Multi-timescale synaptic plasticity on analog neuromorphic hardware

TL;DR

This paper demonstrates the implementation of a calcium-based synaptic plasticity rule on the BrainScaleS-2 neuromorphic hardware, enabling accelerated and accurate simulation of complex plasticity mechanisms in spiking neural networks.

Contribution

It introduces a hardware-compatible implementation of calcium-based plasticity on BrainScaleS-2, combining analog calcium dynamics with digital processing for efficient neuromorphic simulation.

Findings

Accurately emulates synaptic plasticity across multiple protocols

Validates hardware implementation against software models

Achieves extended simulation runtimes with neuromorphic acceleration

Abstract

As numerical simulations grow in complexity, their demands on computing time and energy increase. Accelerators for numerical computation offer significant efficiency gains in many computationally-intensive scientific fields, but their use in simulating spiking neural networks in computational neuroscience is hindered by challenges, mainly in effective parallelism and efficient use of memory in the presence of sparse representations and sparse communication. The BrainScaleS architectures are neuromorphic substrates that can emulate spiking neural networks at accelerated timescales compared to real time, which offers an advantage for studying complex plasticity rules that require extended simulation runtimes. This work presents the implementation of a calcium-based plasticity rule that integrates calcium dynamics based on the synaptic tagging-and-capture hypothesis on the BrainScaleS-2 system. The implementation of the plasticity rule for a single synapse involves incorporating the calcium dynamics and the plasticity rule equations. The calcium dynamics are mapped to the analog circuits of BrainScaleS-2, while the plasticity rule equations are numerically solved on its embedded digital processors. The main hardware constraints include the speed of the processors and the use of integer arithmetic. By adjusting the timestep of the numerical solver and introducing stochastic rounding, we demonstrate that BrainScaleS-2 accurately emulates a single synapse following a calcium-based plasticity rule across four established stimulation protocols and validate our implementation against a software reference model.