Geometry of population activity in spiking networks with low-rank structure - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue PLoS Computational Biology Année : 2023

Geometry of population activity in spiking networks with low-rank structure

Résumé

Recurrent network models are instrumental in investigating how behaviorally-relevant computations emerge from collective neural dynamics. A recently developed class of models based on low-rank connectivity provides an analytically tractable framework for understanding of how connectivity structure determines the geometry of low-dimensional dynamics and the ensuing computations. Such models however lack some fundamental biological constraints, and in particular represent individual neurons in terms of abstract units that communicate through continuous firing rates rather than discrete action potentials. Here we examine how far the theoretical insights obtained from low-rank rate networks transfer to more biologically plausible networks of spiking neurons. Adding a low-rank structure on top of random excitatory-inhibitory connectivity, we systematically compare the geometry of activity in networks of integrate-and-fire neurons to rate networks with statistically equivalent low-rank connectivity. We show that the mean-field predictions of rate networks allow us to identify low-dimensional dynamics at constant population-average activity in spiking networks, as well as novel non-linear regimes of activity such as out-of-phase oscillations and slow manifolds. We finally exploit these results to directly build spiking networks that perform nonlinear computations.
Fichier principal
Vignette du fichier
2022.11.18.517093v2.full.pdf (2.05 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04249189 , version 1 (19-10-2023)

Identifiants

Citer

Ljubica Cimeša, Lazar Ciric, Srdjan Ostojic. Geometry of population activity in spiking networks with low-rank structure. PLoS Computational Biology, 2023, 19 (8), pp.e1011315. ⟨10.1101/2022.11.18.517093⟩. ⟨hal-04249189⟩

Collections

ENS-PARIS PSL
2 Consultations
5 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More