Thalamic networks, at the core of thalamocortical and thalamosubcortical communications, underlie processes of perception, attention, memory, emotions, and the sleep-wake cycle, and are disrupted in mental disorders, including schizophrenia and autism. However, the underlying mechanisms of pathology are unknown. I will present novel evidence on key organizational principles, structural, and molecular features of thalamocortical networks, as well as critical thalamic pathway interactions that are likely affected in disorders. This data can facilitate modeling typical and abnormal brain function and can provide the foundation to understand heterogeneous disruption of these networks in sleep disorders, attention deficits, and cognitive and affective impairments in schizophrenia and autism, with important implications for the design of targeted therapeutic interventions

Efficient cognition requires flexible interactions between distributed neural networks in the human brain. These networks adapt to challenges by flexibly recruiting different regions and connections. In this talk, I will discuss how we study functional network plasticity and reorganization with combined neurostimulation and neuroimaging across the adult life span. I will argue that short-term plasticity enables flexible adaptation to challenges, via functional reorganization. My key hypothesis is that disruption of higher-level cognitive functions such as language can be compensated for by the recruitment of domain-general networks in our brain. Examples from healthy young brains illustrate how neurostimulation can be used to temporarily interfere with efficient processing, probing short-term network plasticity at the systems level. Examples from people with dyslexia help to better understand network disorders in the language domain and outline the potential of facilitatory neurostimulation for treatment. I will also discuss examples from aging brains where plasticity helps to compensate for loss of function. Finally, examples from lesioned brains after stroke provide insight into the brain’s potential for long-term reorganization and recovery of function. Collectively, these results challenge the view of a modular organization of the human brain and argue for a flexible redistribution of function via systems plasticity.

Digital Minds: Brain Development in the Age of Technology examines how our increasingly connected world shapes mental and cognitive health. From screen time and social media to virtual interactions, this seminar delves into the latest research on how technology influences brain development, relationships, and emotional well-being. Join us to explore strategies for harnessing technology's benefits while mitigating its potential challenges, empowering you to thrive in a digital age.

This webinar on learning and memory features three experts—Nicolas Brunel, Ashok Litwin-Kumar, and Julijana Gjorgieva—who present theoretical and computational approaches to understanding how neural circuits acquire and store information across different scales. Brunel discusses calcium-based plasticity and how standard “Hebbian-like” plasticity rules inferred from in vitro or in vivo datasets constrain synaptic dynamics, aligning with classical observations (e.g., STDP) and explaining how synaptic connectivity shapes memory. Litwin-Kumar explores insights from the fruit fly connectome, emphasizing how the mushroom body—a key site for associative learning—implements a high-dimensional, random representation of sensory features. Convergent dopaminergic inputs gate plasticity, reflecting a high-dimensional “critic” that refines behavior. Feedback loops within the mushroom body further reveal sophisticated interactions between learning signals and action selection. Gjorgieva examines how activity-dependent plasticity rules shape circuitry from the subcellular (e.g., synaptic clustering on dendrites) to the cortical network level. She demonstrates how spontaneous activity during development, Hebbian competition, and inhibitory-excitatory balance collectively establish connectivity motifs responsible for key computations such as response normalization.

In this talk, I will explore how social affective biases arise even in the absence of motivational factors as an emergent outcome of the basic structure of social learning. In several studies, we found that initial negative interactions with some members of a group can cause subsequent avoidance of the entire group, and that this avoidance perpetuates stereotypes. Additional cognitive modeling discovered that approach and avoidance behavior based on biased beliefs not only influences the evaluative (positive or negative) impressions of group members, but also shapes the depth of the cognitive representations available to learn about individuals. In other words, people have richer cognitive representations of members of groups that are not avoided, akin to individualized vs group level categories. I will end presenting a series of multi-agent reinforcement learning simulations that demonstrate the emergence of these social-structural feedback loops in the development and maintenance of affective biases.

Britta Engelhard’s research is devoted to understanding thefunction of the different brain barriers in regulating CNS immunesurveillance and how their impaired function contributes toneuroinflammatory diseases such as Multiple Sclerosis (MS) orAlzheimer’s disease (AD). Her laboratory combines expertise invascular biology, neuroimmunology and live cell imaging and hasdeveloped sophisticated in vitro and in vivo approaches to studyimmune cell interactions with the brain barriers in health andneuroinflammation.

