A scalable implementation of the recursive least-squares algorithm for training spiking neural networks

Benjamin J. Arthur*, Christopher M. Kim, Susu Chen, Stephan Preibisch, Ran Darshan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Training spiking recurrent neural networks on neuronal recordings or behavioral tasks has become a popular way to study computations performed by the nervous system. As the size and complexity of neural recordings increase, there is a need for efficient algorithms that can train models in a short period of time using minimal resources. We present optimized CPU and GPU implementations of the recursive least-squares algorithm in spiking neural networks. The GPU implementation can train networks of one million neurons, with 100 million plastic synapses and a billion static synapses, about 1,000 times faster than an unoptimized reference CPU implementation. We demonstrate the code's utility by training a network, in less than an hour, to reproduce the activity of > 66, 000 recorded neurons of a mouse performing a decision-making task. The fast implementation enables a more interactive in-silico study of the dynamics and connectivity underlying multi-area computations. It also admits the possibility to train models as in-vivo experiments are being conducted, thus closing the loop between modeling and experiments.

Original languageEnglish
Article number1099510
JournalFrontiers in Neuroinformatics
Volume17
DOIs
StatePublished - 2023

Funding

FundersFunder number
National Institutes of Health
Howard Hughes Medical Institute
National Institute of Diabetes and Digestive and Kidney Diseases
Janelia Research Campus

    Keywords

    • Neuropixels dense silicon probe
    • balanced networks
    • dynamical system
    • excitation-inhibition
    • integrate and fire neuron

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