Trajectory generation using a spiking neuron implementation of dynamic movement primitives

27th Annual Meeting for the Society for the Neural Control of Movement, 2017

Travis DeWolf, Chris Eliasmith

Abstract

We present a trajectory generating circuit using efficient function representation coding in a spiking neural network that can generate multiple complex trajectories dynamically from a single network. Integrating multiple trajectories within a single network allows us to explore the transitions between movements. We suggest that this kind of network is a possible mechanism for efficiently storing a wide array of movement features in the cortex, and compare our results to experimental data.

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27th Annual Meeting for the Society for the Neural Control of Movement

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