Experience-Dependent Axonal Plasticity in Large-Scale Spiking Neural Network Simulations

Axonal plasticity describes the biological phenomenon in which the myelin sheath thickness and the amplification of a signal change due to experience. Recent studies show this to be important for sequence learning and synchronization of temporal information. In spiking neural networks (SNNs), the time a spike travels from the presynaptic neuron along the axon until it reaches a postsynaptic neuron is an essential principle of how SNNs encode information. In simulators for large scale SNN models such as CARLsim, this time is modeled as synaptic delays with discrete values from one to several milliseconds. To simulate neural activity in large-scale SNNs efficiently, delays are transformed as indices to optimized structures that are built once before the simulation starts. As a consequence, and in contrast to synaptic weights, delays are not directly accessible as scalar data in the runtime memory. In the present paper, we introduce axonal delay learning rules in the SNN simulator CARLsim that can be updated during runtime. To demonstrate this feature, we implement the recent E-Prop learning rule in a recurrent SNN capable of flexible navigation. Compared to other studies for axonal plasticity that are based on LIF neurons, we also develop the SNN based on the more biologically realistic Izhikevich neural model. The present work serves as reference implementation for neuromorphic hardware that encode delays and serves as an interesting alternative to synaptic plasticity.