Path planning using a spiking neuron algorithm with axonal delays - Krichmar, Jeffrey L. (2016 IEEE Congress on Evolutionary Computation (CEC), 2016)
A path planning algorithm is introduced that uses the timing of spiking neurons to create efficient routes. The algorithm is inspired by recent evidence showing activity-dependent plasticity of axon myelination after learning. Using this finding as inspiration, the algorithm's learning rule varies the simulated axon conductance velocity between neurons based on the relative cost of traversing the environment. In terms of path length and path cost, the spiking algorithm is as good or better than other path planners. However, the present spiking algorithm has the added advantage of adapting to change and context by altering axon delays in response to environmental experience. Because the spiking algorithm is suitable for implementation on neuromorphic hardware, it has the potential of realizing orders of magnitude gains in power efficiency and computational gains through parallelization, and thus should offer advantages for small, embedded systems.