Temporal Learning with Biologically Fitted SNN Models

Abstract

Spiking Neural Networks (SNN) can model biological neural networks with different levels of details. There are trade-offs between model fidelity and computation efficiency. Which model is the most appropriate one to use depends on the goal and the computation task. Temporal learning is an important feature of the brain, which requires neural networks to integrate information from the past to solve present computation tasks. Prior work has proposed different SNN models for temporal learning, which includes the Leaky-Integrate-And-Fire (LIF), the Adaptive Leaky-Integrate-And-Fire (ALIF), and the Exponential Adaptive Leaky-Integrate-And-Fire (AdEx). These models capture different biological details and exhibit different learning properties. This work aims to compare the model fidelity and learning performance of these three SNN models. Experimental data for in vitro living neural networks is used to first fit parameters of these three models. An automatic fitting tool is used to match the precise spike timing of the in vitro neurons and the modeled neurons. ALIF and AdEX can match with the spiking timing of the biological neuron better than the LIF does. The fitted models are then compared on a delay task, where the network needs to output values that were input into the network in the recent past. To compute the delay task, the Neural Engineering Framework (NEF) is used to implement a Legendre Memory Unit. Good performance is demonstrated on the delay task using ALIF, which suggests the possibility of implementing the algorithm on in vitro living neural networks. This work proposes a new neuron parameter fitting approach, compares three SNN models, and is the first to use detailed adaptive neurons on the delay task with the NEF approach.