The nervous system orchestrates adaptive behaviors by intricately coordinating responses to internal cues and environmental stimuli. This involves integrating sensory input, managing competing motivational states, and drawing on past experiences to anticipate future outcomes. While traditional models attribute this complexity to interactions between the mesocorticolimbic system and hypothalamic centers, the specific nodes of integration have remained elusive. Recent research, including our own, sheds light on the midline thalamus's overlooked role in this process. We propose that the midline thalamus integrates internal states with memory and emotional signals to guide adaptive behaviors. Our investigations into midline thalamic neuronal circuits have provided crucial insights into the neural mechanisms behind flexibility and adaptability. Understanding these processes is essential for deciphering human behavior and conditions marked by impaired motivation and emotional processing. Our research aims to contribute to this understanding, paving the way for targeted interventions and therapies to address such impairments.

Neuroimaging has greatly extended our capacity to study the workings of the human brain. Despite the wealth of knowledge this tool has generated however, there are still critical gaps in our understanding. While tremendous progress has been made in mapping areas of the brain that are specialized for particular stimuli, or cognitive processes, we still know very little about how large-scale interactions between different cortical networks facilitate the integration of information and the execution of complex tasks. Yet even the simplest behavioral tasks are complex, requiring integration over multiple cognitive domains. Our knowledge falls short not only in understanding how this integration takes place, but also in what drives the profound variation in behavior that can be observed on almost every task, even within the typically developing (TD) population. The search for the neural underpinnings of individual differences is important not only philosophically, but also in the service of precision medicine. We approach these questions using a three-pronged approach. First, we create a battery of behavioral tasks from which we can calculate objective measures for different aspects of the behaviors of interest, with sufficient variance across the TD population. Second, using these individual differences in behavior, we identify the neural variance which explains the behavioral variance at the network level. Finally, using covert neurofeedback, we perturb the networks hypothesized to correspond to each of these components, thus directly testing their casual contribution. I will discuss our overall approach, as well as a few of the new directions we are currently pursuing.

Filmmakers and editors have empirically developed techniques to ensure the spatiotemporal continuity of a film's narration. In terms of time, editing techniques (e.g., elliptical, overlapping, or cut minimization) allow for the manipulation of the perceived duration of events as they unfold on screen. More specifically, a scene can be edited to be time compressed, expanded, or real-time in terms of its perceived duration. Despite the consistent application of these techniques in filmmaking, their perceptual outcomes have not been experimentally validated. Given that viewing a film is experienced as a precise simulation of the physical world, the use of cinematic material to examine aspects of time perception allows for experimentation with high ecological validity, while filmmakers gain more insight on how empirically developed techniques influence viewers' time percept. Here, we investigated how such time manipulation techniques of an action affect a scene's perceived duration. Specifically, we presented videos depicting different actions (e.g., a woman talking on the phone), edited according to the techniques applied for temporal manipulation and asked participants to make verbal estimations of the presented scenes' perceived durations. Analysis of data revealed that the duration of expanded scenes was significantly overestimated as compared to that of compressed and real-time scenes, as was the duration of real-time scenes as compared to that of compressed scenes. Therefore, our results validate the empirical techniques applied for the modulation of a scene's perceived duration. We also found interactions on time estimates of scene type and editing technique as a function of the characteristics and the action of the scene presented. Thus, these findings add to the discussion that the content and characteristics of a scene, along with the editing technique applied, can also modulate perceived duration. Our findings are discussed by considering current timing frameworks, as well as attentional saliency algorithms measuring the visual saliency of the presented stimuli.

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.

Perhaps the most impressive aspect of the way the brain enables us to act on the sensory world is its flexibility. We can make a general inference about many sensory features (rating the ripeness of mangoes or avocados) and map a single stimulus onto many choices (slicing or blending mangoes). These can be thought of as flexibly mapping many (features) to one (inference) and one (feature) to many (choices) sensory inputs to actions. Both theoretical and experimental investigations of this sort of flexible sensorimotor mapping tend to treat sensory areas as relatively static. Models typically instantiate flexibility through changing interactions (or weights) between units that encode sensory features and those that plan actions. Experimental investigations often focus on association areas involved in decision-making that show pronounced modulations by cognitive processes. I will present evidence that the flexible formatting of visual information in visual cortex can support both generalized inference and choice mapping. Our results suggest that visual cortex mediates many forms of cognitive flexibility that have traditionally been ascribed to other areas or mechanisms. Further, we find that a primary difference between visual and putative decision areas is not what information they encode, but how that information is formatted in the responses of neural populations, which is related to difference in the impact of causally manipulating different areas on behavior. This scenario allows for flexibility in the mapping between stimuli and behavior while maintaining stability in the information encoded in each area and in the mappings between groups of neurons.

