Versatile Learning without Synaptic Plasticity in a Spiking Neural Network

Abstract

Learning in the brains cerebral cortex is widely attributed to synaptic plasticity among cortical neurons. However, a growing body of evidence suggests that alternative processes, such as modulation of intrinsic excitability or gating by subcortical inputs, may also serve as important learning mechanisms. We developed the Bias Adaptive Neural Firing Framework (BANFF) as a simplified model of such phenomena embodied by a learnable bias current for each neuron of a rate-based network. Here, we extend this framework to spiking neural networks. We show that learning such biases enables one recurrent spiking neural network with fixed and random synaptic weights to perform well on nine tasks spanning classification, regression, and closed-loop dynamical systems mimicry. The network learnt a unique bias set for each task, and unlike recurrent synapse-based learning, new learning did not interfere with previous learning. The network was robust to non-stationary F-I curves (spike frequency adaptation), and biases could be learned with a learning algorithm (e-prop) that is more biologically plausible than stock gradient descent. Overall, we show that the BANFF can be extended from rate-based to spiking neural networks, maintaining good multi-task performance with a single network of spiking neurons.