BioLCNet: Reward-modulated Locally Connected Spiking Neural Networks

TL;DR

BioLCNet is a biologically inspired spiking neural network that uses reward-modulated learning and local filters to perform visual classification tasks, aiming for compatibility with neuromorphic hardware.

Contribution

The paper introduces BioLCNet, a novel locally connected spiking neural network that employs reward-modulated STDP and biologically plausible local filters for visual learning.

Findings

Robust reward mechanism under varying target responses.

Effective classification on MNIST and XOR MNIST datasets.

Biologically inspired architecture enhances learning plausibility.

Abstract

Brain-inspired computation and information processing alongside compatibility with neuromorphic hardware have made spiking neural networks (SNN) a promising method for solving learning tasks in machine learning (ML). Spiking neurons are only one of the requirements for building a bio-plausible learning model. Network architecture and learning rules are other important factors to consider when developing such artificial agents. In this work, inspired by the human visual pathway and the role of dopamine in learning, we propose a reward-modulated locally connected spiking neural network, BioLCNet, for visual learning tasks. To extract visual features from Poisson-distributed spike trains, we used local filters that are more analogous to the biological visual system compared to convolutional filters with weight sharing. In the decoding layer, we applied a spike population-based voting scheme to determine the decision of the network. We employed Spike-timing-dependent plasticity (STDP) for learning the visual features, and its reward-modulated variant (R-STDP) for training the decoder based on the reward or punishment feedback signal. For evaluation, we first assessed the robustness of our rewarding mechanism to varying target responses in a classical conditioning experiment. Afterwards, we evaluated the performance of our network on image classification tasks of MNIST and XOR MNIST datasets.