Different brain cell types exhibit distinct metabolic signatures that link energy economy to cellular function. Astrocytes and neurons, for instance, diverge dramatically in their reliance on glycolysis versus oxidative phosphorylation, underscoring that metabolic fuel efficiency is not uniform across cell types. A key factor shaping this divergence is the structural organization of the mitochondrial respiratory chain into supercomplexes. Specifically, complexes I (CI) and III (CIII) form a CI–CIII supercomplex, but the degree of this assembly varies by cell type. In neurons, CI is predominantly integrated into supercomplexes, resulting in highly efficient mitochondrial respiration and minimal reactive oxygen species (ROS) generation. Conversely, in astrocytes, a larger fraction of CI remains unassembled, freely existing apart from CIII, leading to reduced respiratory efficiency and elevated mitochondrial ROS production. Despite this apparent inefficiency, astrocytes boast a highly adaptable metabolism capable of responding to diverse stressors. Their looser CI–CIII organization allows for flexible ROS signaling, which activates antioxidant programs via transcription factors like Nrf2. This modular architecture enables astrocytes not only to balance energy production but also to support neuronal health and influence complex organismal behaviors.

The focus of this talk is not about my research in AI or Robotics but my own journey on trying to do research and understand intelligence in a rapidly evolving research landscape. I will trace my path from conducting early-stage research during graduate school, to working on practical solutions within a startup environment, and finally to my current role where I participate in more structured research at a major tech company. Through these varied experiences, I will provide different perspectives on research and talk about how my core beliefs on intelligence have changed and sometimes even been compromised. There are no lessons to be learned from my stories, but hopefully they will be entertaining.

n the neurosciences the need for some 'overarching' theory is sometimes expressed, but it is not always obvious what is meant by this. One can perhaps agree that in modern science observation and experimentation is normally complemented by 'theory', i.e. the development of theoretical concepts that help guiding and evaluating experiments and measurements. A deeper discussion of 'brain theory' will require the clarification of some further distictions, in particular: theory vs. model and brain research (and its theory) vs. neuroscience. Other questions are: Does a theory require mathematics? Or even differential equations? Today it is often taken for granted that the whole universe including everything in it, for example humans, animals, and plants, can be adequately treated by physics and therefore theoretical physics is the overarching theory. Even if this is the case, it has turned out that in some particular parts of physics (the historical example is thermodynamics) it may be useful to simplify the theory by introducing additional theoretical concepts that can in principle be 'reduced' to more complex descriptions on the 'microscopic' level of basic physical particals and forces. In this sense, brain theory may be regarded as part of theoretical neuroscience, which is inside biophysics and therefore inside physics, or theoretical physics. Still, in neuroscience and brain research, additional concepts are typically used to describe results and help guiding experimentation that are 'outside' physics, beginning with neurons and synapses, names of brain parts and areas, up to concepts like 'learning', 'motivation', 'attention'. Certainly, we do not yet have one theory that includes all these concepts. So 'brain theory' is still in a 'pre-newtonian' state. However, it may still be useful to understand in general the relations between a larger theory and its 'parts', or between microscopic and macroscopic theories, or between theories at different 'levels' of description. This is what I plan to do.

Gaze shifts in humans serve to direct high-resolution vision provided by the fovea towards areas in the environment. Gaze can be considered a proxy for attention or indicator of the relative importance of different parts of the environment. In this talk, we discuss the development of generative models of human gaze in response to visual input. We discuss how such models can be learned, both using supervised learning and using implicit feedback as an agent interacts with the environment, the latter being more plausible in biological agents. We also discuss two ways such models can be used. First, they can be used to improve the performance of artificial autonomous systems, in applications such as autonomous navigation. Second, because these models are contingent on the human’s task, goals, and/or state in the context of the environment, observations of gaze can be used to infer information about user intent. This information can be used to improve human-machine and human robot interaction, by making interfaces more anticipative. We discuss example applications in gaze-typing, robotic tele-operation and human-robot interaction.

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.

The human medial temporal lobe contains neurons that respond selectively to the semantic contents of a presented stimulus. These "concept cells" may respond to very different pictures of a given person and even to their written or spoken name. Their response latency is far longer than necessary for object recognition, they follow subjective, conscious perception, and they are found in brain regions that are crucial for declarative memory formation. It has thus been hypothesized that they may represent the semantic "building blocks" of episodic memories. In this talk I will present data from single unit recordings in the hippocampus, entorhinal cortex, parahippocampal cortex, and amygdala during paradigms involving object recognition and conscious perception as well as encoding of episodic memories in order to characterize the role of concept cells in these cognitive functions.

