Unsupervised learning of control signals and their encodings in Caenorhabditis elegans whole-brain recordings.

ABSTRACT: A major goal of computational neuroscience is to understand the relationship between synapse-level structure and network-level functionality. Caenorhabditis elegans is a model organism to probe this relationship due to the historic availability of the synaptic structure (connectome) and recent advances in whole brain calcium imaging techniques. Recent work has applied the concept of network controllability to neuronal networks, discovering some neurons that are able to drive the network to a certain state. However, previous work uses a linear model of the network dynamics, and it is unclear if the real neuronal network conforms to this assumption. Here, we propose a method to build a global, low-dimensional model of the dynamics, whereby an underlying global linear dynamical system

SUBMITTER: Fieseler C 

PROVIDER: S-EPMC7811586 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature