavior change. Knocking out OXTR in the aVMHvl or antagonizing the receptor during defeat, but not during post-defeat social interaction, impairs defeat-induced social avoidance. aVMHvlOXTR receives its private supply of oxytocin from SOROXT cells. SOROXT is highly activated by the noxious somatosensory inputs associated with defeat. Oxytocin released from SOROXT depolarizes aVMHvlOXTR cells and facilitates their synaptic potentiation, and hence, increases aVMHvlOXTR cell responses to aggressor cues. Ablating SOROXT cells impairs defeat-induced social avoidance learning whereas activating the cells promotes social avoidance after a subthreshold defeat experience. Altogether, our study reveals an essential role of SOROXT-aVMHvlOXTR circuit in defeat-induced social learning and highlights the importance of hypothalamic oxytocin system in social ranking and its plasticity.
Dynamics of cortical circuits: underlying mechanisms and computational implications
A signature feature of cortical circuits is the irregularity of neuronal firing, which manifests itself in the high temporal variability of spiking and the broad distribution of rates. Theoretical works have shown that this feature emerges dynamically in network models if coupling between cells is strong, i.e. if the mean number of synapses per neuron K is large and synaptic efficacy is of order 1/\sqrt{K}. However, the degree to which these models capture the mechanisms underlying neuronal firing in cortical circuits is not fully understood. Results have been derived using neuron models with current-based synapses, i.e. neglecting the dependence of synaptic current on the membrane potential, and an understanding of how irregular firing emerges in models with conductance-based synapses is still lacking. Moreover, at odds with the nonlinear responses to multiple stimuli observed in cortex, network models with strongly coupled cells respond linearly to inputs. In this talk, I will discuss the emergence of irregular firing and nonlinear response in networks of leaky integrate-and-fire neurons. First, I will show that, when synapses are conductance-based, irregular firing emerges if synaptic efficacy is of order 1/\log(K) and, unlike in current-based models, persists even under the large heterogeneity of connections which has been reported experimentally. I will then describe an analysis of neural responses as a function of coupling strength and show that, while a linear input-output relation is ubiquitous at strong coupling, nonlinear responses are prominent at moderate coupling. I will conclude by discussing experimental evidence of moderate coupling and loose balance in the mouse cortex.
Cortical seizure mechanisms: insights from calcium, glutamate and GABA imaging
Focal neocortical epilepsy is associated with intermittent brief population discharges (interictal spikes), which resemble sentinel spikes that often occur at the onset of seizures. Why interictal spikes self-terminate whilst seizures persist and propagate is incompletely understood, but is likely to relate to the intermittent collapse of feed-forward GABAergic inhibition. Inhibition could fail through multiple mechanisms, including (i) an attenuation or even reversal of the driving force for chloride in postsynaptic neurons because of intense activation of GABAA receptors, (ii) an elevation of potassium secondary to chloride influx leading to depolarization of neurons, or (iii) insufficient GABA release from interneurons. I shall describe the results of experiments using fluorescence imaging of calcium, glutamate or GABA in awake rodent models of neocortical epileptiform activity. Interictal spikes were accompanied by brief glutamate transients which were maximal at the initiation site and rapidly propagatedcentrifugally. GABA transients lasted longer than glutamate transients and were maximal ~1.5 mm from the focus. Prior to seizure initiation GABA transients were attenuated, whilst glutamate transients increased, consistent with a progressive failure of local inhibitory restraint. As seizures increased in frequency, there was a gradual increase in the spatial extent of spike-associated glutamate transients associated with interictal spikes. Neurotransmitter imaging thus reveals a progressive collapse of an annulus of feed-forward GABA release, allowing runaway recruitment of excitatory neurons as a fundamental mechanism underlying the escape of seizures from local inhibitory restraint.
Self-direction in daily stress management: the solution for mental health issues
In the lecture Yvette Roke and Jamie Hoefakker will discuss the positive and negative effects of daily stress on mental health. They will also highlight which characteristics are likely to cause more stress related issues, and why recovery time is very important. They will give an understanding of autism spectrum disorder (ASD) in relation to daily stress and they will discuss the app, SAM the stress autism mate, developed and investigated (SCED design) in co-creation with their patients with ASD.
Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation
Studies of the mouse visual system have revealed a variety of visual brain areas in a roughly hierarchical arrangement, together with a multitude of behavioral capacities, ranging from stimulus-reward associations, to goal-directed navigation, and object-centric discriminations. However, an overall understanding of the mouse’s visual cortex organization, and how this organization supports visual behaviors, remains unknown. Here, we take a computational approach to help address these questions, providing a high-fidelity quantitative model of mouse visual cortex. By analyzing factors contributing to model fidelity, we identified key principles underlying the organization of mouse visual cortex. Structurally, we find that comparatively low-resolution and shallow structure were both important for model correctness. Functionally, we find that models trained with task-agnostic, unsupervised objective functions, based on the concept of contrastive embeddings were substantially better than models trained with supervised objectives. Finally, the unsupervised objective builds a general-purpose visual representation that enables the system to achieve better transfer on out-of-distribution visual, scene understanding and reward-based navigation tasks. Our results suggest that mouse visual cortex is a low-resolution, shallow network that makes best use of the mouse’s limited resources to create a light-weight, general-purpose visual system – in contrast to the deep, high-resolution, and more task-specific visual system of primates.
Myelin Formation and Oligodendrocyte Biology in Epilepsy
Epilepsy is one of the most common neurological diseases according to the World Health Organization (WHO) affecting around 70 million people worldwide [WHO]. Patients who suffer from epilepsy also suffer from a variety of neuro-psychiatric co-morbidities, which they can experience as crippling as the seizure condition itself. Adequate organization of cerebral white matter is utterly important for cognitive development. The failure of integration of neurologic function with cognition is reflected in neuro-psychiatric disease, such as autism spectrum disorder (ASD). However, in epilepsy we know little about the importance of white matter abnormalities in epilepsy-associated co-morbidities. Epilepsy surgery is an important therapy strategy in patients where conventional anti-epileptic drug treatment fails . On histology of the resected brain samples, malformations of cortical development (MCD) are common among the epilepsy surgery population, especially focal cortical dysplasia (FCD) and tuberous sclerosis complex (TSC). Both pathologies are associated with constitutive activation of the mTOR pathway. Interestingly, some type of FCD is morphological similar to TSC cortical tubers including the abnormalities of the white matter. Hypomyelination with lack of myelin-producing cells, the oligodendrocytes, within the lesional area is a striking phenomenon. Impairment of the complex myelination process can have a major impact on brain function. In the worst case leading to distorted or interrupted neurotransmissions. It is still unclear whether the observed myelin pathology in epilepsy surgical specimens is primarily related to the underlying malformation process or is just a secondary phenomenon of recurrent epileptic seizures creating a toxic micro-environment which hampers myelin formation. Interestingly, mTORC1 has been implicated as key signal for myelination, thus, promoting the maturation of oligodendrocytes . These results, however, remain controversial. Regardless of the underlying pathophysiologic mechanism, alterations of myelin dynamics, depending on their severity, are known to be linked to various kinds of developmental disorders or neuropsychiatric manifestations.
From Machine Learning to Autonomous Intelligence
How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons? I will propose a possible path towards autonomous intelligent agents, based on a new modular cognitive architecture and a somewhat new self supervised training paradigm. The centerpiece of the proposed architecture is a configurable predictive world model that allows the agent to plan. Behavior and learning are driven by a set of differentiable intrinsic cost functions. The world model uses a new type of energy-based model architecture called H-JEPA (Hierarchical Joint Embedding Predictive Architecture). H-JEPA learns hierarchical abstract representations of the world that are simultaneously maximally informative and maximally predictable.