The primary visual cortex (V1) directly projects to the superior colliculus (SC) and is believed to provide sensory drive for eye movements. Consistent with this, a majority of saccade-related SC neurons also exhibit short-latency, stimulus-driven visual responses, which are additionally feature-tuned. However, direct neurophysiological comparisons of the visual response properties of the two anatomically-connected brain areas are surprisingly lacking, especially with respect to active looking behaviors. I will describe a series of experiments characterizing visual response properties in primate V1 and SC neurons, exploring feature dimensions like visual field location, spatial frequency, orientation, contrast, and luminance polarity. The results suggest a substantial, qualitative reformatting of SC visual responses when compared to V1. For example, SC visual response latencies are actively delayed, independent of individual neuron tuning preferences, as a function of increasing spatial frequency, and this phenomenon is directly correlated with saccadic reaction times. Such “coarse-to-fine” rank ordering of SC visual response latencies as a function of spatial frequency is much weaker in V1, suggesting a dissociation of V1 responses from saccade timing. Consistent with this, when we next explored trial-by-trial correlations of individual neurons’ visual response strengths and visual response latencies with saccadic reaction times, we found that most SC neurons exhibited, on a trial-by-trial basis, stronger and earlier visual responses for faster saccadic reaction times. Moreover, these correlations were substantially higher for visual-motor neurons in the intermediate and deep layers than for more superficial visual-only neurons. No such correlations existed systematically in V1. Thus, visual responses in SC and V1 serve fundamentally different roles in active vision: V1 jumpstarts sensing and image analysis, but SC jumpstarts moving. I will finish by demonstrating, using V1 reversible inactivation, that, despite reformatting of signals from V1 to the brainstem, V1 is still a necessary gateway for visually-driven oculomotor responses to occur, even for the most reflexive of eye movement phenomena. This is a fundamental difference from rodent studies demonstrating clear V1-independent processing in afferent visual pathways bypassing the geniculostriate one, and it demonstrates the importance of multi-species comparisons in the study of oculomotor control.

The brain has a highly complex structure in terms of cell types and wiring between different regions. What is it for, if anything? I'll start this talk by asking what might an answer to this question even look like given that we can't run an alternative universe where our brains are structured differently. (Preview: we can do this with models!) I'll then talk about some of our work in two areas: (1) does the modular structure of the brain contribute to specialisation of function? (2) how do different cell types and architectures contribute to multimodal sensory processing?

Memories of emotionally-salient events are long-lasting, guiding behavior from minutes to years after learning. The prelimbic cortex (PL) is required for fear memory retrieval across time and is densely interconnected with many subcortical and cortical areas involved in recent and remote memory recall, including the temporal association area (TeA). While the behavioral expression of a memory may remain constant over time, the neural activity mediating memory-guided behavior is dynamic. In PL, different neurons underlie recent and remote memory retrieval and remote memory-encoding neurons have preferential functional connectivity with cortical association areas, including TeA. TeA plays a preferential role in remote compared to recent memory retrieval, yet how TeA circuits drive remote memory retrieval remains poorly understood. Here we used a combination of activity-dependent neuronal tagging, viral circuit mapping and miniscope imaging to investigate the role of the PL-TeA circuit in fear memory retrieval across time in mice. We show that PL memory ensembles recruit PL-TeA neurons across time, and that PL-TeA neurons have enhanced encoding of salient cues and behaviors at remote timepoints. This recruitment depends upon ongoing synaptic activity in the learning-activated PL ensemble. Our results reveal a novel circuit encoding remote memory and provide insight into the principles of memory circuit reorganization across time.

The centre of memory is the medial temporal lobe (MTL) and especially the hippocampus. In our research, a more flexible brain-inspired computational microcircuit of the CA1 region of the mammalian hippocampus was upgraded and used to examine how information retrieval could be affected under different conditions. Six models (1-6) were created by modulating different excitatory and inhibitory pathways. The results showed that the increase in the strength of the feedforward excitation was the most effective way to recall memories. In other words, that allows the system to access stored memories more accurately.

