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Dynamic memory for robot control via delay neural networks

We present a procedure to decode spatio-temporal 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.