Neuroanatomy seminars

To orchestrate complex behaviors in mammals, the cerebral cortex must continuously transmit its processing results to subcortical regions. While internal cortical processing relies on highly selective sparse codes, these descending cortical output streams employ an enigmatic dense code, characterized by high firing rates and low feature selectivity. This dense population code presents a profound paradox: how can downstream regions extract precise, cognitively relevant signals when most cortical output neurons can be active at any moment? We address this paradox by testing the hypothesis that cortex does not lose information when switching to a dense code. Instead, it utilizes a mechanism for sparse-to-dense coding transformations discovered by my laboratory: thalamocortical synapses target specifically the dendritic initiation zone for calcium action potentials (APs), which enables cortical output neurons to transmit multiple information streams simultaneously via a multiplexed 1-2-3 AP syntax. I will provide first evidence that this remarkable synaptic specificity and coding syntax generalize across long-range pathways. Information coupling via an anatomically and biophysically distinct dendritic nexus may hence be a ubiquitous mechanism for sparse-to-dense coding transformations in cortex. Dissecting these mechanistic origins of cortical output streams is only now possible due to a unique in vivo – in silico approach, perfected over two decades, that my laboratory developed for bridging the gaps between dendritic and population-level computations

Vanessa Siffredi explores why the corpus callosum is central to typical brain development, yet some children born without it or with callosal abnormalities develop comparably to their peers. The talk connects callosal structure and communication between hemispheres with cognition, attention, executive function, memory, language and socio-emotional development. Structural and functional neuroimaging studies of typical development and callosal dysgenesis examine how developing brains compensate for alterations in this major white-matter pathway. Siffredi is affiliated with CHUV and UNIL; Carole Guedj chairs the seminar. Hybrid: Auditoire de pédiatrie Fred Bamatter, HUG, Hôpital des Enfants, Geneva; online via Zoom. Tuesday 29 September 2026, 09:30–10:30 CEST (Europe/Zurich; UTC+2), followed by CIBM news and networking until 11:00. Organized by CIBM Center for Biomedical Imaging — Breakfast and Science. In-person attendance is free without registration. Remote attendees must register through the Zoom link on the event page and select the 29 September occurrence.

Rani Borbara presents brain-wide recordings from more than 150 freely moving C. elegans to examine how anatomical connectivity becomes functional organization during behavior. Cross-animal models map cell-class conditional dependencies and test how synapses, gap junctions, neuromodulation and shared brain states shape circuit dynamics.

This lecture examines the development and evolution of insect learning centres. It asks how expanded neuronal populations and cell-type diversity support the behaviour of social species such as ants. Mechanisms in insect mushroom bodies are compared with changes in the mammalian cortex.

Modern experimental techniques now reveal the structure and function of neural circuits at unprecedented scale and resolution. How can we use this wealth of data to understand how cells and circuits implement computations underlying behaviour? Achieving this goal requires models that are consistent with biophysical mechanisms and circuit dynamics, yet flexible enough to capture behaviourally relevant computations. We develop simulation-based machine learning methods that address this challenge. I will show how these approaches—in combination with connectomic measurements—make it possible to build large-scale mechanistic models of the fruit fly visual system. Our methods generalize across systems and scales, defining a new way to study biological systems by algorithmically learning interpretable models that reveal how structure and dynamics gives rise to behaviour. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2025-12-17. Recording duration: 00:45:20.

Over the past decade our community has made substantial progress in the construction of anatomically detailed network models of the cortical tissue. Thanks to advances in computer hardware and simulation technology, researchers can now routinely work with these models at the natural density of neurons and synapses. Moreover, the availability of cloud services means that such investigations can be carried out without having to install either the model or the simulation software. A recent workshop analyzed the impact of a specific model of the cortical microcircuit, published ten years ago . The model has been reused in multiple contexts: for reproduction studies, validation of mean-field approaches, exploration of methods of model sharing, and as a building block for larger models. Although the model was less successful in inspiring further neuroscientific studies than the authors of the original work had hoped, it became a de facto benchmark for neuromorphic computing systems. It sparked a constructive race for ever shorter simulation times and lower energy consumption. The quantitative comparison of different platforms reveals qualitative differences between conventional and neuromorphic hardware and limits of speed-up. The structure of the model is based on light microscopy because these were the data available at the time. Guided by simulation results and physiological evidence, the original publication hypothesized a preference of excitatory neurons for inhibitory targets. Modern electron microscopy data of cortical volumes combined with AI based reconstruction techniques is capable of resolving individual synaptic connections. This advances the concept of digital twins of the cortical network to a new level of precision, and has already enabled us to confirm the assumption of target type specifity underlying earlier models. Maybe with the progress sketched here, our community is at a transition point where it becomes easier to cooperatively and incrementally work on models with a larger explanatory scope. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2025-12-10. Recording duration: 00:39:15.

Accurate perception of the environment is a constructive process that requires integration of external bottom-up sensory signals with internally-generated top-down information reflecting past experiences and current aims. Decades of work have elucidated how sensory neocortex processes physical stimulus features. In contrast, examining how memory-related-top-down information is encoded and integrated with bottom-up signals has long been challenging. Here, I will discuss our recent work pinpointing the outermost layer 1 of neocortex as a central hotspot for processing of experience-dependent top-down information threat during perception, one of the most fundamentally important forms of sensation.

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

Functional connectomics reveals general wiring rule in mouse visual cortex

Connectomic traces of Hebbian plasticity in the entorhinalhippocampal system

Connectomic reconstruction of a cortical column

In 2023, the FlyWire Consortium released the neuronal wiring diagram of an adult fly brain. This contains as a corollary the first complete wiring diagram of a visual system, which has been used to identify all 200+ cell types that are intrinsic to the Drosophila optic lobe. About half of these cell types were previously unknown, and less than 20% have ever been recorded by a physiologist. I will argue that plausible functions for many cell types can be guessed by interpreting the wiring diagram. VVTNS Fifth Season Closing Lecture. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2025-06-25. Recording duration: 00:42:39.

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.

Structure and function of the hippocampal CA3 module

Visual restoration after decades of blindness is now becoming possible by means of retinal and cortical prostheses, as well as emerging stem cell and gene therapeutic approaches. After restoring visual perception, however, a key question remains. Are there optimal means and methods for retraining the visual cortex to process visual inputs, and for learning or relearning to “see”? Up to this point, it has been largely assumed that if the sensory loss is visual, then the rehabilitation focus should also be primarily visual. However, the other senses play a key role in visual rehabilitation due to the plastic repurposing of visual cortex during blindness by audition and somatosensation, and also to the reintegration of restored vision with the other senses. I will present multisensory neuroimaging results, cortical thickness changes, as well as behavioral outcomes for patients with Retinitis Pigmentosa (RP), which causes blindness by destroying photoreceptors in the retina. These patients have had their vision partially restored by the implantation of a retinal prosthesis, which electrically stimulates still viable retinal ganglion cells in the eye. Our multisensory and structural neuroimaging and behavioral results suggest a new, holistic concept of visual rehabilitation that leverages rather than neglects audition, somatosensation, and other sensory modalities.

Synaptic architecture of a memory engram in the mouse hippocampus

In the age of connectomics, it is increasingly important to understand how the nodes and edges of a brain's anatomical network, or "connectome," gives rise to neural signaling and neural function. I will present the first comprehensive brain-wide cell-resolved causal measurements of how neurons signal to one another in response to stimulation in the nematode C. elegans. I will compare this signal propagation atlas to the worm's known connectome to address fundamental questions of structure and function in the brain.

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.

Neuroscience is experiencing unprecedented growth in dataset size both within individual brains and across populations. Large-scale, multimodal datasets are transforming our understanding of brain structure and function, creating opportunities to address previously unexplored questions. However, managing this increasing data volume requires new training and technology approaches. Modern data technologies are reshaping neuroscience by enabling researchers to tackle complex questions within a Ph.D. or postdoctoral timeframe. I will discuss cloud-based platforms such as brainlife.io, that provide scalable, reproducible, and accessible computational infrastructure. Modern data technology can democratize neuroscience, accelerate discovery and foster scientific transparency and collaboration. Concrete examples will illustrate how these technologies can be applied to mapping brain connectivity, studying human learning and development, and developing predictive models for traumatic brain injury (TBI). By integrating cloud computing and scalable data-sharing frameworks, neuroscience can become more impactful, inclusive, and data-driven..

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.