Over the past decade, neural representations have been studied from the lens of low-dimensional subspaces defined by the co-activation of neurons. However, this view has overlooked other forms of covarying structure in neural activity, including i) condition-specific high-dimensional neural sequences, and ii) representations that change over time due to learning or drift. In this talk, I will present a new framework that extends the classic view towards additional types of covariability that are not constrained to a fixed, low-dimensional subspace. In addition, I will present sliceTCA, a new tensor decomposition that captures and demixes these different types of covariability to reveal task-relevant structure in neural activity. Finally, I will close with some thoughts regarding the circuit mechanisms that could generate mixed covariability. Together this work points to a need to consider new possibilities for how neural populations encode sensory, cognitive, and behavioral variables beyond neural subspaces.

Our ability to recognize an object amongst many others is one of the most important features of the human mind. However, object recognition requires tremendous computational effort, as we need to solve a complex and recursive environment with ease and proficiency. This challenging feat is dependent on the implementation of an effective organization of knowledge in the brain. Here I put forth a novel understanding of how object knowledge is organized in the brain, by proposing that the organization of object knowledge follows key object-related dimensions, analogously to how sensory information is organized in the brain. Moreover, I will also put forth that this knowledge is topographically laid out in the cortical surface according to these object-related dimensions that code for different types of representational content – I call this contentopic mapping. I will show a combination of fMRI and behavioral data to support these hypotheses and present a principled way to explore the multidimensionality of object processing.

Raman spectroscopy is a powerful technique for identifying chemical species by probing their vibrational energy levels, offering exceptional specificity with a relatively simple setup involving a laser source, spectrometer, and microscope/probe. However, the high cost of Raman systems lacking modularity often limits exploratory research hindering broader adoption. To address the need for an affordable, modular microscopy platform for multimodal imaging, we present a customizable confocal Raman spectroscopy setup alongside an open-source acquisition software, ORM (Open Raman Microscopy) Controller, developed in Python. This solution bridges the gap between expensive commercial systems and complex, custom-built setups used by specialist research groups. In this presentation, we will cover the components of the setup, the design rationale, assembly methods, limitations, and its modular potential for expanding functionality. Additionally, we will demonstrate ORM’s capabilities for instrument control, 2D and 3D Raman mapping, region-of-interest selection, and its adaptability to various instrument configurations. We will conclude by showcasing practical applications of this setup across different research fields.

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.

Biological agents continually reconcile the internal states of their brain circuits with incoming sensory and environmental evidence to evaluate when and how to act. The brains of biological agents, including animals and humans, exploit many evolutionary innovations, chiefly modularity—observable at the level of anatomically-defined brain regions, cortical layers, and cell types among others—that can be repurposed in a compositional manner to endow the animal with a highly flexible behavioral repertoire. Accordingly, their behaviors show their own modularity, yet such behavioral modules seldom correspond directly to traditional notions of modularity in brains. It remains unclear how to link neural and behavioral modularity in a compositional manner. We propose a comprehensive framework—compositional modes—to identify overarching compositionality spanning specialized submodules, such as brain regions. Our framework directly links the behavioral repertoire with distributed patterns of population activity, brain-wide, at multiple concurrent spatial and temporal scales. Using whole-brain recordings of zebrafish brains, we introduce an unsupervised pipeline based on neural network models, constrained by biological data, to reveal highly conserved compositional modes across individuals despite the naturalistic (spontaneous or task-independent) nature of their behaviors. These modes provided a scaffolding for other modes that account for the idiosyncratic behavior of each fish. We then demonstrate experimentally that compositional modes can be manipulated in a consistent manner by behavioral and pharmacological perturbations. Our results demonstrate that even natural behavior in different individuals can be decomposed and understood using a relatively small number of neurobehavioral modules—the compositional modes—and elucidate a compositional neural basis of behavior. This approach aligns with recent progress in understanding how reasoning capabilities and internal representational structures develop over the course of learning or training, offering insights into the modularity and flexibility in artificial and biological agents.

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.