Identifying central mechanisms of glucocorticoid circadian rhythm dysfunction in breast cancer
The circadian release of endogenous glucocorticoids is essential in preparing and synchronizing the body’s daily physiological needs. Disruption in the rhythmic activity of glucocorticoids has been observed in individuals with a variety of cancer types, and blunting of this rhythm has been shown to predict cancer mortality and declines in quality of life. This suggests that a disrupted glucocorticoid rhythm is potentially a shared phenotype across cancers. However, where this phenomenon is driven by the cancer itself, and the causal mechanisms that link glucocorticoid rhythm dysfunction and cancer outcomes remain preliminary at best. The regulation of daily glucocorticoid activity has been well-characterized and is maintained, in part, by the coordinated response of the hypothalamic-pituitary-adrenal (HPA) axis, consisting of the suprachiasmatic nucleus (SCN) and corticotropin-releasing hormone-expressing neurons of the paraventricular nucleus of the hypothalamus (PVNCRH). Consequently, we set out to examine if cancer-induced glucocorticoid dysfunction is regulated by disruptions within these hypothalamic nuclei. In comparison to their tumor-free baseline, mammary tumor-bearing mice exhibited a blunting of glucocorticoid rhythms across multiple timepoints throughout the day, as measured by the overall levels and the slope of fecal corticosterone rhythms, during tumor progression. We further examined how peripheral tumors shape hypothalamic activity within the brain. Serial two-photon tomography for whole-brain cFos imaging suggests a disrupted activation of the PVN in mice with tumors. Additionally, we found GFP labeled CRH+ neurons within the PVN after injection of pseudorabies virus expressing GFP into the tumor, pointing to the PVN as a primary target disrupted by mammary tumors. Preliminary in vivo fiber photometry data show that PVNCRH neurons exhibit enhanced calcium activity during tumor progression, as compared to baseline (no tumor) activity. Taken together, this suggests that there may be an overactive HPA response during tumor progression, which in turn, may result in a subsequent negative feedback on glucocorticoid rhythms. Current studies are examining whether tumor progression modulates SCN calcium activity, how the transcriptional profile of PVNCRH neurons is changed, and test if manipulation of the neurocircuitry surrounding glucocorticoid rhythmicity alters tumor characteristics.
How People Form Beliefs
In this talk I will present our recent behavioural and neuroscience research on how the brain motivates itself to form particular beliefs and why it does so. I will propose that the utility of a belief is derived from the potential outcomes associated with holding it. Outcomes can be internal (e.g., positive/negative feelings) or external (e.g., material gain/loss), and only some are dependent on belief accuracy. We show that belief change occurs when the potential outcomes of holding it alters, for example when moving from a safe environment to a threatening environment. Our findings yield predictions about how belief formation alters as a function of mental health. We test these predictions using a linguistic analysis of participants’ web searches ‘in the wild’ to quantify the affective properties of information they consume and relate those to reported psychiatric symptoms. Finally, I will present a study in which we used our framework to alter the incentive structure of social media platforms to reduce the spread of misinformation and improve belief accuracy.
From Machine Learning to Autonomous Intelligence
How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons? I will propose a possible path towards autonomous intelligent agents, based on a new modular cognitive architecture and a somewhat new self-supervised training paradigm. The centerpiece of the proposed architecture is a configurable predictive world model that allows the agent to plan. Behavior and learning are driven by a set of differentiable intrinsic cost functions. The world model uses a new type of energy-based model architecture called H-JEPA (Hierarchical Joint Embedding Predictive Architecture). H-JEPA learns hierarchical abstract representations of the world that are simultaneously maximally informative and maximally predictable. The corresponding working paper is available here:https://openreview.net/forum?id=BZ5a1r-kVsf
The Secret Bayesian Life of Ring Attractor Networks
Efficient navigation requires animals to track their position, velocity and heading direction (HD). Some animals’ behavior suggests that they also track uncertainties about these navigational variables, and make strategic use of these uncertainties, in line with a Bayesian computation. Ring-attractor networks have been proposed to estimate and track these navigational variables, for instance in the HD system of the fruit fly Drosophila. However, such networks are not designed to incorporate a notion of uncertainty, and therefore seem unsuited to implement dynamic Bayesian inference. Here, we close this gap by showing that specifically tuned ring-attractor networks can track both a HD estimate and its associated uncertainty, thereby approximating a circular Kalman filter. We identified the network motifs required to integrate angular velocity observations, e.g., through self-initiated turns, and absolute HD observations, e.g., visual landmark inputs, according to their respective reliabilities, and show that these network motifs are present in the connectome of the Drosophila HD system. Specifically, our network encodes uncertainty in the amplitude of a localized bump of neural activity, thereby generalizing standard ring attractor models. In contrast to such standard attractors, however, proper Bayesian inference requires the network dynamics to operate in a regime away from the attractor state. More generally, we show that near-Bayesian integration is inherent in generic ring attractor networks, and that their amplitude dynamics can account for close-to-optimal reliability weighting of external evidence for a wide range of network parameters. This only holds, however, if their connection strengths allow the network to sufficiently deviate from the attractor state. Overall, our work offers a novel interpretation of ring attractor networks as implementing dynamic Bayesian integrators. We further provide a principled theoretical foundation for the suggestion that the Drosophila HD system may implement Bayesian HD tracking via ring attractor dynamics.
