  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.
