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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Learning long sequences in spiking neural networks.

Matei-Ioan Stan¹, Oliver Rhodes²

¹Department of Computer Science, The University of Manchester, Manchester, UK. matei.stan@manchester.ac.uk.

Scientific Reports

|September 20, 2024

Summary

This summary is machine-generated.

State space models (SSMs) combined with spiking neural networks (SNNs) show promise for energy-efficient long-range sequence modeling. This approach outperforms Transformers and current SNNs on key benchmarks, paving the way for efficient large language models on neuromorphic hardware.

Keywords:

Long range dependencies Sequence modelling Spiking neural networks State space models

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Area of Science:

Artificial Intelligence
Computational Neuroscience
Machine Learning

Background:

Spiking neural networks (SNNs) offer energy-efficient computation but lag behind Transformers in sequential tasks due to RNN limitations and training challenges.
State space models (SSMs) have emerged as efficient alternatives to Transformers for sequence modeling.

Purpose of the Study:

To investigate the integration of state-of-the-art SSMs with SNNs for long-range sequence modeling.
To evaluate the performance of SSM-based SNNs against Transformers and existing SNNs.

Main Methods:

Systematic investigation of SSM-SNN intersection for long-range sequence modeling.
Introduction of a novel feature mixing layer to enhance SNN accuracy.
Benchmarking against established long-range sequence modeling tasks and sequential image classification.

Main Results:

SSM-based SNNs outperformed Transformer models on all tasks in a long-range sequence modeling benchmark.
SSM-based SNNs achieved superior performance compared to state-of-the-art SNNs with fewer parameters in sequential image classification.
A novel feature mixing layer improved SNN accuracy, questioning prior assumptions about binary activations.

Conclusions:

SSM-based SNNs represent a significant advancement for energy-efficient long-range sequence modeling.
This research enables the deployment of powerful SSM architectures, like large language models, on neuromorphic hardware.
The findings open new avenues for efficient and brain-inspired AI.