sensorimotor control, mouvement, touch, EEG

Traditionally, touch is associated with exteroception and is rarely considered a relevant sensory cue for controlling movements in space, unlike vision. We developed a technique to isolate and measure tactile involvement in controlling sliding finger movements over a surface. Young adults traced a 2D shape with their index finger under direct or mirror-reversed visual feedback to create a conflict between visual and somatosensory inputs. In this context, increased reliance on somatosensory input compromises movement accuracy. Based on the hypothesis that tactile cues contribute to guiding hand movements when in contact with a surface, we predicted poorer performance when the participants traced with their bare finger compared to when their tactile sensation was dampened by a smooth, rigid finger splint. The results supported this prediction. EEG source analyses revealed smaller current in the source-localized somatosensory cortex during sensory conflict when the finger directly touched the surface. This finding supports the hypothesis that, in response to mirror-reversed visual feedback, the central nervous system selectively gated task-irrelevant somatosensory inputs, thereby mitigating, though not entirely resolving, the visuo-somatosensory conflict. Together, our results emphasize touch’s involvement in movement control over a surface, challenging the notion that vision predominantly governs goal-directed hand or finger movements.

Computational Mechanisms of Predictive Processing in Brains and Machines

Predictive processing offers a unifying view of neural computation, proposing that brains continuously anticipate sensory input and update internal models based on prediction errors. In this talk, I will present converging evidence for the computational mechanisms underlying this framework across human neuroscience and deep neural networks. I will begin with recent work showing that large-scale distributed prediction-error encoding in the human brain directly predicts how sensory representations reorganize through predictive learning. I will then turn to PredNet, a popular predictive coding inspired deep network that has been widely used to model real-world biological vision systems. Using dynamic stimuli generated with our Spatiotemporal Style Transfer algorithm, we demonstrate that PredNet relies primarily on low-level spatiotemporal structure and remains insensitive to high-level content, revealing limits in its generalization capacity. Finally, I will discuss new recurrent vision models that integrate top-down feedback connections with intrinsic neural variability, uncovering a dual mechanism for robust sensory coding in which neural variability decorrelates unit responses, while top-down feedback stabilizes network dynamics. Together, these results outline how prediction error signaling and top-down feedback pathways shape adaptive sensory processing in biological and artificial systems.

Developmental emergence of personality

The Nature versus Nurture debate has generally been considered from the lens of genome versus experience dichotomy and has dominated our thinking about behavioral individuality and personality traits. In contrast, the role of nonheritable noise during brain development in behavioral variation is understudied. Using the Drosophila melanogaster visual system, I will discuss our efforts to dissect how individuality in circuit wiring emerges during development, and how that helps generate individual behavioral variation.

A human stem cell-derived organoid model of the trigeminal ganglion

Dr. Rudy Behnia

The Behnia Lab, is seeking to hire a Postdoctoral Research Scientist to assist with studies on how our brains see the world around us. The overarching goal of the lab is to define the processing steps that transform light signals in photoreceptors into feature of a visual scene such as color or direction of motion. For a given visual feature, we aim to describe not only the underlying mathematic operations (algorithms) that govern a transformation, but also the neural circuits that implement these. We make extensive use of connectomics data, as well as the abundant genetic tools available in fruit flies, and collaborate extensively with theorist to build biologically constrained models of perception. We are also interested in understanding how different internal/environmental states or other sensory systems influence the visual perception and how multisensory representations are used for higher cognitive functions such as learning and navigation. We invite to you review our website for more details about our work: http://behnialab.neuroscience.columbia.edu. Example projects include: 1/ A multidisciplinary collaboration with the laboratory of Ashok Litwin-Kumar at the Center for Theoretical Neuroscience, aimed at defining circuit mechanisms underlying multisensory learning, 2/ Investigating the role of neuromodulatory systems in color processing. Please contact Rudy Behnia directly at rb3161@columbia.edu for more details. The Behnia lab is part of the Columbia University’s Mortimer B. Zuckerman Mind Brain Behavior Institute (the Zuckerman Institute) brings together world-class researchers from varied scientific disciplines to explore aspects of mind and brain, through the exchange of ideas and active collaboration. The Zuckerman Institute’s home, the Jerome L. Greene Science Center is a state-of-the-art facility on Columbia’s Manhattanville campus. Situated in the heart of Manhattan in New York City, the Zuckerman Institute houses over 50 laboratories employing a broad range of interdisciplinary approaches to transform our understanding of the mind and brain. In this highly collaborative environment, experimental, computational, and theoretical labs work together to gain critical insights into how the brain develops, performs, endures and recovers. The Zuckerman Institute provides multiple levels of support for postdoctoral researchers (https://zuckermaninstitute.columbia.edu/postdocs). The Postdoc Program provides postdocs with an enriched research environment to advance their scientific training and support their professional growth. This includes frameworks to build a professional network of mentors and peers, through the personal board of advisors, as well as leadership opportunities, workshops and opportunities for public engagement. The Behnia lab strives to provide a supportive environment where creativity, independence, work/life balance are valued. We are strong advocates of diversity, equality, inclusion and belonging. We encourage application from applicants from diverse backgrounds. Prior experience in quantitative analysis of neuronal recordings and/or behavior and related programming skills (Python, version control, databases) are required. Prior expertise with in vivo imaging, electrophysiological recordings or behavioral studies, as well as superior motivation, drive and demonstrated aptitude for carrying out independent research are highly desirable qualifications. In addition, the ideal candidate would seek to work in a highly diverse and collaborative environment.

