Integrating programmable plasticity in experiment descriptions for analog neuromorphic hardware

TL;DR

This paper introduces a unified high-level language for describing experiments and plasticity rules in the BrainScaleS-2 neuromorphic hardware, enabling flexible and scalable simulation of complex neural plasticity.

Contribution

It presents an integrated framework that connects experiment topology, protocol, and plasticity algorithms in a high-level language for BrainScaleS-2.

Findings

Enables flexible description of neural experiments

Supports scalable and parallel plasticity computations

Facilitates complex plasticity rule implementation

Abstract

The study of plasticity in spiking neural networks is an active area of research. However, simulations that involve complex plasticity rules, dense connectivity/high synapse counts, complex neuron morphologies, or extended simulation times can be computationally demanding. The BrainScaleS-2 neuromorphic architecture has been designed to address this challenge by supporting "hybrid" plasticity, which combines the concepts of programmability and inherently parallel emulation. In particular, observables that are expensive in numerical simulation, such as per-synapse correlation measurements, are implemented directly in the synapse circuits. The evaluation of the observables, the decision to perform an update, and the magnitude of an update, are all conducted in a conventional program that runs simultaneously with the analog neural network. Consequently, these systems can offer a scalable and flexible solution in such cases. While previous work on the platform has already reported on the use of different kinds of plasticity, the descriptions for the spiking neural network experiment topology and protocol, and the plasticity algorithm have not been connected. In this work, we introduce an integrated framework for describing spiking neural network experiments and plasticity rules in a unified high-level experiment description language for the BrainScaleS-2 platform and demonstrate its use.