Scalability of Large Neural Network Simulations via Activity Tracking With Time Asynchrony and Procedural Connectivity

Autor: Patricia Reynaud-Bouret, Alexandre Muzy, Gilles Scarella, Cyrille Mascart

Publikováno v: Neural computation. 34(9)

We present a new algorithm to efficiently simulate random models of large neural networks satisfying the property of time asynchrony. The model parameters (average firing rate, number of neurons, synaptic connection probability, and postsynaptic dura

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::086407839f772a954397da188173381c
https://pubmed.ncbi.nlm.nih.gov/35896155

Network of interacting neurons with random synaptic weights

Autor: Etienne Tanré, Cyrille Mascart, Julien Chevallier, François Delarue, Paolo Grazieschi, Marta Leocata

Publikováno v: ESAIM: Proceedings and Surveys
ESAIM: Proceedings and Surveys, EDP Sciences, 2019, CEMRACS 2017-Numerical methods for stochastic models: control, uncertainty quantification, mean-field, 65, pp.445-475. ⟨10.1051/proc/201965445⟩
ESAIM: Proceedings and Surveys, Vol 65, Pp 445-475 (2019)
ESAIM: Proceedings and Surveys, 2019, CEMRACS 2017-Numerical methods for stochastic models: control, uncertainty quantification, mean-field, 65, pp.445-475. ⟨10.1051/proc/201965445⟩

Since the pioneering works of Lapicque [17] and of Hodgkin and Huxley [16], several types of models have been addressed to describe the evolution in time of the potential of the membrane of a neuron. In this note, we investigate a connected version o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::35bf2136e8317640f8bbb70dea226783
https://hal.inria.fr/hal-01928990