Temporal pattern identification using spike-timing dependent plasticity

Abstract

This paper addresses the question of the functional role of the dual application of positive and negative Hebbian time dependent plasticity rules, in the particular framework of reinforcement learning tasks. Our simulations take place in a recurrent network of spiking neurons with inhomogeneous synaptic weights. The network spontaneously displays a self-sustained activity.A Spike-Timing Dependent Plasticity (STDP) rule is combined with its opposite, the anti-STDP. A local regulation mechanism moreover maintains the output neuron in the vicinity of a reference frequency, which forces the global dynamics to be maintained in a softly disordered regime.This approach is tested on a simple discrimination task which requires short-term memory : temporal pattern identication. We show that such temporal patterns can be categorized, and present tracks for future improvements.

STDP in recurrent neuronal networks

“…For instance, in the liquid-state machine a recurrently connected network behaves as a reservoir that performs many arbitrary operations on the inputs, which allows simple supervised training to discriminate between different classes of input (Maass et al, 2002 ). Recent studies have shown that STDP applied on the recurrent network can boost the performance of the detection by such a system, by tuning the operations performed by the reservoir, which can be seen as a projection of the input signals onto a large-dimensional space (Henry et al, 2007 ; Carnell, 2009 ; Lazar et al, 2009 ). The resulting information encoding is then distributed, but hidden, in the learned synaptic structure, which can be analyzed in the spiking activity at a fine time scale, e.g., by polychronized groups (Paugam-Moisy et al, 2008 ).…”

Enaction-Based Artificial Intelligence: Toward Co-evolution with Humans in the Loop

“…Learning by reinforcement, which allows an entity to use its past experience to modify its behavior is used as much for symbolic connotation models (Holland & Reitman, 1978;Wilson, 1987;Butz, Goldberg, & Stolzmann, 2000;Gerard, Stolzmann, & Sigaud, 2002) as for neurocomputational approaches (Daucé, Quoy, Cessac, Doyon, & Samuelides, 1998;Henry, Daucé, & Soula, 2007). We discuss "symbolic connotation" approaches first as they are based on discrete variables and a selection of atomic actions.…”

Fast unsupervised learning for visual pattern recognition using spike timing dependent plasticity

“…where V thr is the post-synaptic potential threshold and V (t) represents dy- Spike timing dependent plasticity (STDP) [7], [8], [9], [10], [11] has been proved to be a quiet effective learning rule by neuroscientists, which adjusts the effi- spike fires slightly earlier than the post-synaptic spike, the associated synaptic efficacy will be potentiated (LTP). While the associated synaptic efficacy will be depressed (LTD) if the presynaptic synaptic spike fires later than the postsynaptic spike.…”

“…To learn these spiking patterns, spike timing dependent plasticity (STDP) [7], [8], [9], [10], [11], a biological process that adjusts the efficacy of synaptic connections based on the relative timing of post-synaptic spikes and its input presynaptic spikes, is one of the most biological plausible learning rule. Like Hebb's postulate [12], it emphasizes "Cells that fire together, wire together".…”