Learning static and dynamic mappings with local self-supervised plasticity
Animals exhibit remarkable learning capabilities with little direct supervision. Likewise, self-supervised learning is an emergent paradigm in artificial intelligence, closing the performance gap to supervised learning. In the context of biology, self-supervised learning corresponds to a setting where one sense or specific stimulus may serve as a supervisory signal for another. After learning, the latter can be used to predict the former. On the implementation level, it has been demonstrated that such predictive learning can occur at the single neuron level, in compartmentalized neurons that separate and associate information from different streams. We demonstrate the power such self-supervised learning over unsupervised (Hebb-like) learning rules, which depend heavily on stimulus statistics, in two examples: First, in the context of animal navigation where predictive learning can associate internal self-motion information always available to the animal with external visual landmark information, leading to accurate path-integration in the dark. We focus on the well-characterized fly head direction system and show that our setting learns a connectivity strikingly similar to the one reported in experiments. The mature network is a quasi-continuous attractor and reproduces key experiments in which optogenetic stimulation controls the internal representation of heading, and where the network remaps to integrate with different gains. Second, we show that incorporating global gating by reward prediction errors allows the same setting to learn conditioning at the neuronal level with mixed selectivity. At its core, conditioning entails associating a neural activity pattern induced by an unconditioned stimulus (US) with the pattern arising in response to a conditioned stimulus (CS). Solving the generic problem of pattern-to-pattern associations naturally leads to emergent cognitive phenomena like blocking, overshadowing, saliency effects, extinction, interstimulus interval effects etc. Surprisingly, we find that the same network offers a reductionist mechanism for causal inference by resolving the post hoc, ergo propter hoc fallacy.
Odd dynamics of living chiral crystals
The emergent dynamics exhibited by collections of living organisms often shows signatures of symmetries that are broken at the single-organism level. At the same time, organism development itself encompasses a well-coordinated sequence of symmetry breaking events that successively transform a single, nearly isotropic cell into an animal with well-defined body axis and various anatomical asymmetries. Combining these key aspects of collective phenomena and embryonic development, we describe here the spontaneous formation of hydrodynamically stabilized active crystals made of hundreds of starfish embryos that gather during early development near fluid surfaces. We describe a minimal hydrodynamic theory that is fully parameterized by experimental measurements of microscopic interactions among embryos. Using this theory, we can quantitatively describe the stability, formation and rotation of crystals and rationalize the emergence of mechanical properties that carry signatures of an odd elastic material. Our work thereby quantitatively connects developmental symmetry breaking events on the single-embryo level with remarkable macroscopic material properties of a novel living chiral crystal system.
Active mechanics of sea star oocytes
The cytoskeleton has the remarkable ability to self-organize into active materials which underlie diverse cellular processes ranging from motility to cell division. Actomyosin is a canonical example of an active material, which generates cellularscale contractility in part through the forces exerted by myosin motors on actin filaments. While the molecular players underlying actomyosin contractility have been well characterized, how cellular-scale deformation in disordered actomyosin networks emerges from filament-scale interactions is not well understood. In this talk, I’ll present work done in collaboration with Sebastian Fürthauer and Nikta Fakhri addressing this question in vivo using the meiotic surface contraction wave seen in oocytes of the bat star Patiria miniata as a model system. By perturbing actin polymerization, we find that the cellular deformation rate is a nonmonotonic function of cortical actin density peaked near the wild type density. To understand this, we develop an active fluid model coarse-grained from filament-scale interactions and find quantitative agreement with the measured data. The model makes further predictions, including the surprising prediction that deformation rate decreases with increasing motor concentration. We test these predictions through protein overexpression and find quantitative agreement. Taken together, this work is an important step for bridging the molecular and cellular length scales for cytoskeletal networks in vivo.
A Game Theoretical Framework for Quantifying Causes in Neural Networks
Which nodes in a brain network causally influence one another, and how do such interactions utilize the underlying structural connectivity? One of the fundamental goals of neuroscience is to pinpoint such causal relations. Conventionally, these relationships are established by manipulating a node while tracking changes in another node. A causal role is then assigned to the first node if this intervention led to a significant change in the state of the tracked node. In this presentation, I use a series of intuitive thought experiments to demonstrate the methodological shortcomings of the current ‘causation via manipulation’ framework. Namely, a node might causally influence another node, but how much and through which mechanistic interactions? Therefore, establishing a causal relationship, however reliable, does not provide the proper causal understanding of the system, because there often exists a wide range of causal influences that require to be adequately decomposed. To do so, I introduce a game-theoretical framework called Multi-perturbation Shapley value Analysis (MSA). Then, I present our work in which we employed MSA on an Echo State Network (ESN), quantified how much its nodes were influencing each other, and compared these measures with the underlying synaptic strength. We found that: 1. Even though the network itself was sparse, every node could causally influence other nodes. In this case, a mere elucidation of causal relationships did not provide any useful information. 2. Additionally, the full knowledge of the structural connectome did not provide a complete causal picture of the system either, since nodes frequently influenced each other indirectly, that is, via other intermediate nodes. Our results show that just elucidating causal contributions in complex networks such as the brain is not sufficient to draw mechanistic conclusions. Moreover, quantifying causal interactions requires a systematic and extensive manipulation framework. The framework put forward here benefits from employing neural network models, and in turn, provides explainability for them.
Peripersonal space (PPS) as a primary interface for self-environment interactions
Peripersonal space (PPS) defines the portion of space where interactions between our body and the external environment more likely occur. There is no physical boundary defining the PPS with respect to the extrapersonal space, but PPS is continuously constructed by a dedicated neural system integrating external stimuli and tactile stimuli on the body, as a function of their potential interaction. This mechanism represents a primary interface between the individual and the environment. In this talk, I will present most recent evidence and highlight the current debate about the neural and computational mechanisms of PPS, its main functions and properties. I will discuss novel data showing how PPS dynamically shapes to optimize body-environment interactions. I will describe a novel electrophysiological paradigm to study and measure PPS, and show how this has been used to search for a basic marker of potentials of self-environment interaction in newborns and patients with disorders of consciousness. Finally, I will discuss how PPS is also involved in, and in turn shaped by, social interactions. Under these acceptances, I will discuss how PPS plays a key role in self-consciousness.
Membrane mechanics meet minimal manifolds
Changes in the geometry and topology of self-assembled membranes underlie diverse processes across cellular biology and engineering. Similar to lipid bilayers, monolayer colloidal membranes studied by the Sharma (IISc Bangalore) and Dogic (UCSB) Labs have in-plane fluid-like dynamics and out-of-plane bending elasticity, but their open edges and micron length scale provide a tractable system to study the equilibrium energetics and dynamic pathways of membrane assembly and reconfiguration. First, we discuss how doping colloidal membranes with short miscible rods transforms disk-shaped membranes into saddle-shaped minimal surfaces with complex edge structures. Theoretical modeling demonstrates that their formation is driven by increasing positive Gaussian modulus, which in turn is controlled by the fraction of short rods. Further coalescence of saddle-shaped surfaces leads to exotic topologically distinct structures, including shapes similar to catenoids, tri-noids, four-noids, and higher order structures. We then mathematically explore the mechanics of these catenoid-like structures subject to an external axial force and elucidate their intimate connection to two problems whose solutions date back to Euler: the shape of an area-minimizing soap film and the buckling of a slender rod under compression. A perturbation theory argument directly relates the tensions of membranes to the stability properties of minimal surfaces. We also investigate the effects of including a Gaussian curvature modulus, which, for small enough membranes, causes the axial force to diverge as the ring separation approaches its maximal value.
From the Didactic to the Heuristic Use of Analogies in Science Teaching
Extensive research on science teaching has shown the effectiveness of analogies as a didactic tool which, when appropriately and effectively used, facilitates the learning process of abstract concepts. This seminar does not contradict the efficacy of such a didactic use of analogies in this seminar but switches attention and interest on their heuristic use in approaching and understanding of what previously unknown. Such a use of analogies derives from research with 10 to 17 year-olds, who, when asked to make predictions in novel situations and to then provide explanations about these predictions, they self-generated analogies and used them by reasoning on their basis. This heuristic use of analogies can be used in science teaching in revealing how students approach situations they have not considered before as well as the sources they draw upon in doing so.
Heading perception in crowded environments
Self-motion through a visual world creates a pattern of expanding visual motion called optic flow. Heading estimation from the optic flow is accurate in rigid environments. But it becomes challenging when other humans introduce an independent motion to the scene. The biological motion of human walkers consists of translation through space and associated limb articulation. The characteristic motion pattern is regular, though complex. A world full of humans moving around is nonrigid, causing heading errors. But limb articulation alone does not perturb the global structure of the flow field, matching the rigidity assumption. For heading perception from optic flow analysis, limb articulation alone should not impair heading estimates. But we observed heading biases when participants encountered a group of point-light walkers. Our research investigates the interactions between optic flow perception and biological motion perception. We further analyze the impact of environmental information.
Canonical neural networks perform active inference
The free-energy principle and active inference have received a significant attention in the fields of neuroscience and machine learning. However, it remains to be established whether active inference is an apt explanation for any given neural network that actively exchanges with its environment. To address this issue, we show that a class of canonical neural networks of rate coding models implicitly performs variational Bayesian inference under a well-known form of partially observed Markov decision process model (Isomura, Shimazaki, Friston, Commun Biol, 2022). Based on the proposed theory, we demonstrate that canonical neural networks—featuring delayed modulation of Hebbian plasticity—can perform planning and adaptive behavioural control in the Bayes optimal manner, through postdiction of their previous decisions. This scheme enables us to estimate implicit priors under which the agent’s neural network operates and identify a specific form of the generative model. The proposed equivalence is crucial for rendering brain activity explainable to better understand basic neuropsychology and psychiatric disorders. Moreover, this notion can dramatically reduce the complexity of designing self-learning neuromorphic hardware to perform various types of tasks.
Learning from others, helping others learn: Cognitive foundations of distinctively human social learning
Learning does not occur in isolation. From parent-child interactions to formal classroom environments, humans explore, learn, and communicate in rich, diverse social contexts. Rather than simply observing and copying their conspecifics, humans engage in a range of epistemic practices that actively recruit those around them. What makes human social learning so distinctive, powerful, and smart? In this talk, I will present a series of studies that reveal the remarkably sophisticated inferential abilities that young children show not only in how they learn from others but also in how they help others learn. Children interact with others as learners and as teachers to learn and communicate about the world, about others, and even about the self. The results collectively paint a picture of human social learning that is far more than copying and imitation: It is active, bidirectional, and cooperative. I will end by discussing ongoing work that extends this picture beyond what we typically call “social learning”, with implications for building better machines that learn from and interact with humans.