NF1 exon 51 alternative splicing: functional implications in Central Nervous System (CNS) Cells

Competing Rhythms: Understanding and Modulating Auditory Neural Entrainment

Memory Decoding Journal Club: "Connectomic traces of Hebbian plasticity in the entorhinalhippocampal system

Connectomic traces of Hebbian plasticity in the entorhinalhippocampal system

Endocannabinoid System Dysregulations in Binge Eating Disorder and Obesity

The SIMple microscope: Development of a fibre-based platform for accessible SIM imaging in unconventional environments

Advancements in imaging speed, depth and resolution have made structured illumination microscopy (SIM) an increasingly powerful optical sectioning (OS) and super-resolution (SR) technique, but these developments remain inaccessible to many life science researchers due to the cost, optical complexity and delicacy of these instruments. We address these limitations by redesigning the optical path using in-line fibre components that are compact, lightweight and easily assembled in a “Plug & Play” modality, without compromising imaging performance. They can be integrated into an existing widefield microscope with a minimum of optical components and alignment, making OS-SIM more accessible to researchers with less optics experience. We also demonstrate a complete SR-SIM imaging system with dimensions 300 mm × 300 mm × 450 mm. We propose to enable accessible SIM imaging by utilising its compact, lightweight and robust design to transport it where it is needed, and image in “unconventional” environments where factors such as temperature and biosafety considerations currently limit imaging experiments.

The Systems Vision Science Summer School & Symposium, August 11 – 22, 2025, Tuebingen, Germany

Applications are invited for our third edition of Systems Vision Science (SVS) summer school since 2023, designed for everyone interested in gaining a systems level understanding of biological vision. We plan a coherent, graduate-level, syllabus on the integration of experimental data with theory and models, featuring lectures, guided exercises and discussion sessions. The summer school will end with a Systems Vision Science symposium on frontier topics on August 20-22, with additional invited and contributed presentations and posters. Call for contributions and participations to the symposium will be sent out spring of 2025. All summer school participants are invited to attend, and welcome to submit contributions to the symposium.

The Systems Vision Science Summer School & Symposium, August 11 – 22, 2025, Tuebingen, Germany

Applications are invited for our third edition of Systems Vision Science (SVS) summer school since 2023, designed for everyone interested in gaining a systems level understanding of biological vision. We plan a coherent, graduate-level, syllabus on the integration of experimental data with theory and models, featuring lectures, guided exercises and discussion sessions. The summer school will end with a Systems Vision Science symposium on frontier topics on August 20-22, with additional invited and contributed presentations and posters. Call for contributions and participations to the symposium will be sent out spring of 2025. All summer school participants are invited to attend, and welcome to submit contributions to the symposium.

The Systems Vision Science Summer School & Symposium, August 11 – 22, 2025, Tuebingen, Germany

Applications are invited for our third edition of Systems Vision Science (SVS) summer school since 2023, designed for everyone interested in gaining a systems level understanding of biological vision. We plan a coherent, graduate-level, syllabus on the integration of experimental data with theory and models, featuring lectures, guided exercises and discussion sessions. The summer school will end with a Systems Vision Science symposium on frontier topics on August 20-22, with additional invited and contributed presentations and posters. Call for contributions and participations to the symposium will be sent out spring of 2025. All summer school participants are invited to attend, and welcome to submit contributions to the symposium.

The Systems Vision Science Summer School & Symposium, August 11 – 22, 2025, Tuebingen, Germany

Applications are invited for our third edition of Systems Vision Science (SVS) summer school since 2023, designed for everyone interested in gaining a systems level understanding of biological vision. We plan a coherent, graduate-level, syllabus on the integration of experimental data with theory and models, featuring lectures, guided exercises and discussion sessions. The summer school will end with a Systems Vision Science symposium on frontier topics on August 20-22, with additional invited and contributed presentations and posters. Call for contributions and participations to the symposium will be sent out spring of 2025. All summer school participants are invited to attend, and welcome to submit contributions to the symposium.

The Systems Vision Science Summer School & Symposium, August 11 – 22, 2025, Tuebingen, Germany

Applications are invited for our third edition of Systems Vision Science (SVS) summer school since 2023, designed for everyone interested in gaining a systems level understanding of biological vision. We plan a coherent, graduate-level, syllabus on the integration of experimental data with theory and models, featuring lectures, guided exercises and discussion sessions. The summer school will end with a Systems Vision Science symposium on frontier topics on August 20-22, with additional invited and contributed presentations and posters. Call for contributions and participations to the symposium will be sent out spring of 2025. All summer school participants are invited to attend, and welcome to submit contributions to the symposium.

The Systems Vision Science Summer School & Symposium, August 11 – 22, 2025, Tuebingen, Germany

Applications are invited for our third edition of Systems Vision Science (SVS) summer school since 2023, designed for everyone interested in gaining a systems level understanding of biological vision. We plan a coherent, graduate-level, syllabus on the integration of experimental data with theory and models, featuring lectures, guided exercises and discussion sessions. The summer school will end with a Systems Vision Science symposium on frontier topics on August 20-22, with additional invited and contributed presentations and posters. Call for contributions and participations to the symposium will be sent out spring of 2025. All summer school participants are invited to attend, and welcome to submit contributions to the symposium.

The Systems Vision Science Summer School & Symposium, August 11 – 22, 2025, Tuebingen, Germany

Applications are invited for our third edition of Systems Vision Science (SVS) summer school since 2023, designed for everyone interested in gaining a systems level understanding of biological vision. We plan a coherent, graduate-level, syllabus on the integration of experimental data with theory and models, featuring lectures, guided exercises and discussion sessions. The summer school will end with a Systems Vision Science symposium on frontier topics on August 20-22, with additional invited and contributed presentations and posters. Call for contributions and participations to the symposium will be sent out spring of 2025. All summer school participants are invited to attend, and welcome to submit contributions to the symposium.

The Systems Vision Science Summer School & Symposium, August 11 – 22, 2025, Tuebingen, Germany

Applications are invited for our third edition of Systems Vision Science (SVS) summer school since 2023, designed for everyone interested in gaining a systems level understanding of biological vision. We plan a coherent, graduate-level, syllabus on the integration of experimental data with theory and models, featuring lectures, guided exercises and discussion sessions. The summer school will end with a Systems Vision Science symposium on frontier topics on August 20-22, with additional invited and contributed presentations and posters. Call for contributions and participations to the symposium will be sent out spring of 2025. All summer school participants are invited to attend, and welcome to submit contributions to the symposium.

The Systems Vision Science Summer School & Symposium, August 11 – 22, 2025, Tuebingen, Germany

Applications are invited for our third edition of Systems Vision Science (SVS) summer school since 2023, designed for everyone interested in gaining a systems level understanding of biological vision. We plan a coherent, graduate-level, syllabus on the integration of experimental data with theory and models, featuring lectures, guided exercises and discussion sessions. The summer school will end with a Systems Vision Science symposium on frontier topics on August 20-22, with additional invited and contributed presentations and posters. Call for contributions and participations to the symposium will be sent out spring of 2025. All summer school participants are invited to attend, and welcome to submit contributions to the symposium.

The Systems Vision Science Summer School & Symposium, August 11 – 22, 2025, Tuebingen, Germany

Applications are invited for our third edition of Systems Vision Science (SVS) summer school since 2023, designed for everyone interested in gaining a systems level understanding of biological vision. We plan a coherent, graduate-level, syllabus on the integration of experimental data with theory and models, featuring lectures, guided exercises and discussion sessions. The summer school will end with a Systems Vision Science symposium on frontier topics on August 20-22, with additional invited and contributed presentations and posters. Call for contributions and participations to the symposium will be sent out spring of 2025. All summer school participants are invited to attend, and welcome to submit contributions to the symposium.

Digital Traces of Human Behaviour: From Political Mobilisation to Conspiracy Narratives

Digital platforms generate unprecedented traces of human behaviour, offering new methodological approaches to understanding collective action, polarisation, and social dynamics. Through analysis of millions of digital traces across multiple studies, we demonstrate how online behaviours predict offline action: Brexit-related tribal discourse responds to real-world events, machine learning models achieve 80% accuracy in predicting real-world protest attendance from digital signals, and social validation through "likes" emerges as a key driver of mobilization. Extending this approach to conspiracy narratives reveals how digital traces illuminate psychological mechanisms of belief and community formation. Longitudinal analysis of YouTube conspiracy content demonstrates how narratives systematically address existential, epistemic, and social needs, while examination of alt-tech platforms shows how emotions of anger, contempt, and disgust correlate with violence-legitimating discourse, with significant differences between narratives associated with offline violence versus peaceful communities. This work establishes digital traces as both methodological innovation and theoretical lens, demonstrating that computational social science can illuminate fundamental questions about polarisation, mobilisation, and collective behaviour across contexts from electoral politics to conspiracy communities.

Open SPM: A Modular Framework for Scanning Probe Microscopy

OpenSPM aims to democratize innovation in the field of scanning probe microscopy (SPM), which is currently dominated by a few proprietary, closed systems that limit user-driven development. Our platform includes a high-speed OpenAFM head and base optimized for small cantilevers, an OpenAFM controller, a high-voltage amplifier, and interfaces compatible with several commercial AFM systems such as the Bruker Multimode, Nanosurf DriveAFM, Witec Alpha SNOM, Zeiss FIB-SEM XB550, and Nenovision Litescope. We have created a fully documented and community-driven OpenSPM platform, with training resources and sourcing information, which has already enabled the construction of more than 15 systems outside our lab. The controller is integrated with open-source tools like Gwyddion, HDF5, and Pycroscopy. We have also engaged external companies, two of which are integrating our controller into their products or interfaces. We see growing interest in applying parts of the OpenSPM platform to related techniques such as correlated microscopy, nanoindentation, and scanning electron/confocal microscopy. To support this, we are developing more generic and modular software, alongside a structured development workflow. A key feature of the OpenSPM system is its Python-based API, which makes the platform fully scriptable and ideal for AI and machine learning applications. This enables, for instance, automatic control and optimization of PID parameters, setpoints, and experiment workflows. With a growing contributor base and industry involvement, OpenSPM is well positioned to become a global, open platform for next-generation SPM innovation.

Gene regulation networks in nervous system cancers: identification of novel drug targets

“Development and application of gaze control models for active perception”

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.