Psychedelics like psilocybin have shown rapid and long-lasting efficacy on depressive and anxiety symptoms. Other psychedelics with shorter half-lives, such as DMT and 5-MeO-DMT, have also shown promising preliminary outcomes in major depression, making them interesting candidates for clinical practice. Despite several promising clinical studies, the influence of the context on therapeutic responses or adverse effects remains poorly documented. To address this, we conducted preclinical studies evaluating the psychopharmacological profile of 5-MeO-DMT in contexts previously validated in mice as either pleasant (positive setting) or aversive (negative setting). Healthy C57BL/6J male mice received a single intraperitoneal (i.p.) injection of 5-MeO-DMT at doses of 0.5, 5, and 10 mg/kg, with assessments at 2 hours, 24 hours, and one week post-administration. In a corticosterone (CORT) mouse model of depression, 5-MeO-DMT was administered in different settings, and behavioral tests mimicking core symptoms of depression and anxiety were conducted. In CORT-exposed mice, an acute dose of 0.5 mg/kg administered in a neutral setting produced antidepressant-like effects at 24 hours, as observed by reduced immobility time in the Tail Suspension Test (TST). In a positive setting, the drug also reduced latency to first immobility and total immobility time in the TST. However, these beneficial effects were negated in a negative setting, where 5-MeO-DMT failed to produce antidepressant-like effects and instead elicited an anxiogenic response in the Elevated Plus Maze (EPM).Our results indicate a strong influence of setting on the psychopharmacological profile of 5-MeO-DMT. Future experiments will examine cortical markers of pre- and post-synaptic density to correlate neuroplasticity changes with the behavioral effects of 5-MeO-DMT in different settings.

I my talk I will give an overview of recent research on deception and concealed information detection. I will start with a short introduction on the problems and shortcomings of traditional deception detection tools and why those still prevail in many recent approaches (e.g., in AI-based deception detection). I want to argue for the importance of more fundamental deception research and give some examples for insights gained therefrom. In the second part of the talk, I will introduce the Concealed Information Test (CIT), a promising paradigm for research and applied contexts to investigate whether someone actually recognizes information that they do not want to reveal. The CIT is based on solid scientific theory and produces large effects sizes in laboratory studies with a number of different measures (e.g., behavioral, psychophysiological, and neural measures). I will highlight some challenges a forensic application of the CIT still faces and how scientific research could assist in overcoming those.

Emotion is defined as a rapid psychological process involving experiential, expressive and physiological responses. These emerge following an appraisal process that involves cognitive evaluations of the environment assessing its relevance, implication, coping potential, and normative significance. It has been suggested that changes in appraisal processes lead to changes in the resulting emotional nature. Simultaneously, it was demonstrated that personality can be seen as a predisposition to feel more frequently certain emotions, but the personality-appraisal-emotional response chain is rarely fully investigated. The present project thus sought to investigate the extent to which personality traits influence certain appraisals, which in turn influence the subsequent emotional reactions via a systematic analysis of the link between personality traits of different current models, specific appraisals, and emotional response patterns at the experiential, expressive, and physiological levels. Major results include the coherence of emotion components clustering, and the centrality of the pleasantness, coping potential and consequences appraisals, in context; and the differentiated mediating role of cognitive appraisal in the relation between personality and the intensity and duration of an emotional state, and autonomic arousal, such as Extraversion-pleasantness-experience, and Neuroticism-powerlessness-arousal. Elucidating these relationships deepens our understanding of individual differences in emotional reactivity and spot routes of action on appraisal processes to modify upcoming adverse emotional responses, with a broader societal impact on clinical and non-clinical populations.

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.

Understanding and potentially reversing memory decline necessitates a comprehensive examination of memory's evolution throughout life. Traditional memory assessments, however, suffer from a lack of comparability across different age groups due to the diverse nature of the tests employed. Addressing this gap, our study introduces a novel, ACT-R model-based memory assessment designed to provide a consistent metric for evaluating memory function across a lifespan, from 5 to 85-year-olds. This approach allows for direct comparison across various tasks and materials tailored to specific age groups. Our findings reveal a pronounced U-shaped trajectory of long-term memory function, with performance at age 5 mirroring those observed in elderly individuals with impairments, highlighting critical periods of memory development and decline. Leveraging this unified assessment method, we further investigate the therapeutic potential of rs-fMRI-guided TBS targeting area 8AV in individuals with early-onset Alzheimer’s Disease—a region implicated in memory deterioration and mood disturbances in this population. This research not only advances our understanding of memory's lifespan dynamics but also opens new avenues for targeted interventions in Alzheimer’s Disease, marking a significant step forward in the quest to mitigate memory decay.

How does the dynamic shape of a sound affect its perceived microtiming? In the TIME project, we studied basic aspects of musical microrhythm, exploring both stimulus features and the participants’ enculturated expertise via perception experiments, observational studies of how musicians produce particular microrhythms, and ethnographic studies of musicians’ descriptions of microrhythm. Collectively, we show that altering the microstructure of a sound (“what” the sound is) changes its perceived temporal location (“when” it occurs). Specifically, there are systematic effects of core acoustic factors (duration, attack) on perceived timing. Microrhythmic features in longer and more complex sounds can also give rise to different perceptions of the same sound. Our results shed light on conflicting results regarding the effect of microtiming on the “grooviness” of a rhythm.

Inhibition has long been thought to stabilize the activity of cortical networks at low rates, and to shape significantly their response to sensory inputs. In this talk, I will describe three recent collaborative projects that shed light on these issues. (1) I will show how optogenetic excitation of inhibition neurons is consistent with cortex being inhibition stabilized even in the absence of sensory inputs, and how this data can constrain the coupling strengths of E-I cortical network models. (2) Recent analysis of the effects of optogenetic excitation of pyramidal cells in V1 of mice and monkeys shows that in some cases this optogenetic input reshuffles the firing rates of neurons of the network, leaving the distribution of rates unaffected. I will show how this surprising effect can be reproduced in sufficiently strongly coupled E-I networks. (3) Another puzzle has been to understand the respective roles of different inhibitory subtypes in network stabilization. Recent data reveal a novel, state dependent, paradoxical effect of weakening AMPAR mediated synaptic currents onto SST cells. Mathematical analysis of a network model with multiple inhibitory cell types shows that this effect tells us in which conditions SST cells are required for network stabilization.

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.

People are confronted every day with internal or external stimuli that can elicit emotions. In order to avoid negative ones, or to pursue individual aims, emotions are often regulated. The available emotion regulation strategies have been previously described as efficient or inefficient, but many studies highlighted that the strategies’ efficiency may be influenced by some different aspects such as personality. In this project, the efficiency of several strategies (e.g., reappraisal, suppression, distraction, …) has been studied according to personality profiles, by using the Big Five personality model and the Maladaptive Personality Trait Model. Moreover, the strategies’ efficiency has been tested according to the main emotional responses, namely experience, expressivity and physiological arousal. Results mainly highlighted the differential impact of strategies on individuals and a slight impact of personality. An important factor seems however to be the emotion parameter we are considering, potentially revealing a complex interplay between strategy, personality, and the considered emotion response. Based on these outcomes, further clinical aspects and recommendations will be also discussed.

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal, and abstract relationships that can be used to shape thought, planning, and behavior. Cognitive maps have been observed in the hippocampus, but their algorithmic form and the processes by which they are learned remain obscure. Here, we employed large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different versions of linear tracks in virtual reality. The results provide a detailed view of the formation of a cognitive map in the hippocampus. Throughout learning, both the animal behavior and hippocampal neural activity progressed through multiple intermediate stages, gradually revealing improved task representation that mirrored improved behavioral efficiency. The learning process led to progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent struture of the task. We show that a Hidden Markov Model (HMM) and a biologically plausible recurrent neural network trained using Hebbian learning can both capture core aspects of the learning dynamics and the orthogonalized representational structure in neural activity. In contrast, we show that gradient-based learning of sequence models such as Long Short-Term Memory networks (LSTMs) and Transformers do not naturally produce such orthogonalized representations. We further demonstrate that mice exhibited adaptive behavior in novel task settings, with neural activity reflecting flexible deployment of the state machine. These findings shed light on the mathematical form of cognitive maps, the learning rules that sculpt them, and the algorithms that promote adaptive behavior in animals. The work thus charts a course toward a deeper understanding of biological intelligence and offers insights toward developing more robust learning algorithms in artificial intelligence.

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.

Seizure suppression by deep brain stimulation (DBS) applies high frequency stimulation (HFS) to grey matter to block seizures. In this presentation, I will present the results of a different method that employs low frequency stimulation (LFS) (1 to 10Hz) of white matter tracts to prevent seizures. The approach has been shown to be effective in the hippocampus by stimulating the ventral and dorsal hippocampal commissure in both animal and human studies respectively for mesial temporal lobe seizures. A similar stimulation paradigm has been shown to be effective at controlling focal cortical seizures in rats with corpus callosum stimulation. This stimulation targets the axons of the corpus callosum innervating the focal zone at low frequencies (5 to 10Hz) and has been shown to significantly reduce both seizure and spike frequency. The mechanisms of this suppression paradigm have been elucidated with in-vitro studies and involve the activation of two long-lasting inhibitory potentials GABAB and sAHP. LFS mechanisms are similar in both hippocampus and cortical brain slices. Additionally, the results show that LFS does not block seizures but rather decreases the excitability of the tissue to prevent seizures. Three methods of seizure suppression, LFS applied to fiber tracts, HFS applied to focal zone and stimulation of the anterior nucleus of the thalamus (ANT) were compared directly in the same animal in an in-vivo epilepsy model. The results indicate that LFS generated a significantly higher level of suppression, indicating LFS of white matter tract could be a useful addition as a stimulation paradigm for the treatment of epilepsy.

