Coherence detection in a spiking neuron via hebbian learning

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It is generally assumed that neurons in the central nervous system communicate through temporal firing patterns. As a first step, we will study the learning of a layer of realistic neurons in the particular case where the relevant messages are formed by temporally correlated patterns, or synfire patterns. The model is a layer of Integrate-and-Fire (IF) neurons with synaptic current dynamics that adapts by minimizing a cost according to a gradient descent scheme. This leads to a rule similar to Spike-Time Dependent Hebbian Plasticity (STDHP). Our results show that the rule that we derive is biologically plausible and leads to the detection of the coherence in the input in an unsupervised way. An application to shape recognition is shown as an illustration.


Figure 1: Neural Model of adaptative synchrony detection using STDP (Perrinet, 2002) : (left) Input spikes (with a synfire pattern at t =25ms), are (middle) modulated in time and amplitude forming postsynaptic current pulses and are finally (right) integrated at the soma. When the potential (plain line) reaches the threshold (dotted line), a spike is emitted and the potential is decreased. A sample PSP (at synapse 1) is also shown.


Figure 3: Coherence detection in (Perrinet, 2002) : (left) different input patterns (t = 100ms, 300ms, 500ms, 700ms, 900ms) are (right) learnt by the system: only one neuron per input fires (100 learning steps).


All material (c) L. Perrinet. Please check the copyright notice.


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