Astrocytes are multifunctional glial cells, implicated in neurogenesis and synaptogenesis, supporting and fine-tuning neuronal activity and maintaining brain homeostasis by controlling blood-brain barrier permeability. During the last years a number of studies have shown that astrocytes can also be converted into neurons if they force-express neurogenic transcription factors or miRNAs. Direct astrocytic reprogramming to induced-neurons (iNs) is a powerful approach for manipulating cell fate, as it takes advantage of the intrinsic neural stem cell (NSC) potential of brain resident reactive astrocytes. To this end, astrocytic cell fate conversion to iNs has been well-established in vitro and in vivo using combinations of transcription factors (TFs) or chemical cocktails. Challenging the expression of lineage-specific TFs is accompanied by changes in the expression of miRNAs, that post-transcriptionally modulate high numbers of neurogenesis-promoting factors and have therefore been introduced, supplementary or alternatively to TFs, to instruct direct neuronal reprogramming. The neurogenic miRNA miR-124 has been employed in direct reprogramming protocols supplementary to neurogenic TFs and other miRNAs to enhance direct neurogenic conversion by suppressing multiple non-neuronal targets. In our group we aimed to investigate whether miR-124 is sufficient to drive direct reprogramming of astrocytes to induced-neurons (iNs) on its own both in vitro and in vivo and elucidate its independent mechanism of reprogramming action. Our in vitro data indicate that miR-124 is a potent driver of the reprogramming switch of astrocytes towards an immature neuronal fate. Elucidation of the molecular pathways being triggered by miR-124 by RNA-seq analysis revealed that miR-124 is sufficient to instruct reprogramming of cortical astrocytes to immature induced-neurons (iNs) in vitro by down-regulating genes with important regulatory roles in astrocytic function. Among these, the RNA binding protein Zfp36l1, implicated in ARE-mediated mRNA decay, was found to be a direct target of miR-124, that be its turn targets neuronal-specific proteins participating in cortical development, which get de-repressed in miR-124-iNs. Furthermore, miR-124 is potent to guide direct neuronal reprogramming of reactive astrocytes to iNs of cortical identity following cortical trauma, a novel finding confirming its robust reprogramming action within the cortical microenvironment under neuroinflammatory conditions. In parallel to their reprogramming properties, astrocytes also participate in the maintenance of blood-brain barrier integrity, which ensures the physiological functioning of the central nervous system and gets affected contributing to the pathology of several neurodegenerative diseases. To study in real time the dynamic physical interactions of astrocytes with brain vasculature under homeostatic and pathological conditions, we performed 2-photon brain intravital imaging in a mouse model of systemic neuroinflammation, known to trigger astrogliosis and microgliosis and to evoke changes in astrocytic contact with brain vasculature. Our in vivo findings indicate that following neuroinflammation the endfeet of activated perivascular astrocytes lose their close proximity and physiological cross-talk with vasculature, however this event is at compensated by the cross-talk of astrocytes with activated microglia, safeguarding blood vessel coverage and maintenance of blood-brain integrity.

Most brain functions involve interactions among multiple, distinct areas or nuclei. Yet our understanding of how populations of neurons in interconnected brain areas communicate is in its infancy. Using a population approach, we found that interactions between early visual cortical areas (V1 and V2) occur through a low-dimensional bottleneck, termed a communication subspace. In this talk, I will focus on the statistical methods we have developed for studying interactions between brain areas. First, I will describe Delayed Latents Across Groups (DLAG), designed to disentangle concurrent, bi-directional (i.e., feedforward and feedback) interactions between areas. Second, I will describe an extension of DLAG applicable to three or more areas, and demonstrate its utility for studying simultaneous Neuropixels recordings in areas V1, V2, and V3. Our results provide a framework for understanding how neuronal population activity is gated and selectively routed across brain areas.

