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Useful Computation with Coincidence Detection

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If you have a question about this talk, please contact Philip Sterne .

Using a simple model of a spiking neuron we show how to construct a distributed phase code. This type of code uses the firing time of a neuron relative to other neurons firing to represent information. We show that the network is able to do: pattern completion, simultaneous recall, 1-D line attractors, 2-D plane attractors. (Much of the work is inspired by Seb Will´s 2004 dissertation, but with several extensions.)

The last 20 minutes will look at work-in-progress that maps analogue inputs (e.g. sensory input) into a distributed phase code.

This talk is part of the Inference Group series.

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