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Tutorial 2 - The Leaky Integrate-and-Fire Neuron — snntorch 1.0.0 documentation

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The snnTorch tutorial series is based on the following paper. If you find these resources or code useful in your work, please consider citing the following source: Jason K. Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu. “Training Spiking Neural Networks Using Lessons From Deep Learning”. Proceedings of the IEEE, 111(9) September 2023. Note Google Colab Local Notebook (download via GitHub) In this tutorial, you will: Learn the fundamentals of the leaky integrate-and-fire (LIF) neuron model Use snnTorch to implement a first order LIF neuron Install the latest PyPi distribution of snnTorch: A large variety of neuron models are out there, ranging from biophysically accurate models (i.e., the Hodgkin-Huxley models) to the extremely simple artificial neuron that pervades all facets of modern deep learning. Hodgkin-Huxley Neuron Models− While biophysical models can reproduce electrophysiological results with a hi

Tutorial written by Jason K. Eshraghian (www.ncg.ucsc.edu) The snnTorch tutorial series is based on the following paper. If you find these resources or code useful in your work, please consider citing the following source: Introduction In this tutorial, you will: Learn the fundamentals of the leaky integrate-and-fire (LIF) neuron model Use snnTorch to implement a first order LIF neuron Install the latest PyPi distribution of snnTorch: $ pip install snntorch # imports import snntorch as snn from snntorch import spikeplot as splt from snntorch import spikegen import torch import…

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