The adult intestine is a major barrier epithelium and coordinator of multi-organ functions. Stem cells constantly repair the intestinal epithelium by adjusting their proliferation and differentiation to tissue intrinsic as well as micro- and macro-environmental signals. How these signals integrate to control intestinal and whole-body homeostasis is largely unknown. Addressing this gap in knowledge is central to an improved understanding of intestinal pathophysiology and its systemic consequences. Combining Drosophila and mammalian model systems my laboratory has discovered fundamental mechanisms driving intestinal regeneration and tumourigenesis and outlined complex inter-organ signaling regulating health and disease. During my talk, I will discuss inter-related areas of research from my lab, including:1- Interactions between the intestine and its microenvironment influencing intestinal regeneration and tumourigenesis. 2- Long-range signals from the intestine impacting whole-body in health and disease.

When interacting with complex environments, humans can rapidly adapt their behavior to changes in task or context. To facilitate this adaptation, we often spend substantial periods of time contemplating possible futures before acting. For such planning to be rational, the benefits of planning to future behavior must at least compensate for the time spent thinking. Here we capture these features of human behavior by developing a neural network model where not only actions, but also planning, are controlled by prefrontal cortex. This model consists of a meta-reinforcement learning agent augmented with the ability to plan by sampling imagined action sequences drawn from its own policy, which we refer to as `rollouts'. Our results demonstrate that this agent learns to plan when planning is beneficial, explaining the empirical variability in human thinking times. Additionally, the patterns of policy rollouts employed by the artificial agent closely resemble patterns of rodent hippocampal replays recently recorded in a spatial navigation task, in terms of both their spatial statistics and their relationship to subsequent behavior. Our work provides a new theory of how the brain could implement planning through prefrontal-hippocampal interactions, where hippocampal replays are triggered by -- and in turn adaptively affect -- prefrontal dynamics.

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

Emotions and social interactions color our lives and shape our behaviors. Using animal models and engineered manipulations, we aim to understand how social and emotional behaviors are encoded in the brain, focusing on the neural circuits underlying dominance hierarchy and depression. This lecture will highlight our recent discoveries on how downward social mobility leads to depression; how ketamine tames depression by blocking burst firing in the brain’s antireward center; and, how glia-neuron interaction plays a surprising role in this process. I will also present our recent work on the mechanism underlying the sustained antidepressant activity of ketamine and its brain region specificity. With these results, we hope to illuminate on a more unified theory on ketamine’s mode of action and inspire new treatment strategies for depression.

Emotions and social interactions color our lives and shape our behaviors. Using animal models and engineered manipulations, we aim to understand how social and emotional behaviors are encoded in the brain, focusing on the neural circuits underlying dominance hierarchy and depression. This lecture will highlight our recent discoveries on how downward social mobility leads to depression; how ketamine tames depression by blocking burst firing in the brain’s antireward center; and, how glia-neuron interaction plays a surprising role in this process. I will also present our recent work on the mechanism underlying the sustained antidepressant activity of ketamine and its brain region specificity. With these results, we hope to illuminate on a more unified theory on ketamine’s mode of action and inspire new treatment strategies for depression.

Despite her still poor visual acuity and minimal visual experience, a 2-3 month old baby will reliably respond to facial expressions, smiling back at her caretaker or older sibling. But what if that same baby had been deprived of her early visual experience? Will she be able to appropriately respond to seemingly mundane interactions, such as a peer’s facial expression, if she begins seeing at the age of 10? My work is part of Project Prakash, a dual humanitarian/scientific mission to identify and treat curably blind children in India and then study how their brain learns to make sense of the visual world when their visual journey begins late in life. In my talk, I will give a brief overview of Project Prakash, and present findings from one of my primary lines of research: plasticity of face perception with late sight onset. Specifically, I will discuss a mixed methods effort to probe and explain the differential windows of plasticity that we find across different aspects of distributed face recognition, from distinguishing a face from a nonface early in the developmental trajectory, to recognizing facial expressions, identifying individuals, and even identifying one’s own caretaker. I will draw connections between our empirical findings and our recent theoretical work hypothesizing that children with late sight onset may suffer persistent face identification difficulties because of the unusual acuity progression they experience relative to typically developing infants. Finally, time permitting, I will point to potential implications of our findings in supporting newly-sighted children as they transition back into society and school, given that their needs and possibilities significantly change upon the introduction of vision into their lives.