Developmental and evolutionary perspectives on thalamic function

Brain organization and function is a complex topic. We are good at establishing correlates of perception and behavior across forebrain circuits, as well as manipulating activity in these circuits to affect behavior. However, we still lack good models for the large-scale organization and function of the forebrain. What are the contributions of the cortex, basal ganglia, and thalamus to behavior? In addressing these questions, we often ascribe function to each area as if it were an independent processing unit. However, we know from the anatomy that the cortex, basal ganglia, and thalamus, are massively interconnected in a large network. One way to generate insight into these questions is to consider the evolution and development of forebrain systems. In this talk, I will discuss the developmental and evolutionary (comparative anatomy) data on the thalamus, and how it fits within forebrain networks. I will address questions including, when did the thalamus appear in evolution, how is the thalamus organized across the vertebrate lineage, and how can the change in the organization of forebrain networks affect behavioral repertoires.

Neurobiological constraints on learning: bug or feature?

Understanding how brains learn requires bridging evidence across scales—from behaviour and neural circuits to cells, synapses, and molecules. In our work, we use computational modelling and data analysis to explore how the physical properties of neurons and neural circuits constrain learning. These include limits imposed by brain wiring, energy availability, molecular noise, and the 3D structure of dendritic spines. In this talk I will describe one such project testing if wiring motifs from fly brain connectomes can improve performance of reservoir computers, a type of recurrent neural network. The hope is that these insights into brain learning will lead to improved learning algorithms for artificial systems.

Investigating the Neurobiology and Neurophysiology of Psilocybin Using Drosophila melanogaster as a Model System

HealthCore: A modular data collection ecosystem to connect the dots in Neurorehab

Functional Plasticity in the Language Network – evidence from Neuroimaging and Neurostimulation

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.

Neural mechanisms of rhythmic motor control in Drosophila

All animal locomotion is rhythmic,whether it is achieved through undulatory movement of the whole body or the coordination of articulated limbs. Neurobiologists have long studied locomotor circuits that produce rhythmic activity with non-rhythmic input, also called central pattern generators (CPGs). However, the cellular and microcircuit implementation of a walking CPG has not been described for any limbed animal. New comprehensive connectomes of the fruit fly ventral nerve cord (VNC) provide an opportunity to study rhythmogenic walking circuits at a synaptic scale.We use a data-driven network modeling approach to identify and characterize a putative walking CPG in the Drosophila leg motor system.

Relating circuit dynamics to computation: robustness and dimension-specific computation in cortical dynamics

Neural dynamics represent the hard-to-interpret substrate of circuit computations. Advances in large-scale recordings have highlighted the sheer spatiotemporal complexity of circuit dynamics within and across circuits, portraying in detail the difficulty of interpreting such dynamics and relating it to computation. Indeed, even in extremely simplified experimental conditions, one observes high-dimensional temporal dynamics in the relevant circuits. This complexity can be potentially addressed by the notion that not all changes in population activity have equal meaning, i.e., a small change in the evolution of activity along a particular dimension may have a bigger effect on a given computation than a large change in another. We term such conditions dimension-specific computation. Considering motor preparatory activity in a delayed response task we utilized neural recordings performed simultaneously with optogenetic perturbations to probe circuit dynamics. First, we revealed a remarkable robustness in the detailed evolution of certain dimensions of the population activity, beyond what was thought to be the case experimentally and theoretically. Second, the robust dimension in activity space carries nearly all of the decodable behavioral information whereas other non-robust dimensions contained nearly no decodable information, as if the circuit was setup to make informative dimensions stiff, i.e., resistive to perturbations, leaving uninformative dimensions sloppy, i.e., sensitive to perturbations. Third, we show that this robustness can be achieved by a modular organization of circuitry, whereby modules whose dynamics normally evolve independently can correct each other’s dynamics when an individual module is perturbed, a common design feature in robust systems engineering. Finally, we will recent work extending this framework to understanding the neural dynamics underlying preparation of speech.

Computational modelling of ocular pharmacokinetics

Pharmacokinetics in the eye is an important factor for the success of ocular drug delivery and treatment. Pharmacokinetic features determine the feasible routes of drug administration, dosing levels and intervals, and it has impact on eventual drug responses. Several physical, biochemical, and flow-related barriers limit drug exposure of anterior and posterior ocular target tissues during treatment during local (topical, subconjunctival, intravitreal) and systemic administration (intravenous, per oral). Mathematical models integrate joint impact of various barriers on ocular pharmacokinetics (PKs) thereby helping drug development. The models are useful in describing (top-down) and predicting (bottom-up) pharmacokinetics of ocular drugs. This is useful also in the design and development of new drug molecules and drug delivery systems. Furthermore, the models can be used for interspecies translation and probing of disease effects on pharmacokinetics. In this lecture, ocular pharmacokinetics and current modelling methods (noncompartmental analyses, compartmental, physiologically based, and finite element models) are introduced. Future challenges are also highlighted (e.g. intra-tissue distribution, prediction of drug responses, active transport).

