Modeling Axonal Plasticity in Artificial Neural Networks

Abstract

Axonal growth and pruning are the brain’s primary method of controlling the structured sparsity of its neural circuits. Without long-distance axon branches connecting distal neurons, no direct communication is possible. Artificial neural networks have almost entirely ignored axonal growth and pruning, instead relying on implicit assumptions that prioritize dendritic/synaptic learning above all other concerns. This project proposes a new model called the axon game, which allows biologically-inspired axonal plasticity dynamics to be incorporated into most artificial neural network models in a computationally efficient manner. First, we demonstrate that the axon game replicates multiple previously defined pre-synaptic cortical maps. Second, we demonstrate that the axon game integrated with a synaptic learning model similar to the Laterally Interconnected Synergetically Self-Organizing Map (LISSOM), can simulate the interaction of axonal plasticity and synaptic plasticity within one model creating both pre-synaptic and post-synaptic cortical maps. Finally, it is shown that pre-synaptic and post-synaptic maps can be decoupled from one another. This decoupling depends on the relative sizes of dendritic and axonal arbors, and indicates a novel theoretical prediction about how axonal and synaptic dynamics interact.