This paper investigates the dynamical and control properties of a discrete spiking neural network model with axonal delays. After examining contemporary work on spike timing as a mechanism for neural coding, we introduce a simple axonal delay network model which, via coincidence detection, demonstrates the presence of biologically observed regimes such as sustained firing and the emergence of synchrony. We establish delay criteria allowing for the classification of three distinct regimes including global synchrony, complex firing, and dissipation. We then proceed to test this model in a robot light seeking task. Results show that evolving network delays is sufficient for solving the task. We conclude by hypothesizing that global synchronous firing is more suited to reactive behaviours while complex firing patterns may serve as an organizing mechanism for more indirect processing.

Additional Metadata
Keywords axonal delays, coincidence detection, embodied cognition, neural adaptation, neural coding, spiking neural network
Persistent URL dx.doi.org/10.1145/2330163.2330181
Conference 14th International Conference on Genetic and Evolutionary Computation, GECCO'12
Citation
Jeanson, F. (Francis), & White, A. (2012). Evolving axonal delay neural networks for robot control. Presented at the 14th International Conference on Genetic and Evolutionary Computation, GECCO'12. doi:10.1145/2330163.2330181