Stationary-State Statistics of a Binary Neural Network Model with Quenched Disorder

Autor: Diego Fasoli, Stefano Panzeri

Publikováno v: Entropy, Vol 21, Iss 7, p 630 (2019)

In this paper, we study the statistical properties of the stationary firing-rate states of a neural network model with quenched disorder. The model has arbitrary size, discrete-time evolution equations and binary firing rates, while the topology and

Externí odkaz: https://doaj.org/article/7c470c500da842b5ae24f7b00947bd80

A model of the mouse cortex with attractor dynamics explains the structure and emergence of rsfMRI co-activation patterns

Autor: Diego Fasoli, Ludovico Coletta, Daniel Gutierrez-Barragan, Alessandro Gozzi, Stefano Panzeri

Neural network models have been instrumental in revealing the foundational principles of whole-brain dynamics. Here we describe a new whole-cortex model of mouse resting-state fMRI (rsfMRI) activity. Our model implements neural input-output nonlinear

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0364b8069948460867a83e836a33988f
https://doi.org/10.1101/2022.04.28.489908

Pattern Storage, Bifurcations, and Groupwise Correlation Structure of an Exactly Solvable Asymmetric Neural Network Model

Autor: Anna Cattani, Diego Fasoli, Stefano Panzeri

Publikováno v: Neural computation. 30(5)

Despite their biological plausibility, neural network models with asymmetric weights are rarely solved analytically, and closed-form solutions are available only in some limiting cases or in some mean-field approximations. We found exact analytical s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9ef6316ef4e70d8cd3e273aaf1456fb5
https://pubmed.ncbi.nlm.nih.gov/29566351

Bifurcation Analysis of a Sparse Neural Network with Cubic Topology

Autor: Anna Cattani, Diego Fasoli, Stefano Panzeri

Publikováno v: Mathematical and Theoretical Neuroscience ISBN: 9783319682969

We study analytically the changes of dynamics of a firing-rate network model with cubic topology. The present study is performed by extending to this sparse network a formalism we previously developed for the bifurcation analysis of fully-connected c

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::890c5dcabfa82fa86f18555b82d5bd3e
https://doi.org/10.1007/978-3-319-68297-6_5

A formalism for evaluating analytically the cross-correlation structure of a firing-rate network model

Autor: Olivier Faugeras, Stefano Panzeri, Diego Fasoli

Publikováno v: Journal of Mathematical Neuroscience
Journal of Mathematical Neuroscience, BioMed Central, 2015, ⟨10.1186/s13408-015-0020-y⟩
Journal of Mathematical Neuroscience, 2015, ⟨10.1186/s13408-015-0020-y⟩

We introduce a new formalism for evaluating analytically the cross-correlation structure of a finite-size firing-rate network with recurrent connections. The analysis performs a first-order perturbative expansion of neural activity equations that inc

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0aca76342193fc833e4739541bffa360
https://pubmed.ncbi.nlm.nih.gov/25852981