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

On a fast timescale, neurons mostly interact by short, stereotypical electrical impulses or spikes. Why? A common answer is that spikes are useful for long-distance communication, to avoid alterations while traveling along axons. But as it turns out, spikes are seen in many places outside neurons: in the heart, in muscles, in plants and even in protists. From these examples, it appears that action potentials mediate some form of coordinated action, a timed event. From this perspective, spikes should not be seen simply as noisy implementations of underlying continuous signals (a sort of analog-to-digital conversion), but rather as events or actions. I will give a number of examples of functional spike-based interactions in living systems.

When interacting with complex environments, humans can rapidly adapt their behavior to changes in task or context. To facilitate this adaptation, we often spend substantial periods of time contemplating possible futures before acting. For such planning to be rational, the benefits of planning to future behavior must at least compensate for the time spent thinking. Here we capture these features of human behavior by developing a neural network model where not only actions, but also planning, are controlled by prefrontal cortex. This model consists of a meta-reinforcement learning agent augmented with the ability to plan by sampling imagined action sequences drawn from its own policy, which we refer to as 'rollouts'. Our results demonstrate that this agent learns to plan when planning is beneficial, explaining the empirical variability in human thinking times. Additionally, the patterns of policy rollouts employed by the artificial agent closely resemble patterns of rodent hippocampal replays recently recorded in a spatial navigation task, in terms of both their spatial statistics and their relationship to subsequent behavior. Our work provides a new theory of how the brain could implement planning through prefrontal-hippocampal interactions, where hippocampal replays are triggered by - and in turn adaptively affect - prefrontal dynamics.

Neural activity is often described in terms of population-level factors extracted from the responses of many neurons. Factors provide a lower-dimensional description with the aim of shedding light on network computations. Yet, mechanistically, computations are performed not by continuously valued factors but by interactions among neurons that spike discretely and variably. Models provide a means of bridging these levels of description. We developed a general method for training model networks of spiking neurons by leveraging factors extracted from either data or firing-rate-based networks. In addition to providing a useful model-building framework, this formalism illustrates how reliable and continuously valued factors can arise from seemingly stochastic spiking. Our framework establishes procedures for embedding this property in network models with different levels of realism. The relationship between spikes and factors in such networks provides a foundation for interpreting (and subtly redefining) commonly used quantities such as firing rates.

Humans are natural teachers; learning through instruction is one of the most fundamental ways that we learn. Interactive Task Learning (ITL) is an emerging research agenda that studies the design of complex intelligent robots that can acquire new knowledge through natural human teacher-robot learner interactions. ITL methods are particularly useful for designing intelligent robots whose behavior can be adapted by humans collaborating with them. In this talk, I will summarize our recent findings on the structure that human instruction naturally has and motivate an intelligent system design that can exploit their structure. The system – AILEEN – is being developed using the common model of cognition. Architectures that implement the Common Model of Cognition - Soar, ACT-R, and Sigma - have a prominent place in research on cognitive modeling as well as on designing complex intelligent agents. However, they miss a critical piece of intelligent behavior – analogical reasoning and generalization. I will introduce a new memory – concept memory – that integrates with a common model of cognition architecture and supports ITL.

In the central nervous system, neuronal activity is a critical regulator of development and plasticity. Activity-dependent proliferation of healthy glial progenitors, oligodendrocyte precursor cells (OPCs), and the consequent generation of new oligodendrocytes contributes to adaptive myelination. This plasticity of myelin tunes neural circuit function and contributes to healthy cognition. The robust mitogenic effect of neuronal activity on normal oligodendroglial precursor cells, a putative cellular origin for many forms of glioma, suggests that dysregulated or “hijacked” mechanisms of myelin plasticity might similarly promote malignant cell proliferation in this devastating group of brain cancers. Indeed, neuronal activity promotes progression of both high-grade and low-grade glioma subtypes in preclinical models. Crucial mechanisms mediating activity-regulated glioma growth include paracrine secretion of BDNF and the synaptic protein neuroligin-3 (NLGN3). NLGN3 induces multiple oncogenic signaling pathways in the cancer cell, and also promotes glutamatergic synapse formation between neurons and glioma cells. Glioma cells integrate into neural circuits synaptically through neuron-to-glioma synapses, and electrically through potassium-evoked currents that are amplified through gap-junctional coupling between tumor cells This synaptic and electrical integration of glioma into neural circuits is central to tumor progression in preclinical models. Thus, neuron-glial interactions not only modulate neural circuit structure and function in the healthy brain, but paracrine and synaptic neuron-glioma interactions also play important roles in the pathogenesis of glial cancers. The mechanistic parallels between normal and malignant neuron-glial interactions underscores the extent to which mechanisms of neurodevelopment and plasticity are subverted by malignant gliomas, and the importance of understanding the neuroscience of cancer.

Humans are very good at visually recognizing materials and inferring their properties. Without touching surfaces, we can usually tell what they would feel like, and we enjoy vivid visual intuitions about how they typically behave. This is impressive because the retinal image that the visual system receives as input is the result of complex interactions between many physical processes. Somehow the brain has to disentangle these different factors. I will present some recent work in which we show that an unsupervised neural network trained on images of surfaces spontaneously learns to disentangle reflectance, lighting and shape. However, the disentanglement is not perfect, and we find that as a result the network not only predicts the broad successes of human gloss perception, but also the specific pattern of errors that humans exhibit on an image-by-image basis. I will argue this has important implications for thinking about appearance and vision more broadly.

The transit of food through the gastrointestinal tract is critical for nutrient absorption and survival, and the gastrointestinal tract has the ability to initiate motility reflexes triggered by luminal distention. This complex function depends on the crosstalk between extrinsic and intrinsic neuronal innervation within the intestine, as well as local specialized enteroendocrine cells. However, the molecular mechanisms and the subset of sensory neurons underlying the initiation and regulation of intestinal motility remain largely unknown. Here, we show that humans lacking PIEZO2 exhibit impaired bowel sensation and motility. Piezo2 in mouse dorsal root but not nodose ganglia is required to sense gut content, and this activity slows down food transit rates in the stomach, small intestine, and colon. Indeed, Piezo2 is directly required to detect colon distension in vivo. Our study unveils the mechanosensory mechanisms that regulate the transit of luminal contents throughout the gut, which is a critical process to ensure proper digestion, nutrient absorption, and waste removal. These findings set the foundation of future work to identify the highly regulated interactions between sensory neurons, enteric neurons and non- neuronal cells that control gastrointestinal motility.

Since Darwin, comparative research has shown that most animals share basic timing capacities, such as the ability to process temporal regularities and produce rhythmic behaviors. What seems to be more exclusive, however, are the capacities to generate temporal predictions and to display anticipatory behavior at salient time points. These abilities are associated with subcortical structures like basal ganglia (BG) and cerebellum (CE), which are more developed in humans as compared to nonhuman animals. In the first research line, we investigated the basic capacities to extract temporal regularities from the acoustic environment and produce temporal predictions. We did so by adopting a comparative and translational approach, thus making use of a unique EEG dataset including 2 macaque monkeys, 20 healthy young, 11 healthy old participants and 22 stroke patients, 11 with focal lesions in the BG and 11 in the CE. In the second research line, we holistically explore the functional relevance of body-brain physiological interactions in human behavior. Thus, a series of planned studies investigate the functional mechanisms by which body signals (e.g., respiratory and cardiac rhythms) interact with and modulate neurocognitive functions from rest and sleep states to action and perception. This project supports the effort towards individual profiling: are individuals’ timing capacities (e.g., rhythm perception and production), and general behavior (e.g., individual walking and speaking rates) influenced / shaped by body-brain interactions?

Temporal predictions are fundamental instruments for facilitating sensory selection, allowing humans to exploit regularities in the world. Recent evidence indicates that the motor system instantiates predictive timing mechanisms, helping to synchronize temporal fluctuations of attention with the timing of events in a task-relevant stream, thus facilitating sensory selection. Accordingly, in the auditory domain auditory-motor interactions are observed during perception of speech and music, two temporally structured sensory streams. I will present a behavioral and neurophysiological account for this theory and will detail the parameters governing the emergence of this auditory-motor coupling, through a set of behavioral and magnetoencephalography (MEG) experiments.

Brains can gracefully weed out irrelevant stimuli to guide behavior. This feat is believed to rely on a progressive selection of task-relevant stimuli across the cortical hierarchy, but the specific across-area interactions enabling stimulus selection are still unclear. Here, we propose that population gating, occurring within A1 but controlled by top-down inputs from mPFC, can support across-area stimulus selection. Examining single-unit activity recorded while rats performed an auditory context-dependent task, we found that A1 encoded relevant and irrelevant stimuli along a common dimension of its neural space. Yet, the relevant stimulus encoding was enhanced along an extra dimension. In turn, mPFC encoded only the stimulus relevant to the ongoing context. To identify candidate mechanisms for stimulus selection within A1, we reverse-engineered low-rank RNNs trained on a similar task. Our analyses predicted that two context-modulated neural populations gated their preferred stimulus in opposite contexts, which we confirmed in further analyses of A1. Finally, we show in a two-region RNN how population gating within A1 could be controlled by top-down inputs from PFC, enabling flexible across-area communication despite fixed inter-areal connectivity.

