This paper demonstrates the feasibility of dynamic memory in transmission delay coincidence detection networks. We present a low complexity, procedural algorithm for determining delay connectivity for the control of a simulated e-puck robot to solve the t-maze memory task. This work shows that dynamic memory modules need not undergo structural change during learning but that peripheral structures could be alternate candidates for this. Overall, this supports the view that delay coincidence detection networks can be effectively coupled to produce embodied adaptive behaviours.

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Keywords Coincidence Detection, Dynamic Memory, Embodied Cognition, Spiking Neural Networks, Transmission Delays
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Series Lecture Notes in Computer Science
Jeanson, F. (Francis), & White, A. (2013). Dynamic memory for robot control using delay-based coincidence detection neurones. In Lecture Notes in Computer Science. doi:10.1007/978-3-642-40728-4_35