Neural networks with optimized single-neuron adaptation uncover biologically plausible regularization.

ABSTRACT: Neurons in the brain have rich and adaptive input-output properties. Features such as heterogeneous f-I curves and spike frequency adaptation are known to place single neurons in optimal coding regimes when facing changing stimuli. Yet, it is still unclear how brain circuits exploit single-neuron flexibility, and how network-level requirements may have shaped such cellular function. To answer this question, a multi-scaled approach is needed where the computations of single neurons and neural circuits must be considered as a complete system. In this work, we use artificial neural networks to systematically investigate single-neuron input-output adaptive mechanisms, optimized in an end-to-end fashion. Throughout the optimization process, each neuron has the liberty to modify its nonlinear ac

SUBMITTER: Geadah V 

PROVIDER: S-EPMC11676530 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature