Dynamic memory for robot control via delay neural networks
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.
|Keywords||Coincidence detection, Dynamic memory, Embodied cognition, Spiking neural networks, Transmission delays|
|Conference||15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013|
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