Visual perception is traditionally studied in isolation from other sensory systems, and while this approach has been exceptionally successful, in the real world, visual objects are often accompanied by sounds, smells, tactile information, or taste. How is visual processing influenced by these other sensory inputs? In this talk, I will review studies from our lab showing that a sound can influence the perception of a visual object in multiple ways. In the first part, I will focus on spatial interactions between sound and sight, demonstrating that co-localized sounds enhance visual perception. Then, I will show that these cross-modal interactions also occur at a higher contextual and semantic level, where naturalistic sounds facilitate the processing of real-world objects that match these sounds. Throughout my talk I will explore to what extent sounds not only improve visual processing but also alter perceptual representations of the objects we see. Most broadly, I will argue for the importance of considering multisensory influences on visual perception for a more complete understanding of our visual experience.

In this talk, we propose to decipher the activity of neural networks via a “multiply and conquer” approach. This approach considers limit networks made of infinitely many replicas with the same basic neural structure. The key point is that these so-called replica-mean-field networks are in fact simplified, tractable versions of neural networks that retain important features of the finite network structure of interest. The finite size of neuronal populations and synaptic interactions is a core determinant of neural dynamics, being responsible for non-zero correlation in the spiking activity and for finite transition rates between metastable neural states. Theoretically, we develop our replica framework by expanding on ideas from the theory of communication networks rather than from statistical physics to establish Poissonian mean-field limits for spiking networks. Computationally, we leverage our original replica approach to characterize the stationary spiking activity of various network models via reduction to tractable functional equations. We conclude by discussing perspectives about how to use our replica framework to probe nontrivial regimes of spiking correlations and transition rates between metastable neural states.

Drosophila olfactory sensory hairs ("sensilla") typically house two olfactory receptor neurons (ORNs) which can laterally inhibit each other via electrical ("ephaptic") coupling. ORN pairing is highly stereotyped and genetically determined. Thus, olfactory signals arriving in the Antennal Lobe (AL) have been pre-processed by a fixed and shallow network at the periphery. To uncover the functional significance of this organization, we developed a nonlinear phenomenological model of asymmetrically coupled ORNs responding to odor mixture stimuli. We derived an analytical solution to the ORNs’ dynamics, which shows that the peripheral network can extract the valence of specific odor mixtures via transient amplification. Our model predicts that for efficient read-out of the amplified valence signal there must exist specific patterns of downstream connectivity that reflect the organization at the periphery. Analysis of AL→Lateral Horn (LH) fly connectomic data reveals evidence directly supporting this prediction. We further studied the effect of ephaptic coupling on olfactory processing in the AL→Mushroom Body (MB) pathway. We show that stereotyped ephaptic interactions between ORNs lead to a clustered odor representation of glomerular responses. Such clustering in the AL is an essential assumption of theoretical studies on odor recognition in the MB. Together our work shows that preprocessing of olfactory stimuli by a fixed and shallow network increases sensitivity to specific odor mixtures, and aids in the learning of novel olfactory stimuli. Work led by Palka Puri, in collaboration with Chih-Ying Su and Shiuan-Tze Wu.

How seizures emerge from the abnormal dynamics of neural networks within the epileptogenic tissue remains an enigma. Are seizures random events, or do detectable changes in brain dynamics precede them? Are mechanisms of seizure emergence identical at the onset and later stages of epilepsy? Is the risk of seizure occurrence stable, or does it change over time? A myriad of questions about seizure genesis remains to be answered to understand the core principles governing seizure genesis. The last decade has brought unprecedented insights into the complex nature of seizure emergence. It is now believed that seizure onset represents the product of the interactions between the process of a transition to seizure, long-term fluctuations in seizure susceptibility, epileptogenesis, and disease progression. During the lecture, we will review the latest observations about mechanisms of ictogenesis operating at multiple temporal scales. We will show how the latest observations contribute to the formation of a comprehensive theory of seizure genesis, and challenge the traditional perspectives on ictogenesis. Finally, we will discuss how combining conventional approaches with computational modeling, modern techniques of in vivo imaging, and genetic manipulation open prospects for exploration of yet hidden mechanisms of seizure genesis.

