@inproceedings{
plagge2023expanding,
title={Expanding Spiking Neural Networks With Dendrites for Deep Learning},
author={Mark Plagge and Suma G Cardwell and Frances S. Chance},
booktitle={Machine Learning with New Compute Paradigms},
year={2023},
url={https://openreview.net/forum?id=iwYCqNb0B9}
}
As deep learning networks increase in size and performance, so do associated computational costs, approaching prohibitive levels. Dendrites offer powerful nonlinear ``on-the-wire'' computational capabilities, increasing the  expressivity of the point neuron
 while preserving many of the advantages of SNNs. We seek to demonstrate the potential of dendritic computations by combining them with the low-power event-driven computation of Spiking Neural Networks (SNNs) for deep learning applications.
To this end, we have developed a library that adds dendritic computation to SNNs within the PyTorch framework, enabling complex deep learning networks that still retain the low power advantages of SNNs. Our library leverages a dendrite CMOS hardware model to inform the software model, which enables nonlinear computation integrated with snnTorch at scale. Finally, we discuss potential deep learning applications in the context of current state-of-the-art deep learning methods and energy-efficient neuromorphic hardware.
Expanding Spiking Neural Networks With Dendrites for Deep Learning
Mark Plagge Suma G Cardwell Frances S. Chance