On biological and cognitive autonomy
In this talk I will introduce the central notions of the theory of autonomy, as it is being currently developed in biology and cognitive science. The theory of autonomy puts forward the capacity of self-determination of organisms as whole systems, and constitutes thereby an alternative to more reductionist and mechanistic approaches. I will discuss how the theory of autonomy provides a justification for the scientific use of notions as function, norm, agency and teleology, whose epistemological legitimacy is highly debated. I will conclude by describing the difficult challenges that poses the transition from biological to cognitive autonomy.
Hebbian Plasticity Supports Predictive Self-Supervised Learning of Disentangled Representations
Discriminating distinct objects and concepts from sensory stimuli is essential for survival. Our brains accomplish this feat by forming meaningful internal representations in deep sensory networks with plastic synaptic connections. Experience-dependent plasticity presumably exploits temporal contingencies between sensory inputs to build these internal representations. However, the precise mechanisms underlying plasticity remain elusive. We derive a local synaptic plasticity model inspired by self-supervised machine learning techniques that shares a deep conceptual connection to Bienenstock-Cooper-Munro (BCM) theory and is consistent with experimentally observed plasticity rules. We show that our plasticity model yields disentangled object representations in deep neural networks without the need for supervision and implausible negative examples. In response to altered visual experience, our model qualitatively captures neuronal selectivity changes observed in the monkey inferotemporal cortex in-vivo. Our work suggests a plausible learning rule to drive learning in sensory networks while making concrete testable predictions.
The balance of excitation and inhibition and a canonical cortical computation
Excitatory and inhibitory (E & I) inputs to cortical neurons remain balanced across different conditions. The balanced network model provides a self-consistent account of this observation: population rates dynamically adjust to yield a state in which all neurons are active at biological levels, with their E & I inputs tightly balanced. But global tight E/I balance predicts population responses with linear stimulus-dependence and does not account for systematic cortical response nonlinearities such as divisive normalization, a canonical brain computation. However, when necessary connectivity conditions for global balance fail, states arise in which only a localized subset of neurons are active and have balanced inputs. We analytically show that in networks of neurons with different stimulus selectivities, the emergence of such localized balance states robustly leads to normalization, including sublinear integration and winner-take-all behavior. An alternative model that exhibits normalization is the Stabilized Supralinear Network (SSN), which predicts a regime of loose, rather than tight, E/I balance. However, an understanding of the causal relationship between E/I balance and normalization in SSN and conditions under which SSN yields significant sublinear integration are lacking. For weak inputs, SSN integrates inputs supralinearly, while for very strong inputs it approaches a regime of tight balance. We show that when this latter regime is globally balanced, SSN cannot exhibit strong normalization for any input strength; thus, in SSN too, significant normalization requires localized balance. In summary, we causally and quantitatively connect a fundamental feature of cortical dynamics with a canonical brain computation. Time allowing I will also cover our work extending a normative theoretical account of normalization which explains it as an example of efficient coding of natural stimuli. We show that when biological noise is accounted for, this theory makes the same prediction as the SSN: a transition to supralinear integration for weak stimuli.
Spatial uncertainty provides a unifying account of navigation behavior and grid field deformations
To localize ourselves in an environment for spatial navigation, we rely on vision and self-motion inputs, which only provide noisy and partial information. It is unknown how the resulting uncertainty affects navigation behavior and neural representations. Here we show that spatial uncertainty underlies key effects of environmental geometry on navigation behavior and grid field deformations. We develop an ideal observer model, which continually updates probabilistic beliefs about its allocentric location by optimally combining noisy egocentric visual and self-motion inputs via Bayesian filtering. This model directly yields predictions for navigation behavior and also predicts neural responses under population coding of location uncertainty. We simulate this model numerically under manipulations of a major source of uncertainty, environmental geometry, and support our simulations by analytic derivations for its most salient qualitative features. We show that our model correctly predicts a wide range of experimentally observed effects of the environmental geometry and its change on homing response distribution and grid field deformation. Thus, our model provides a unifying, normative account for the dependence of homing behavior and grid fields on environmental geometry, and identifies the unavoidable uncertainty in navigation as a key factor underlying these diverse phenomena.
