Connectome

…experimental designs and benchmarks, connectome proofreading bottlenecks, definitions of memory, differences across model organisms and the technological and conceptual limits of connectome decoding. The publisher notes imperfect audio and provides a subtitled video version.

…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.

…Brand new comprehensive connectomes of the fruit fly ventral nerve cord (VNC) provide an opportunity to study rhythmogenic walking circuits at a synaptic resolution. In this project, we deve

Recent technological advances have enabled the measurement of the ‘connectome,’ or synaptic wiring diagram, of neurons across large brain regions or entire brains. Theoretical work has long sought to link connectivity features of large neural networks to their emergent dynamics. Nevertheless, how to integrate connectomics data and neural recordings to determine the function of a neural system remains challenging, given the physiological properties of individual neurons that remain unconstrained. To address this, we developed a theory that describes the space of solutions when inferring activit

…explores insights from the fruit fly connectome, emphas

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

We can now measure the connectivity of every neuron in a neural circuit, but we are still blind to other biological details, including the dynamical characteristics of each neuron. The degree to which connectivity measurements alone can inform understanding of neural computation is an open question. Here we show that with experimental measurements of only the connectivity of a biological neural network, we can predict the neural activity underlying a specified neural computation. We constructed a model neural network with the experimentally determined connectivity for 64 cell types in the moti

Transcranial direct current stimulation (tDCS) has proven to be effective in reducing the seizure frequency in patients with epilepsy. This therapy greatly benefits from designing personalized electrode montages (electrode positions and currents) that target the appropriate areas of the brain. In the state-of-the-art pipelines, a specialized physician selects these areas and a biophysical model of the patient’s head is used to find an optimal electrode montage that inhibits them. In the Galvani project, we aim to go a step forward and develop a pipeline that uses personalized Neurotwins for mo

We present a comprehensive dataset of neural activity, compiled from 11 neuroimaging experiments in the nematode worm C. elegans. These source datasets, while collected under different experimental protocols, all measure neural activity via changes in calcium fluorescence in labeled subsets of the hermaphrodite worm’s 300 neurons. Our contribution is to standardize these disparate datasets into a unified framework including ordering the neural data from labeled neurons consistently, resampling traces to a common timestep, and including a feature mask to indicate which neurons were labeled in e

…and global scaling factor on connectome), ideally incorporating measures of uncertainty. In this work, we provide f

Mechanosensory neurons across the body have axon projections that terminate in distinct arborizations in the brain corresponding to body location. These somatotopically-organized projections provide the interface with neural circuits that direct body location-appropriate behavioral responses, such as aimed grooming. Using electron microscopy-based reconstructions, we investigate how circuits transform mechanical stimulations into location-appropriate behavior. We previously described the somatotopic organization of bristle mechanosensory neurons (BMNs) around the Drosophila head that elicit a

…model organism with a fully mapped connectome. C.elegans displays stereotyped behaviour, with specific gaits of locomotion (like forward/backward movement) matching distinct neuronal brain activity dynamics, which are comprised of synchronous network activity patterns (Kato 2015, Uzel 2022). Here, we aim to understand how the C.elegans connectome supports such dynamics.Our studies (Avila 2023) have suggested that these network sy

…the first vertebrate basal ganglia connectome, compris

In this talk, I will explain a theory of connectome-constrained neural networks in which a “student” networks is trained to reproduce the activity of a ground-truth “teacher”, representing a neural system for which a connectome is available. Unlike standard paradigms with unconstrained connectivity, here both networks have the same connectivity but they have different biophysical parameters, reflecting uncertainty in neuronal and synaptic properties. We find that the connectome is often insufficient to constrain the dynamics of networks that perform a specific task, illustrating the difficulty

In the 17th century, physician Marcello Malpighi observed the existence of distinctive patterns of ridges and sweat glands on fingertips. This was a major breakthrough, and originated a long and continuing quest for ways to uniquely identify individuals based on fingerprints, a technique massively used until today. It is only in the past few years that technologies and methodologies have achieved high-quality measures of an individual’s brain to the extent that personality traits and behavior can be characterized. The concept of “fingerprints of the brain” is very novel and has been boosted th

…This theory forms the basis of connectome-based computational models, which combine real human data and theoret

Learning in neural networks requires assigning the right values to thousands to trillions or more of individual connections, so that the network as a whole produces the desired behavior. Neuroscientists have gained insights into this “credit assignment” problem through decades of experimental, modeling, and theoretical studies. This has suggested key roles for synaptic eligibility traces and top-down feedback signals, among other factors. Here we study the potential contribution of another type of signaling that is being revealed in greater and greater fidelity by ongoing molecular and genomic

…local properties of the human connectome, it remains unclear whether it also shapes the asymmetric signaling that arises from higher-order topology. Here, we used network control theory to examine the amount of energy required to propagate dynamics across the sensory-fugal axis. Our results revealed an asymmetry in this energy

…of neural assemblies model the connectome as a directed Erd\"

Serotonin signaling is evolutionarily ancient and controls many aspects of behavior. The principles by which serotonin acts on its diverse types of receptors to modulate neural circuit activity and resultant behavior is poorly understood. Here, we examine how serotonin released from a food-responsive neuron in C. elegans alters neural activity at whole-brain scale to induce adaptive behaviors, such slow locomotion and increased pharyngeal pumping. A comprehensive in vivo genetic analysis of the six C. elegans serotonin receptors identifies three core serotonin receptors that sense serotonin re

Across species, many navigational spatial representations, including those of head direction and traveling direction, require the integration of changes in sensory information induced by self-motion. In Drosophila, connectomic analysis points to a variety of so-called LNO neurons as key inputs for this information to the central complex (CX), a navigational center of insect brains. Recent studies have reported that the responses of several of these neurons correlate with self-motion and with optic flow resulting from rotations and translations in 2D. However, flying animals, like bats and inse

…elegans connectome encod

…has completed the proofreading of a connectome for a Drosophila female brain which contains both complete hemispheres of the central brain and includes neurons that receive inputs in the optic lobes. Here, we dissect the network properties of the complete central brain of the fruit fly. We characterized th

Efficient navigation requires animals to track their position, velocity and heading direction (HD). Some animals’ behavior suggests that they also track uncertainties about these navigational variables, and make strategic use of these uncertainties, in line with a Bayesian computation. Ring-attractor networks have been proposed to estimate and track these navigational variables, for instance in the HD system of the fruit fly Drosophila. However, such networks are not designed to incorporate a notion of uncertainty, and therefore seem unsuited to implement dynamic Bayesian inference. Here, we c