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Learning Precisely Timed Patterns of Spikesold_uid | 12473 |
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title | Learning Precisely Timed Patterns of Spikes |
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start_date | 2013/05/21 |
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schedule | 15h30-16h30 |
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online | no |
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location_info | salle Celan |
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details | GNT Theoretical Neuroscience Seminar Series |
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summary | Experiments have revealed precisely timed patterns of spikes in
several neuronal systems, raising the possibility that these temporal
signals are used by the brain to encode and transmit sensory
information. It is thus important to understand the capability of
neural circuits to learn to produce stimulus specific temporally
precise spikes. Learning to spike at given times is challenging since
the spike threshold and the ensuing reset induce a strongly nonlinear
dependence of the voltage on the value of the synaptic weights. We
develop two learning algorithms, High Threshold Projection Learning
and First Error Learning, that are inspired by the well known
Perceptron rule and accomplish the task. High Threshold Projection
Learning converges in finite time to exactly fit the desired spike
pattern if is realizable. First Error Learning is a more biologically
plausible rule, which converges to solutions with finite precision.
The algorithms are employed to establish the capacity of a leaky
integrate-and-fire neuron using a temporal code. We use theoretical
considerations to derive the scaling of the capacity and to predict
its numerical value in the low output rate regime. To show that our
algorithms are able to learn behaviorally meaningful tasks from real
neuronal data, we apply them to neuronal recordings of song birds. In
addition, this proposes a novel way to estimate the information
content carried by spike patterns which is accessible to neuronal
architectures. Finally, we generalize our learning algorithms to
perform learning of precise spike timing patterns in arbitrary
neuronal architecture that includes feedback and recurrent
connections. |
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responsibles | Koechlin |
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