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.

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

With the advent of several different fMRI analysis tools and packages outside of the established ones (i.e., SPM, AFNI, and FSL), today's researcher may wonder what the best practices are for fMRI analysis. This talk will discuss some of the recent trends in neuroimaging, including design optimization and power analysis, standardized analysis pipelines such as fMRIPrep, and an overview of current recommendations for how to present neuroimaging results. Along the way we will discuss the balance between Type I and Type II errors with different correction mechanisms (e.g., Threshold-Free Cluster Enhancement and Equitable Thresholding and Clustering), as well as considerations for working with large open-access databases.

Neural entrainment has become a phenomenon of exceptional interest to neuroscience, given its involvement in rhythm perception, production, and overt synchronized behavior. Yet, traditional methods fail to quantify neural entrainment due to a misalignment with its fundamental definition (e.g., see Novembre and Iannetti, 2018; Rajandran and Schupp, 2019). The definition of entrainment assumes that endogenous oscillatory brain activity undergoes dynamic frequency adjustments to synchronize with environmental rhythms (Lakatos et al., 2019). Following this definition, we recently developed a method sensitive to this process. Our aim was to isolate from the electroencephalographic (EEG) signal an oscillatory component that is attuned to the frequency of a rhythmic stimulation, hypothesizing that the oscillation would adaptively speed up and slow down to achieve stable synchronization over time. To induce and measure these adaptive changes in a controlled fashion, we developed the event-related frequency adjustment (ERFA) paradigm (Rosso et al., 2023). A total of twenty healthy participants took part in our study. They were instructed to tap their finger synchronously with an isochronous auditory metronome, which was unpredictably perturbed by phase-shifts and tempo-changes in both positive and negative directions across different experimental conditions. EEG was recorded during the task, and ERFA responses were quantified as changes in instantaneous frequency of the entrained component. Our results indicate that ERFAs track the stimulus dynamics in accordance with the perturbation type and direction, preferentially for a sensorimotor component. The clear and consistent patterns confirm that our method is sensitive to the process of frequency adjustment that defines neural entrainment. In this Virtual Journal Club, the discussion of our findings will be complemented by methodological insights beneficial to researchers in the fields of rhythm perception and production, as well as timing in general. We discuss the dos and don’ts of using instantaneous frequency to quantify oscillatory dynamics, the advantages of adopting a multivariate approach to source separation, the robustness against the confounder of responses evoked by periodic stimulation, and provide an overview of domains and concrete examples where the methodological framework can be applied.

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

Non-human great apes inform one another in ways that can seem very humanlike. Especially in the gestural domain, their behavior exhibits many similarities with human communication, meeting widely used empirical criteria for intentionality. At the same time, there remain some manifest differences. How to account for these similarities and differences in a unified way remains a major challenge. This presentation will summarise the arguments developed in a recent paper with Christophe Heintz. We make a key distinction between the expression of intentions (Ladyginian) and the expression of specifically informative intentions (Gricean), and we situate this distinction within a ‘special case of’ framework for classifying different modes of attention manipulation. The paper also argues that the attested tendencies of great ape interaction—for instance, to be dyadic rather than triadic, to be about the here-and-now rather than ‘displaced’—are products of its Ladyginian but not Gricean character. I will reinterpret video footage of great ape gesture as Ladyginian but not Gricean, and distinguish several varieties of meaning that are continuous with one another. We conclude that the evolutionary origins of linguistic meaning lie in gradual changes in not communication systems as such, but rather in social cognition, and specifically in what modes of attention manipulation are enabled by a species’ cognitive phenotype: first Ladyginian and in turn Gricean. The second of these shifts rendered humans, and only humans, ‘language ready’.

