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
Oscillatory Networks with Hebbian Matrix of Connections
IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
Computational theories on the function of theta oscillations
Biological Cybernetics
Patterns of Synchrony in Neural Networks with Spike Adaptation
Neural Computation
Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning
Neural Computation
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Associative memory of phase-coded spatiotemporal patterns in leaky Integrate and Fire networks
Journal of Computational Neuroscience
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In this paper we review a model of learning based on the Spike Timing Dependent Plasticity (STDP), introduced in our previous works, and we extend the analysis to the case of multiple frequencies, showing how the learning rule is able to encode multiple spatio-temporal oscillatory patterns with distributed frequencies as dynamical attractors of the network. After learning, each encoded oscillatory spatio-temporal pattern who satisfy the stability condition forms a dynamical attractor, such that, when the state of the system falls in the basin of attraction of one such dynamical attractor, it is recovered with the same encoded phase relationship among units. Here we extend the analysis introduced in our previous work, to the case of distributed frequencies, and we study the relation between stability of multiple frequencies and the shape of the learning window. The stability of the dynamical attractors play a critical role. We show that imprinting into the network a spatio-temporal pattern with a new frequency of oscillation can destroy the stability of patterns encoded with different frequency of oscillation. The system is studied both with numerical simulations, and analytically in terms of order parameters when a finite number of dynamic attractors are encoded into the network in the thermodynamic limit.