Homeostatic synaptic normalization optimizes learning in network models of neural population codes.

ABSTRACT: Studying and understanding the code of large neural populations hinge on accurate statistical models of population activity. A novel class of models, based on learning to weigh sparse nonlinear Random Projections (RP) of the population, has demonstrated high accuracy, efficiency, and scalability. Importantly, these RP models have a clear and biologically plausible implementation as shallow neural networks. We present a new class of RP models that are learned by optimizing the randomly selected sparse projections themselves. This 'reshaping' of projections is akin to changing synaptic connections in just one layer of the corresponding neural circuit model. We show that Reshaped RP models are more accurate and efficient than the standard RP models in recapitulating the code of tens of cortic

SUBMITTER: Mayzel J 

PROVIDER: S-EPMC11649238 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature