Acetylcholine and learning in a cortical associative memory
Neural Computation
Weakly connected neural networks
Weakly connected neural networks
Hebbian imprinting and retrieval in oscillatory neural networks
Neural Computation
Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning
Neural Computation
Phase precession through synaptic facilitation
Neural Computation
Phase precession and recession with STDP and Anti-STDP
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
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Many experimental results have generated renewed appreciation that precise temporal synchronization, and synchronized oscillatory activity in distributed groups of neurons, may play a fundamental role in perception, memory and sensory computation, especially to encode relationship and increase saliency. Here we investigate how precise temporal synchronization of groups of neurons can be memorized as attractors of the network dynamics. Multiple patterns, each corresponding to different groups of synchronized oscillatory activity, are encoded using a temporally asymmetric learning rule inspired to the spike-timing-dependent plasticity recently observed in cortical area. In this paper we compare the results previously obtained for phase-locked oscillation in the random phases hypothesis, to the case of patterns with synchronous subgroups of neurons, each pattern having neurons with only Q = 4 possible values of the phase.