Astroglia-neuron interactions are involved in multiple processes, regulating development, excitability and connectivity of neural circuits. Accumulating number of evidences highlight a direct connection between aberrant astroglial genetics and physiology in various forms of epilepsies. Using zebrafish seizure models, we showed that neurons and astroglia follow different spatiotemporal dynamics during transitions from pre-ictal to ictal activity. We observed that during pre-ictal period neurons exhibit local synchrony and low level of activity, whereas astroglia exhibit global synchrony and high-level of calcium signals that are anti correlated with neural activity. Instead, generalized seizures are marked by a massive release of astroglial glutamate release as well as a drastic increase of astroglia and neuronal activity and synchrony across the entire brain. Knocking out astroglial glutamate transporters leads to recurrent spontaneous generalized seizures accompanied with massive astroglial glutamate release. We are currently using a combination of genetic and pharmacological approaches to perturb astroglial glutamate signalling and astroglial gap junctions to further investigate their role in generation and spreading of epileptic seizures across the brain.

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) 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.

Prior studies of A:B::C:D verbal analogies have identified several factors that affect performance, including the semantic similarity between source and target domains (semantic distance), the semantic association between the C-term and incorrect answers (distracter salience), and the type of relations between word pairs (e.g., categorical, compositional, and causal). However, it is unclear how these stimulus properties affect performance when utilized together. Moreover, how do these item factors interact with individual differences such as crystallized intelligence and creative thinking? Several studies reveal interactions among these item and individual difference factors impacting verbal analogy performance. For example, a three-way interaction demonstrated that the effects of semantic distance and distracter salience had a greater impact on performance for compositional and causal relations than for categorical ones (Jones, Kmiecik, Irwin, & Morrison, 2022). Implications for analogy theories and future directions are discussed.

The David Smith Lectures in Anatomical Neuropharmacology, Part of the 'Pharmacology, Anatomical Neuropharmacology and Drug Discovery Seminars Series', Department of Pharmacology, University of Oxford. The 15th David Smith Award Lecture in Anatomical Neuropharmacology will be delivered by Professor Tim Bliss, Visiting Professor at UCL and the Frontier Institutes of Science and Technology, Xi’an Jiaotong University, China, and is hosted by Professor Nigel Emptage. This award lecture was set up to celebrate the vision of Professor A David Smith, namely, that explanations of the action of drugs on the brain requires the definition of neuronal circuits, the location and interactions of molecules. Tim Bliss gained his PhD at McGill University in Canada. He joined the MRC National Institute for Medical Research in Mill Hill, London in 1967, where he remained throughout his career. His work with Terje Lømo in the late 1960’s established the phenomenon of long-term potentiation (LTP) as the dominant synaptic model of how the mammalian brain stores memories. He was elected as a Fellow of the Royal Society in 1994 and is a founding fellow of the Academy of Medical Sciences. He shared the Bristol Myers Squibb award for Neuroscience with Eric Kandel in 1991, the Ipsen Prize for Neural Plasticity with Richard Morris and Yadin Dudai in 2013. In May 2012 he gave the annual Croonian Lecture at the Royal Society on ‘The Mechanics of Memory’. In 2016 Tim, with Graham Collingridge and Richard Morris shared the Brain Prize, one of the world's most coveted science prizes. Abstract: In 1966 there appeared in Acta Physiologica Scandinavica an abstract of a talk given by Terje Lømo, a PhD student in Per Andersen’s laboratory at the University of Oslo. In it Lømo described the long-lasting potentiation of synaptic responses in the dentate gyrus of the anaesthetised rabbit that followed repeated episodes of 10-20Hz stimulation of the perforant path. Thus, heralded and almost entirely unnoticed, one of the most consequential discoveries of 20th century neuroscience was ushered into the world. Two years later I arrived in Oslo as a visiting post-doc from the National Institute for Medical Research in Mill Hill, London. In this talk I recall the events that led us to embark on a systematic reinvestigation of the phenomenon now known as long-term potentiation (LTP) and will then go on to describe the discoveries and controversies that enlivened the early decades of research into synaptic plasticity in the mammalian brain. I will end with an observer’s view of the current state of research in the field, and what we might expect from it in the future.