Plasticity of the adult visual system

Deepfake emotional expressions trigger the uncanny valley brain response, even when they are not recognised as fake

Facial expressions are inherently dynamic, and our visual system is sensitive to subtle changes in their temporal sequence. However, researchers often use dynamic morphs of photographs—simplified, linear representations of motion—to study the neural correlates of dynamic face perception. To explore the brain's sensitivity to natural facial motion, we constructed a novel dynamic face database using generative neural networks, trained on a verified set of video-recorded emotional expressions. The resulting deepfakes, consciously indistinguishable from videos, enabled us to separate biological motion from photorealistic form. Results showed that conventional dynamic morphs elicit distinct responses in the brain compared to videos and photos, suggesting they violate expectations (n400) and have reduced social salience (late positive potential). This suggests that dynamic morphs misrepresent facial dynamism, resulting in misleading insights about the neural and behavioural correlates of face perception. Deepfakes and videos elicited largely similar neural responses, suggesting they could be used as a proxy for real faces in vision research, where video recordings cannot be experimentally manipulated. And yet, despite being consciously undetectable as fake, deepfakes elicited an expectation violation response in the brain. This points to a neural sensitivity to naturalistic facial motion, beyond conscious awareness. Despite some differences in neural responses, the realism and manipulability of deepfakes make them a valuable asset for research where videos are unfeasible. Using these stimuli, we proposed a novel marker for the conscious perception of naturalistic facial motion – Frontal delta activity – which was elevated for videos and deepfakes, but not for photos or dynamic morphs.

Memory Decoding Journal Club: Reconstructing a new hippocampal engram for systems reconsolidation and remote memory updating

Join us for the Memory Decoding Journal Club, a collaboration between the Carboncopies Foundation and BPF Aspirational Neuroscience. This month, we're diving into a groundbreaking paper: 'Reconstructing a new hippocampal engram for systems reconsolidation and remote memory updating' by Bo Lei, Bilin Kang, Yuejun Hao, Haoyu Yang, Zihan Zhong, Zihan Zhai, and Yi Zhong from Tsinghua University, Beijing Academy of Artificial Intelligence, IDG/McGovern Institute of Brain Research, and Peking Union Medical College. Dr. Randal Koene will guide us through an engaging discussion on these exciting findings and their implications for neuroscience and memory research.

COSYNE 2025

The COSYNE 2025 conference was held in Montreal with post-conference workshops in Mont-Tremblant, continuing to provide a premier forum for computational and systems neuroscience. Attendees exchanged cutting-edge research in a single-track main meeting and in-depth specialized workshops, reflecting Cosyne’s mission to understand how neural systems function:contentReference[oaicite:6]{index=6}:contentReference[oaicite:7]{index=7}.

Resonancia Magnética y Detección Remota: No se Necesita Estar tan Cerca”

La resonancia magnética nuclear está basada en el fenómeno del magnetismo nuclear que más aplicaciones ha encontrado para el estudio de enfermedades humanas. Usualmente la señal de RM es recibida y transmitida a distancias cercanas al objeto del que se quiere obtener una imagen. Otra alternativa es emitir y recibir la misma señal de manera remota haciendo uso de guías de onda. Este enfoque tiene la ventaja que se puede aplicar a altos campos magnéticos, la absorción de energía es menor, además es posible cubrir mayores regiones de interés y comodidad para el paciente. Por otro lado, sufre de baja calidad de imagen en algunos casos. En esta ocasión hablaremos de nuestra experiencia haciendo uso de este enfoque empleando una guía de ondas abierta y metamateriales tanto para sistemas clínicos como preclínicos de IRM.

Impact of High Fat Diet on Central Cardiac Circuits: When The Wanderer is Lost

Cardiac vagal motor drive originates in the brainstem's cardiac vagal motor neurons (CVNs). Despite well-established cardioinhibitory functions in health, our understanding of CVNs in disease is limited. There is a clear connection of cardiovascular regulation with metabolic and energy expenditure systems. Using high fat diet as a model, this talk will explore how metabolic dysfunction impacts the regulation of cardiac tissue through robust inhibition of CVNs. Specifically, it will present an often overlooked modality of inhibition, tonic gamma-aminobuytric acid (GABA) A-type neurotransmission using an array of techniques from single cell patch clamp electrophysiology to transgenic in vivo whole animal physiology. It also will highlight a unique interaction with the delta isoform of protein kinase C to facilitate GABA A-type receptor expression.

Cognitive maps as expectations learned across episodes – a model of the two dentate gyrus blades

How can the hippocampal system transition from episodic one-shot learning to a multi-shot learning regime and what is the utility of the resultant neural representations? This talk will explore the role of the dentate gyrus (DG) anatomy in this context. The canonical DG model suggests it performs pattern separation. More recent experimental results challenge this standard model, suggesting DG function is more complex and also supports the precise binding of objects and events to space and the integration of information across episodes. Very recent studies attribute pattern separation and pattern integration to anatomically distinct parts of the DG (the suprapyramidal blade vs the infrapyramidal blade). We propose a computational model that investigates this distinction. In the model the two processing streams (potentially localized in separate blades) contribute to the storage of distinct episodic memories, and the integration of information across episodes, respectively. The latter forms generalized expectations across episodes, eventually forming a cognitive map. We train the model with two data sets, MNIST and plausible entorhinal cortex inputs. The comparison between the two streams allows for the calculation of a prediction error, which can drive the storage of poorly predicted memories and the forgetting of well-predicted memories. We suggest that differential processing across the DG aids in the iterative construction of spatial cognitive maps to serve the generation of location-dependent expectations, while at the same time preserving episodic memory traces of idiosyncratic events.

Constructing and deconstructing the human nervous system to study development and disease

Pharmacological exploitation of neurotrophins and their receptors to develop novel therapeutic approaches against neurodegenerative diseases and brain trauma

Neurotrophins (NGF, BDNF, NT-3) are endogenous growth factors that exert neuroprotective effects by preventing neuronal death and promoting neurogenesis. They act by binding to their respective high-affinity, pro-survival receptors TrkA, TrkB or TrkC, as well as to p75NTR death receptor. While these molecules have been shown to significantly slow or prevent neurodegeneration, their reduced bioavailability and inability to penetrate the blood-brain-barrier limit their use as potential therapeutics. To bypass these limitations, our research team has developed and patented small-sized, lipophilic compounds which selectively resemble neurotrophins’ effects, presenting preferable pharmacological properties and promoting neuroprotection and repair against neurodegeneration. In addition, the combination of these molecules with 3D cultured human neuronal cells, and their targeted delivery in the brain ventricles through soft robotic systems, could offer novel therapeutic approaches against neurodegenerative diseases and brain trauma.

Brain Emulation Challenge Workshop

Brain Emulation Challenge workshop will tackle cutting-edge topics such as ground-truthing for validation, leveraging artificial datasets generated from virtual brain tissue, and the transformative potential of virtual brain platforms, such as applied to the forthcoming Brain Emulation Challenge.

Brain Emulation Challenge Workshop

Brain Emulation Challenge workshop will tackle cutting-edge topics such as ground-truthing for validation, leveraging artificial datasets generated from virtual brain tissue, and the transformative potential of virtual brain platforms, such as applied to the forthcoming Brain Emulation Challenge.

Structural & Functional Neuroplasticity in Children with Hemiplegia

About 30% of children with cerebral palsy have congenital hemiplegia, resulting from periventricular white matter injury, which impairs the use of one hand and disrupts bimanual co-ordination. Congenital hemiplegia has a profound effect on each child's life and, thus, is of great importance to the public health. Changes in brain organization (neuroplasticity) often occur following periventricular white matter injury. These changes vary widely depending on the timing, location, and extent of the injury, as well as the functional system involved. Currently, we have limited knowledge of neuroplasticity in children with congenital hemiplegia. As a result, we provide rehabilitation treatment to these children almost blindly based exclusively on behavioral data. In this talk, I will present recent research evidence of my team on understanding neuroplasticity in children with congenital hemiplegia by using a multimodal neuroimaging approach that combines data from structural and functional neuroimaging methods. I will further present preliminary data regarding functional improvements of upper extremities motor and sensory functions as a result of rehabilitation with a robotic system that involves active participation of the child in a video-game setup. Our research is essential for the development of novel or improved neurological rehabilitation strategies for children with congenital hemiplegia.

LRRK2 – a master regulator of neurodegeneration: acting on multiple systems including neuroinflammatory signaling, vesicular trafficking, and cell death pathways

Vision for perception versus vision for action: dissociable contributions of visual sensory drives from primary visual cortex and superior colliculus neurons to orienting behaviors

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.

Neural makers of lapses in attention during sustained ‘real-world’ task performance

Lapses in attention are ubiquitous and, unfortunately, the cause of many tragic accidents. One potential solution may be to develop assistance systems which can use objective, physiological signals to monitor attention levels and predict a lapse in attention before it occurs. As it stands, it is unclear which physiological signals are the most reliable markers of inattention, and even less is known about how reliably they will work in a more naturalistic setting. My project aims to address these questions across two experiments: a lab-based experiment and a more ‘real-world’ experiment. In this talk I will present the findings from my lab experiment, in which we combined EEG and pupillometry to detect markers of inattention during two computerised sustained attention tasks. I will then present the methods for my second, more ‘naturalistic’ experiment in which we use the same methods (EEG and pupillometry) to examine whether these markers can still be extracted from noisier data.

Memory formation in hippocampal microcircuit

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.

Analyzing Network-Level Brain Processing and Plasticity Using Molecular Neuroimaging

Behavior and cognition depend on the integrated action of neural structures and populations distributed throughout the brain. We recently developed a set of molecular imaging tools that enable multiregional processing and plasticity in neural networks to be studied at a brain-wide scale in rodents and nonhuman primates. Here we will describe how a novel genetically encoded activity reporter enables information flow in virally labeled neural circuitry to be monitored by fMRI. Using the reporter to perform functional imaging of synaptically defined neural populations in the rat somatosensory system, we show how activity is transformed within brain regions to yield characteristics specific to distinct output projections. We also show how this approach enables regional activity to be modeled in terms of inputs, in a paradigm that we are extending to address circuit-level origins of functional specialization in marmoset brains. In the second part of the talk, we will discuss how another genetic tool for MRI enables systematic studies of the relationship between anatomical and functional connectivity in the mouse brain. We show that variations in physical and functional connectivity can be dissociated both across individual subjects and over experience. We also use the tool to examine brain-wide relationships between plasticity and activity during an opioid treatment. This work demonstrates the possibility of studying diverse brain-wide processing phenomena using molecular neuroimaging.

CNS Control of Peripheral Mitochondrial Form and Function: Mitokines

My laboratory has made an intriguing discovery that mitochondrial stress in one tissue can be communicated to distal tissues. We find that mitochondrial stress in the nervous system triggers the production of entities known as mitokines. These mitokines are discharged from the nervous system, orchestrating a response in peripheral tissues that extends the lifespan of C. elegans. The revelation came as a surprise, given the prevalent belief that cell autonomous mechanisms would underlie the relationship between mitochondrial function and aging. It was also surprising given the prevailing dogma that mitochondrial function must be increased, not decreased, to improve health and longevity. Our work also underscores the fact that mitochondria, which originated as a microbial entity and later evolved into an intracellular symbiont, have retained their capacity for intercommunication, now facilitated by signals from the nervous system. We hypothesize that this communication has evolved as a mechanism to reduce infection from pathogens.

Rett syndrome, MECP2 and therapeutic strategies

The development of the iPS cell technology has revolutionized our ability to study development and diseases in defined in vitro cell culture systems. The talk will focus on Rett Syndrome and discuss two topics: (i) the use of gene editing as an approach to therapy and (ii) the role of MECP2 in gene expression (i) The mutation of the X-linked MECP2 gene is causative for the disease. In a female patient, every cell has a wt copy that is, however, in 50% of the cells located on the inactive X chromosome. We have used epigenetic gene editing tools to activate the wt MECP2 allele on the inactive X chromosome. (ii) MECP2 is thought to act as repressor of gene expression. I will present data which show that MECP2 binds to Pol II and acts as an activator for thousands of genes. The target genes are significantly enriched for Autism related genes. Our data challenge the established model of MECP2’s role in gene expression and suggest novel therapeutic approaches.

Towards open meta-research in neuroimaging

When meta-research (research on research) makes an observation or points out a problem (such as a flaw in methodology), the project should be repeated later to determine whether the problem remains. For this we need meta-research that is reproducible and updatable, or living meta-research. In this talk, we introduce the concept of living meta-research, examine prequels to this idea, and point towards standards and technologies that could assist researchers in doing living meta-research. We introduce technologies like natural language processing, which can help with automation of meta-research, which in turn will make the research easier to reproduce/update. Further, we showcase our open-source litmining ecosystem, which includes pubget (for downloading full-text journal articles), labelbuddy (for manually extracting information), and pubextract (for automatically extracting information). With these tools, you can simplify the tedious data collection and information extraction steps in meta-research, and then focus on analyzing the text. We will then describe some living meta-research projects to illustrate the use of these tools. For example, we’ll show how we used GPT along with our tools to extract information about study participants. Essentially, this talk will introduce you to the concept of meta-research, some tools for doing meta-research, and some examples. Particularly, we want you to take away the fact that there are many interesting open questions in meta-research, and you can easily learn the tools to answer them. Check out our tools at https://litmining.github.io/

“Open Raman Microscopy (ORM): A modular Raman spectroscopy setup with an open-source controller”

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.

Sensory cognition

This webinar features presentations from SueYeon Chung (New York University) and Srinivas Turaga (HHMI Janelia Research Campus) on theoretical and computational approaches to sensory cognition. Chung introduced a “neural manifold” framework to capture how high-dimensional neural activity is structured into meaningful manifolds reflecting object representations. She demonstrated that manifold geometry—shaped by radius, dimensionality, and correlations—directly governs a population’s capacity for classifying or separating stimuli under nuisance variations. Applying these ideas as a data analysis tool, she showed how measuring object-manifold geometry can explain transformations along the ventral visual stream and suggested that manifold principles also yield better self-supervised neural network models resembling mammalian visual cortex. Turaga described simulating the entire fruit fly visual pathway using its connectome, modeling 64 key cell types in the optic lobe. His team’s systematic approach—combining sparse connectivity from electron microscopy with simple dynamical parameters—recapitulated known motion-selective responses and produced novel testable predictions. Together, these studies underscore the power of combining connectomic detail, task objectives, and geometric theories to unravel neural computations bridging from stimuli to cognitive functions.

Brain circuits for spatial navigation

In this webinar on spatial navigation circuits, three researchers—Ann Hermundstad, Ila Fiete, and Barbara Webb—discussed how diverse species solve navigation problems using specialized yet evolutionarily conserved brain structures. Hermundstad illustrated the fruit fly’s central complex, focusing on how hardwired circuit motifs (e.g., sinusoidal steering curves) enable rapid, flexible learning of goal-directed navigation. This framework combines internal heading representations with modifiable goal signals, leveraging activity-dependent plasticity to adapt to new environments. Fiete explored the mammalian head-direction system, demonstrating how population recordings reveal a one-dimensional ring attractor underlying continuous integration of angular velocity. She showed that key theoretical predictions—low-dimensional manifold structure, isometry, uniform stability—are experimentally validated, underscoring parallels to insect circuits. Finally, Webb described honeybee navigation, featuring path integration, vector memories, route optimization, and the famous waggle dance. She proposed that allocentric velocity signals and vector manipulation within the central complex can encode and transmit distances and directions, enabling both sophisticated foraging and inter-bee communication via dance-based cues.

LLMs and Human Language Processing

This webinar convened researchers at the intersection of Artificial Intelligence and Neuroscience to investigate how large language models (LLMs) can serve as valuable “model organisms” for understanding human language processing. Presenters showcased evidence that brain recordings (fMRI, MEG, ECoG) acquired while participants read or listened to unconstrained speech can be predicted by representations extracted from state-of-the-art text- and speech-based LLMs. In particular, text-based LLMs tend to align better with higher-level language regions, capturing more semantic aspects, while speech-based LLMs excel at explaining early auditory cortical responses. However, purely low-level features can drive part of these alignments, complicating interpretations. New methods, including perturbation analyses, highlight which linguistic variables matter for each cortical area and time scale. Further, “brain tuning” of LLMs—fine-tuning on measured neural signals—can improve semantic representations and downstream language tasks. Despite open questions about interpretability and exact neural mechanisms, these results demonstrate that LLMs provide a promising framework for probing the computations underlying human language comprehension and production at multiple spatiotemporal scales.

