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Prospective coding by spiking neurons

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Brains can learn to predict. But can a single neuron do so? Building on the work of Urbanczik & Senn (2014) on learning by the dendritic prediction of somatic spiking, a plasticity rule that implements supervised, unsupervised and reinforcement learning in a spiking neuron model, I will show that a slightly longer window of synaptic potentiation allows a spiking neuron to match its current firing rate to its own expected future discounted firing rate. For instance, if an originally neutral event is repeatedly followed by an event that elevates the firing rate of a neuron, the originally neutral event will eventually also elevate the neuron’s firing rate. The plasticity rule is a form of spike timing dependent plasticity in which a presynaptic spike followed by a postsynaptic spike leads to potentiation. Even if the plasticity window has a width of 20 milliseconds, associations on the time scale of seconds can be learned. I will illustrate prospective coding with three examples: learning to predict a time varying input, learning to predict the next stimulus in a delayed paired-associate task and learning with a recurrent network to reproduce a temporally compressed version of a sequence. In the special case that the signal to be predicted encodes reward, the neuron learns to predict the discounted future reward and learning is closely related to the temporal difference learning algorithm TD(lambda).

This talk is part of the Computational Neuroscience series.

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