RT Journal Article
SR Electronic
T1 Representational learning by optimization of neural manifolds in an olfactory memory network
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2024.11.17.623906
DO 10.1101/2024.11.17.623906
A1 Hu, Bo
A1 Temiz, Nesibe Z.
A1 Chou, Chi-Ning
A1 Rupprecht, Peter
A1 Meissner-Bernard, Claire
A1 Titze, Benjamin
A1 Chung, SueYeon
A1 Friedrich, Rainer W.
YR 2024
UL http://biorxiv.org/content/early/2024/11/18/2024.11.17.623906.abstract
AB 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 representational manifolds that supported the classification of task-relevant sensory information. Manifold capacity predicted odor discrimination across individuals, indicating a close link between manifold geometry and behavior. Hence, pDp and possibly related recurrent networks store information in the geometry of representational manifolds, resulting in joint sensory and semantic maps that may support distributed learning processes.Competing Interest StatementThe authors have declared no competing interest.