How do we sleep?

There is no consensus on if sleep is for the brain, body or both. But the difference in how we feel following disrupted sleep or having a good night of continuous sleep is striking. Understanding how and why we sleep will likely give insights into many aspects of health. In this talk I will outline our recent work on how the prefrontal cortex can signal to the hypothalamus to regulate sleep preparatory behaviours and sleep itself, and how other brain regions, including the ventral tegmental area, respond to psychosocial stress to induce beneficial sleep. I will also outline our work on examining the function of the glymphatic system, and whether clearance of molecules from the brain is enhanced during sleep or wakefulness.

COSYNE 2023

The COSYNE 2023 conference provided an inclusive forum for exchanging experimental and theoretical approaches to problems in systems neuroscience, continuing the tradition of bringing together the computational neuroscience community:contentReference[oaicite:5]{index=5}. The main meeting was held in Montreal followed by post-conference workshops in Mont-Tremblant, fostering intensive discussions and collaboration.

COSYNE 2022

The annual Cosyne meeting provides an inclusive forum for the exchange of empirical and theoretical approaches to problems in systems neuroscience, in order to understand how neural systems function:contentReference[oaicite:2]{index=2}. The main meeting is single-track, with invited talks selected by the Executive Committee and additional talks and posters selected by the Program Committee based on submitted abstracts:contentReference[oaicite:3]{index=3}. The workshops feature in-depth discussion of current topics of interest in a small group setting:contentReference[oaicite:4]{index=4}.

An Attention-based Multimodal Decoder for Hybrid Brain-Computer Interface Control Systems

Bernstein Conference 2024

Co-Design of Analog Neuromorphic Systems and Cortical Motifs with Local Dendritic Learning Rules

Bernstein Conference 2024

Computational mechanisms of odor perception and representational drift in rodent olfactory systems

Bernstein Conference 2024

Connectome and task predict neural activity across the fly visual system

Bernstein Conference 2024

Efficient nonlinear receptive field estimation across processing stages of sensory systems

Bernstein Conference 2024

Forecasting motor cortex activity with a nonlinear latent dynamical system model

Bernstein Conference 2024

Information flow in the somatosensory system : From Mechanoreceptor to Cortex

Bernstein Conference 2024

Modeling gait dynamics with switching non-linear dynamical systems

Bernstein Conference 2024

To add or to multiply? The ring-attractor network in the zebrafish heading-direction system.

Bernstein Conference 2024

Neural manifold discovery via dynamical systems

Bernstein Conference 2024

Unified C. elegans Neural Activity and Connectivity Datasets for Building Foundation Models of a Small Nervous System

Bernstein Conference 2024

Using Dynamical Systems Theory to Improve Temporal Credit Assignment in Spiking Neural Networks

Bernstein Conference 2024

Computational principles of systems memory consolidation

COSYNE 2022

Data-driven dynamical systems model of epilepsy development simulates intervention strategies

COSYNE 2022

Dynamical systems analysis reveals a novel hypothalamic encoding of state in nodes controlling social behavior

COSYNE 2022

Integrating deep reinforcement learning agents with the C. elegans nervous system

COSYNE 2022

Integrating deep reinforcement learning agents with the C. elegans nervous system

COSYNE 2022

Learning accurate path integration in ring attractor models of the head direction system

COSYNE 2022

Learning accurate path integration in ring attractor models of the head direction system

COSYNE 2022

Learning generalised representations of behaviour within the hippocampal-entorhinal-prefrontal system

COSYNE 2022

Learning generalised representations of behaviour within the hippocampal-entorhinal-prefrontal system

COSYNE 2022

A Model for Representational Drift: Implications for the Olfactory System

COSYNE 2022

A Model for Representational Drift: Implications for the Olfactory System

COSYNE 2022

Modeling multi-region neural communication during decision making with recurrent switching dynamical systems

COSYNE 2022

Modeling multi-region neural communication during decision making with recurrent switching dynamical systems

COSYNE 2022

Modelling Systems Memory Consolidation with neural fields

COSYNE 2022

Modelling Systems Memory Consolidation with neural fields

COSYNE 2022

Normative Network Regularization for Neural System Identification

COSYNE 2022

Normative Network Regularization for Neural System Identification

COSYNE 2022

Transformation of population representations of sounds throughout the auditory system

COSYNE 2022

Transformation of population representations of sounds throughout the auditory system

COSYNE 2022

Biased AI systems produce biased humans

COSYNE 2023

Brainstem serotonin neurons selectively gate retinal information flow to thalamus

COSYNE 2023

A complementary systems theory of meta-learning

COSYNE 2023

Decomposed linear dynamical systems for C. elegans functional connectivity

COSYNE 2023

Directly comparing fly and mouse visual systems reveals algorithmic similarities for motion detection

COSYNE 2023

Dissecting the Functional Organization of the Serotonergic System at Whole-Brain Scale in C. elegans

COSYNE 2023

Mouse visual cortex as a limited-resource system that self-learns a task-general representation

COSYNE 2023

Parsing neural dynamics with infinite recurrent switching linear dynamical systems

COSYNE 2023

A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations

Bernstein Conference 2024