State-Space Model Inspired Multiple-Input Multiple-Output Spiking Neurons

• URL: http://arxiv.org/abs/2504.02591v1
• Date: Thu, 03 Apr 2025 13:55:11 GMT
• Title: State-Space Model Inspired Multiple-Input Multiple-Output Spiking Neurons
• Authors: Sanja Karilanova, Subhrakanti Dey, Ayça Özçelikkale,
• Abstract summary: In spiking neural networks (SNNs), the main unit of information processing is the neuron with an internal state.<n>We propose a general multiple-input multiple-output (MIMO) spiking neuron model.<n>We show that for SNNs with a small number of neurons with large internal state spaces, significant performance gains may be obtained by increasing the number of output channels of a neuron.
• Score: 3.2443914909457594
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In spiking neural networks (SNNs), the main unit of information processing is the neuron with an internal state. The internal state generates an output spike based on its component associated with the membrane potential. This spike is then communicated to other neurons in the network. Here, we propose a general multiple-input multiple-output (MIMO) spiking neuron model that goes beyond this traditional single-input single-output (SISO) model in the SNN literature. Our proposed framework is based on interpreting the neurons as state-space models (SSMs) with linear state evolutions and non-linear spiking activation functions. We illustrate the trade-offs among various parameters of the proposed SSM-inspired neuron model, such as the number of hidden neuron states, the number of input and output channels, including single-input multiple-output (SIMO) and multiple-input single-output (MISO) models. We show that for SNNs with a small number of neurons with large internal state spaces, significant performance gains may be obtained by increasing the number of output channels of a neuron. In particular, a network with spiking neurons with multiple-output channels may achieve the same level of accuracy with the baseline with the continuous-valued communications on the same reference network architecture.

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