Visual Analysis of Leaky Integrate-and-Fire Spiking Neuron Models and Circuits

Emulating biologically plausible online learning in spiking neural networks (SNNs) will enable the next generation of energy-efficient neuromorphic architectures. While software leads the way in terms of exploring various Machine Learning (ML) algorithms and applications, bridging the gap between ha...

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Bibliographic Details
Published in2024 IEEE 67th International Midwest Symposium on Circuits and Systems (MWSCAS) pp. 1437 - 1440
Main Authors Sedighi, Sara, Afrin, Farhana, Onyejegbu, Elonna, Cantley, Kurtis D.
Format Conference Proceeding
LanguageEnglish
Published IEEE 11.08.2024
Subjects
Analytical models
Circuits
decay rate
Hardware
LIF neuron
Neurons
Software
Spiking neural network
Spiking neural networks
Threshold dynamics
Visualization
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Summary:Emulating biologically plausible online learning in spiking neural networks (SNNs) will enable the next generation of energy-efficient neuromorphic architectures. While software leads the way in terms of exploring various Machine Learning (ML) algorithms and applications, bridging the gap between hardware (devices and circuits) and software is crucial to accurately predict network properties, especially at large scale. This work compares behavior of a spiking neuron circuit simulated with Cadence Spectre to a Python model implemented with a custom spiking neuron model. The results demonstrate that the two exhibit the same spiking characteristics over a range of parameter values, confirming that the more versatile Python model indeed has a hardware equivalent.
ISSN:1558-3899
DOI:10.1109/MWSCAS60917.2024.10658798