Topic: C. elegans

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.

Konrad Kording discusses what it would take to reverse engineer the nervous system of C. elegans and what this could teach neuroscience.

Learning interpretable representations of neural dynamics at a population level is a crucial first step to understanding how neural activity relates to perception and behavior. Models of neural dynamics often focus on either low-dimensional projections of neural activity, or on learning dynamical systems that explicitly relate to the neural state over time. We discuss how these two approaches are interrelated by considering dynamical systems as representative of flows on a low-dimensional manifold. Building on this concept, we propose a new decomposed dynamical system model that represents com

Behavior results from neural computations that occur at the level of populations of neurons, and understanding how a given computation produces a particular behavior requires a characterization of how distinct signals interact and evolve. For example, in motor cortex, discrete reaches are generated by a combination of ‘preparatory’ and ‘execution-related’ signals. While the number of computationally-relevant signals is much lower than the number of neurons in a population – and is therefore amenable to dimensionality-reduction methods – standard techniques, like principal components analysis,

A fundamental problem in neuroscience is how neuronal activity in the brain generates organized and stable behaviors across multiple timescales. Recent research in many model organisms, e.g. in monkeys, led to the observations that the neuronal encoding of individual actions, such as preparation for and execution of arm movements, is achieved by large neuronal populations but often lies on low-dimensional manifolds. However, it is largely unclear if such manifolds also coordinate higher dimensional activity patterns that correspond to simultaneously executed fine-tuned movements. In order to a

A long-standing premise in theoretical neuroscience holds that the complex interactions between neurons or, at a different systems level, between networks in the nervous system can be expressed in terms of an underlying system of nonlinear dynamics. A classical approach has been dynamical systems theory, whereby biophysical processes in the brain can be naturally formalized in terms of differential equations (ODEs) that may be capable of offering an explicit and interpretable account of how different biochemical and physiological processes in the brain evolve to produce the complex repertoire

Understanding how neuronal populations interact to process information and generate behaviour is a central goal of neuroscience. This can be challenging due to the high dimensionality of the neuronal populations, the dense interactions between them and the unobserved factors that influence the dynamics. The neuronal manifold hypothesis tackles high dimensionality by positing that the relevant dynamics occurs on a lower-dimensional manifold. While the neuronal manifold may be useful in visualising global dynamics, it tells us very little about how various subsystems interact to produce these dy

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