scholarly journals An ultra-compact leaky-integrate-and-fire model for building spiking neural networks

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
M. J. Rozenberg ◽  
O. Schneegans ◽  
P. Stoliar
Keyword(s):  
Neural Networks ◽  
Spiking Neural Networks ◽  
Fire Model ◽  
Integrate And Fire

scholarly journals Event-Driven Simulations of Nonlinear Integrate-and-Fire Neurons

2007 ◽  
Vol 19 (12) ◽  
pp. 3226-3238 ◽  
Author(s):  
Arnaud Tonnelier ◽  
Hana Belmabrouk ◽  
Dominique Martinez
Keyword(s):  
Neural Networks ◽  
Spiking Neural Networks ◽  
Synaptic Currents ◽  
Fire Model ◽  
Integrate And Fire ◽  
Fire Models ◽  
Event Driven

Event-driven strategies have been used to simulate spiking neural networks exactly. Previous work is limited to linear integrate-and-fire neurons. In this note, we extend event-driven schemes to a class of nonlinear integrate-and-fire models. Results are presented for the quadratic integrate-and-fire model with instantaneous or exponential synaptic currents. Extensions to conductance-based currents and exponential integrate-and-fire neurons are discussed.

PCMO RRAM for Integrate-and-Fire Neuron in Spiking Neural Networks

2018 ◽  
Vol 39 (4) ◽  
pp. 484-487 ◽  
Author(s):  
S. Lashkare ◽  
S. Chouhan ◽  
T. Chavan ◽  
A. Bhat ◽  
P. Kumbhare ◽  
...  
Keyword(s):  
Neural Networks ◽  
Spiking Neural Networks ◽  
Integrate And Fire ◽  
Integrate And Fire Neuron

scholarly journals Spontaneous Dynamics of Asymmetric Random Recurrent Spiking Neural Networks

2006 ◽  
Vol 18 (1) ◽  
pp. 60-79 ◽  
Author(s):  
Hédi Soula ◽  
Guillaume Beslon ◽  
Olivier Mazet
Keyword(s):  
Neural Networks ◽  
Steady State ◽  
Numerical Simulations ◽  
Weight Distribution ◽  
Spiking Neural Networks ◽  
Mathematical Formalism ◽  
Recurrent Networks ◽  
Integrate And Fire ◽  
Independence Hypothesis ◽  
Spiking Activity

In this letter, we study the effect of a unique initial stimulation on random recurrent networks of leaky integrate-and-fire neurons. Indeed, given a stochastic connectivity, this so-called spontaneous mode exhibits various nontrivial dynamics. This study is based on a mathematical formalism that allows us to examine the variability of the afterward dynamics according to the parameters of the weight distribution. Under the independence hypothesis (e.g., in the case of very large networks), we are able to compute the average number of neurons that fire at a given time—the spiking activity. In accordance with numerical simulations, we prove that this spiking activity reaches a steady state. We characterize this steady state and explore the transients.

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