Training Spiking Neural Networks with an Adaptive Leaky Integrate-and-Fire Neuron

2020 ◽  
Author(s):  
Mingyu Sung ◽  
Yongtae Kim
Keyword(s):  
Neural Networks ◽  
Spiking Neural Networks ◽  
Integrate And Fire ◽  
Integrate And Fire Neuron

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 Does High Firing Irregularity Enhance Learning?

2011 ◽  
Vol 23 (3) ◽  
pp. 656-663 ◽  
Author(s):  
Chris Christodoulou ◽  
Aristodemos Cleanthous
Keyword(s):  
Neural Networks ◽  
Spike Timing ◽  
Spiking Neural Networks ◽  
Benchmark Problem ◽  
Integrate And Fire ◽  
Eligibility Trace ◽  
Firing Irregularity ◽  
The Brain ◽  
High Firing ◽  
Integrate And Fire Neuron

In this note, we demonstrate that the high firing irregularity produced by the leaky integrate-and-fire neuron with the partial somatic reset mechanism, which has been shown to be the most likely candidate to reflect the mechanism used in the brain for reproducing the highly irregular cortical neuron firing at high rates (Bugmann, Christodoulou, & Taylor, 1997 ; Christodoulou & Bugmann, 2001 ), enhances learning. More specifically, it enhances reward-modulated spike-timing-dependent plasticity with eligibility trace when used in spiking neural networks, as shown by the results when tested in the simple benchmark problem of XOR, as well as in a complex multiagent setting task.

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.

Sign in / Sign up

Export Citation Format

Share Document