Representational learning by optimization of neural manifolds in an olfactory memory network.

ABSTRACT: Higher brain functions depend on experience-dependent representations of relevant information that may be organized by attractor dynamics or by geometrical modifications of continuous "neural manifolds". To explore these scenarios we analyzed odor-evoked activity in telencephalic area pDp of juvenile and adult zebrafish, the homolog of piriform cortex. No obvious signatures of attractor dynamics were detected. Rather, olfactory discrimination training selectively enhanced the separation of neural manifolds representing task-relevant odors from other representations, consistent with predictions of autoassociative network models endowed with precise synaptic balance. Analytical approaches using the framework of manifold capacity revealed multiple geometrical modifications of represent

SUBMITTER: Hu B 

PROVIDER: S-EPMC11601331 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature