We present a procedure to decode spatiotemporal spiking patterns in delay coincidence detection networks with stable limit cycles. We apply this to control a simulated e-puck robot to solve the t-maze memory task. This work shows that dynamic memory modules formed by coincidence detection neurones with transmission delays can be effectively coupled to produce adaptive behaviours.

Additional Metadata
Keywords Coincidence detection, Dynamic memory, Embodied cognition, Spiking neural networks, Transmission delays
Persistent URL dx.doi.org/10.1145/2464576.2464590
Conference 15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013
Citation
Jeanson, F. (Francis), & White, A. (2013). Dynamic memory for robot control via delay neural networks. Presented at the 15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013. doi:10.1145/2464576.2464590