Energy-efficient network activity from disparate circuit parameters.

ABSTRACT: Neural circuits can produce similar activity patterns from vastly different combinations of channel and synaptic conductances. These conductances are tuned for specific activity patterns but might also reflect additional constraints, such as metabolic cost or robustness to perturbations. How do such constraints influence the range of permissible conductances? Here we investigate how metabolic cost affects the parameters of neural circuits with similar activity in a model of the pyloric network of the crab Cancer borealis. We present a machine learning method that can identify a range of network models that generate activity patterns matching experimental data and find that neural circuits can consume largely different amounts of energy despite similar circuit activity. Furthermore,

SUBMITTER: Deistler M 

PROVIDER: S-EPMC9636970 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature