Expressive Dendrites in Spiking Networks

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. By leveraging dendrites in a deep learning framework, we examine the capabilities of dendrites via coincidence detection and comparison in a machine learning task with a SNN. Finally, we discuss potential deep learning applications in the context of current state-of-the-art deep learning methods and energy-efficient neuromorphic hardware.