This webinar will focus on spike sorting analysis with SpikeInterface, an open-source framework for the analysis of extracellular electrophysiology data. After a brief introduction of the project (~30 mins) highlighting the basics of the SpikeInterface software and advanced features (e.g., data compression, quality metrics, drift correction, cloud visualization), we will have an extensive hands-on tutorial (~90 mins) showing how to use SpikeInterface in a real-world scenario. After attending the webinar, you will: (1) have a global overview of the different steps involved in a processing pipeline; (2) know how to write a complete analysis pipeline with SpikeInterface.

Qualitative coding is the process of categorizing and labeling raw data to identify themes, patterns, and concepts within qualitative research. This process requires significant time, reflection, and discussion, often characterized by inherent subjectivity and uncertainty. Here, we explore the possibility to leverage large language models (LLM) to enhance the process and assist researchers with qualitative coding. LLMs, trained on extensive human-generated text, possess an architecture that renders them capable of understanding the broader context of a conversation or text. This allows them to extract patterns and meaning effectively, making them particularly useful for the accurate extraction and coding of relevant themes. In our current approach, we employed the chatGPT 3.5 Turbo API, integrating it into the qualitative coding process for data from the SWISS100 study, specifically focusing on data derived from centenarians' experiences during the Covid-19 pandemic, as well as a systematic centenarian literature review. We provide several instances illustrating how our approach can assist researchers with extracting and coding relevant themes. With data from human coders on hand, we highlight points of convergence and divergence between AI and human thematic coding in the context of these data. Moving forward, our goal is to enhance the prototype and integrate it within an LLM designed for local storage and operation (LLaMa). Our initial findings highlight the potential of AI-enhanced qualitative coding, yet they also pinpoint areas requiring attention. Based on these observations, we formulate tentative recommendations for the optimal integration of LLMs in qualitative coding research. Further evaluations using varied datasets and comparisons among different LLMs will shed more light on the question of whether and how to integrate these models into this domain.

We will discuss this paper on Neuroquery, a relatively new web-based meta-analysis tool: https://elifesciences.org/articles/53385.pdf. This is different from Neurosynth in that it generates meta-analysis maps using predictive modeling from the string of text provided at the prompt, instead of performing inferential statistics to calculate the overlap of activation from different studies. This allows the user to generate predictive maps for more nuanced cognitive processes - especially for clinical populations which may be underrepresented in the literature compared to controls - and can be useful in generating predictions about where the activity will be for one's own study, and for creating ROIs.

Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing a conceptual link between the mathematics of graph theory and biological aspects of neural signalling such as transmission delays and metabolic cost. We organize key network communication models and measures into a taxonomy, aimed at helping researchers navigate the growing number of concepts and methods in the literature. The taxonomy highlights the pros, cons and interpretations of different conceptualizations of connectome signalling. We showcase the utility of network communication models as a flexible, interpretable and tractable framework to study brain function by reviewing prominent applications in basic, cognitive and clinical neurosciences. Finally, we provide recommendations to guide the future development, application and validation of network communication models.

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

Spatial frequency domain imaging (SFDI) is a diffuse optical measurement technique that can quantify tissue optical absorption and reduced scattering on a pixel by-pixel basis. Measurements of absorption at different wavelengths enable the extraction of molar concentrations of tissue chromophores over a wide field, providing a noncontact and label-free means to assess tissue viability, oxygenation, microarchitecture, and molecular content. In this talk, I will describe openSFDI, an open-source guide for building a low-cost, small-footprint, multi-wavelength SFDI system capable of quantifying absorption and reduced scattering as well as oxyhemoglobin and deoxyhemoglobin concentrations in biological tissue. The openSFDI project has a companion website which provides a complete parts list along with detailed instructions for assembling the openSFDI system. I will also review several technological advances our lab has recently made, including the extension of SFDI to the shortwave infrared wavelength band (900-1300 nm), where water and lipids provide strong contrast. Finally, I will discuss several preclinical and clinical applications for SFDI, including applications related to cancer, dermatology, rheumatology, cardiovascular disease, and others.

Waiting is one of healthcare’s core experiences. It is there in the time it takes to access services; through the days, weeks, months or years needed for diagnoses; in the time that treatment takes; and in the elongated time-frames of recovery, relapse, remission and dying.Funded by the Wellcome Trust, our project opens up what it means to wait in and for healthcare by examining lived experiences, representations and histories of delayed and impeded time.In an era in which time is lived at increasingly different and complex tempos, Waiting Times looks to understand both the difficulties and vital significance of waiting for practices of care, offering a fundamental re-conceptualisation of the relation between time and care in contemporary thinking about health, illness, and wellbeing.

Representational Connectivity Analysis (RCA) has gained mounting interest in the past few years. This is because, rather than conventional tracking of signal, RCA allows for the tracking of information across the brain. It can also provide insights into the content and potential transformations of the transferred information. This presentation explains several variations of the method in terms of implementation and how it can be adopted for different modalities (E/MEG and fMRI). I will also present caveats and nuances of the method which should be considered when using the RCA.

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.

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

Caroline Williams-Gray is a Principal Research Associate in the Department of Clinical Neurosciences, University of Cambridge, and an honorary consultant neurologist specializing in Parkinson’s disease and movement disorders. She leads a translational research group investigating the clinical and biological heterogeneity of PD, with the ultimate goal of developing more targeted therapies for different Parkinson’s subtypes. Her recent work has focused on the theory that the immune system plays a significant role in mediating the heterogeneity of PD and its progression. Her lab is investigating this using blood and CSF -based immune markers, PET neuroimaging and neuropathology in stratified PD cohorts; and she is leading the first randomized controlled trial repurposing a peripheral immunosuppressive drug (azathioprine) to slow the progression of PD.

Speech may be characterized as conveying both segmental information (i.e., about vowels and consonants) as well as suprasegmental information - cued through pitch, intensity, and duration - also known as the prosody of speech. In this contribution, I will argue that prosody shapes low-level speech perception, changing which speech sounds we hear. Perhaps the most notable example of how prosody guides word recognition is the phenomenon of lexical stress, whereby suprasegmental F0, intensity, and duration cues can distinguish otherwise segmentally identical words, such as "PLAto" vs. "plaTEAU" in Dutch. Work from our group showcases the vast variability in how different talkers produce stressed vs. unstressed syllables, while also unveiling the remarkable flexibility with which listeners can learn to handle this between-talker variability. It also emphasizes that lexical stress is a multimodal linguistic phenomenon, with the voice, lips, and even hands conveying stress in concert. In turn, human listeners actively weigh these multisensory cues to stress depending on the listening conditions at hand. Finally, lexical stress is presented as having a robust and lasting impact on low-level speech perception, even down to changing vowel perception. Thus, prosody - in all its multisensory forms - is a potent factor in speech perception, determining what speech sounds we hear.

Different prefrontal areas contribute in distinctly different ways to rule-guided behaviour in the context of a Wisconsin Card Sorting Test (WCST) analog for macaques. For example, causal evidence from circumscribed lesions in NHPs reveals that dorsolateral prefrontal cortex (dlPFC) is necessary to maintain a reinforced abstract rule in working memory, orbitofrontal cortex (OFC) is needed to rapidly update representations of rule value, and the anterior cingulate cortex (ACC) plays a key role in cognitive control and integrating information for correct and incorrect trials over recent outcomes. Moreover, recent lesion studies of frontopolar cortex (FPC) suggest it contributes to representing the relative value of unchosen alternatives, including rules. Yet we do not understand how these functional specializations relate to intrinsic neuronal activities nor the extent to which these neuronal activities differ between different prefrontal regions. After reviewing the aforementioned causal evidence I will present our new data from studies using multi-area multi-electrode recording techniques in NHPs to simultaneously record from four different prefrontal regions implicated in rule-guided behaviour. Multi-electrode micro-arrays (‘Utah arrays’) were chronically implanted in dlPFC, vlPFC, OFC, and FPC of two macaques, allowing us to simultaneously record single and multiunit activity, and local field potential (LFP), from all regions while the monkey performs the WCST analog. Rule-related neuronal activity was widespread in all areas recorded but it differed in degree and in timing between different areas. I will also present preliminary results from decoding analyses applied to rule-related neuronal activities both from individual clusters and also from population measures. These results confirm and help quantify dynamic task-related activities that differ between prefrontal regions. We also found task-related modulation of LFPs within beta and gamma bands in FPC. By combining this correlational recording methods with trial-specific causal interventions (electrical microstimulation) to FPC we could significantly enhance and impair animals performance in distinct task epochs in functionally relevant ways, further consistent with an emerging picture of regional functional specialization within a distributed framework of interacting and interconnected cortical regions.

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