Plasticity-driven circuit self-organization on spiking stabilized supralinear networks
Bernstein Conference 2024
Variability in Self-Organizing Networks of Neurons: Between Chance and Design
Bernstein Conference 2024
Heavy-tailed connectivity emerges from Hebbian self-organization
COSYNE 2022
Heavy-tailed connectivity emerges from Hebbian self-organization
COSYNE 2022
Predictive processing of natural images by V1 firing rates revealed by self-supervised deep neural networks
COSYNE 2022
Predictive processing of natural images by V1 firing rates revealed by self-supervised deep neural networks
COSYNE 2022
Self-assembly of the mammalian neocortex, from mouse to macaque
COSYNE 2022
Self-assembly of the mammalian neocortex, from mouse to macaque
COSYNE 2022
Self-supervised learning in neocortical layers: how the present teaches the past
COSYNE 2022
Self-supervised learning in neocortical layers: how the present teaches the past
COSYNE 2022
Back to the present: self-supervised learning in neocortical microcircuits
COSYNE 2023
Credit-based self-organization yields cortex-like topography in deep convolutional networks
COSYNE 2023
Drosophila detects negative visual evidence against self-motion
COSYNE 2023
Exploring the role of image domains in self-supervised DNN models of the rodent brain
COSYNE 2023
Mouse visual cortex as a limited-resource system that self-learns a task-general representation
COSYNE 2023
Paradoxical self-sustained dynamics emerge from orchestrated excitatory and inhibitory homeostatic plasticity rules
COSYNE 2023
A pre-cerebellar brainstem integrator implements self-location memory and enables positional homeostasis
COSYNE 2023
Self-generated vestibular prosthetic input updates forward internal model of self-motion
COSYNE 2023
Self-timed self-supervised learning
COSYNE 2023
How Symmetry and Self-Coupling Shape Dynamics and Trainability of Recurrent Neural Networks
COSYNE 2023
Contrastive-Equivariant self-supervised learning improves alignment with primate visual area IT
COSYNE 2025
Correctness is its own reward: bootstrapping error codes in self-guided reinforcement learning
COSYNE 2025
Deeper brain circuits are more self-organized
COSYNE 2025
Self-supervised predictive learning across saccades enables visual path integration
COSYNE 2025
TweetyBERT, a self-supervised vision transformer to automate birdsong annotation
COSYNE 2025
Asymmetrical modulations of decision and movement speeds during self-paced foraging reveal the dorsal striatum selective contribution to effort sensitivity
FENS Forum 2024
An attempt to elucidate how people perceive self-location
FENS Forum 2024
Can auditory self-related stimuli make changes in hemodynamic response in frontal cortex: A preliminary fNIRS study
FENS Forum 2024
An automated group-housed oral fentanyl self-administration method in mice
FENS Forum 2024
Δ9-Tetrahydrocannabinol modulates addictive behaviour induced by saturated and unsaturated high-fat diets in an animal model of operant self-administration
FENS Forum 2024
Different mechanisms underlie compulsive alcohol self-administration in male and female rats
FENS Forum 2024
Exposure to virtual nature decreases neural nociceptive processing and self-reported ratings during acute pain
FENS Forum 2024
Gender differences in estimates of one's own body size depending on % body fat and self-compassion
FENS Forum 2024
Inter-regional neural dynamics underlying self-paced action decisions
FENS Forum 2024
A midline thalamic nucleus promotes compulsive-like self-grooming in rodents
FENS Forum 2024
Neural representations underlying self and conspecific action-outcomes during joint decision-making in mice
FENS Forum 2024
A neurocomputational approach for effort-based decision-making: Comparing self and environmental motivation
FENS Forum 2024
Self-degradation of memory in Alzheimer’s disease: Experimental testing of the hypothesis and search for methods of neuroprotection
FENS Forum 2024
Self-organizing principles are insufficient for cortical neural stem cell lineage progression
FENS Forum 2024
Self-produced bodily actions and rhythms modulate fear in humans
FENS Forum 2024