Learning of oscillatory correlated patterns in a cortical network by a STDP-based learning rule.
In this paper, we propose an iterative learning rule that allows the imprinting of correlated oscillatory patterns in a model of the hippocampus able to work as an associative memory for oscillatory spatio-temporal patterns. We analyze the dynamics in the Fourier domain, showing how the network selectively amplify or distort the Fourier components of the input, in a manner which depends on the imprinted patterns. We also prove that the proposed iterative local rule converges to the pseudo-inverse rule generalized to oscillatory patterns.
Marinaro
Maria
M
Dipartimento di Fisica E.R.Caianiello, Universita di Salerno, Via S.Allende Baronissi, SA, Italy.
Scarpetta
Silvia
S
Yoshioka
Mashaiko
M
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D005583:Fourier Analysis; D006624:Hippocampus / Q000502:physiology; D006801:Humans; D007173:Imprinting, Psychological / Q000502:physiology; D007395:Interneurons / Q000502:physiology; D007858:Learning / Q000502:physiology*; D008568:Memory / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009433:Neural Inhibition / Q000502:physiology; D009473:Neuronal Plasticity / Q000502:physiology*; D009474:Neurons / Q000502:physiology; D017966:Pyramidal Cells / Q000502:physiology; D013569:Synapses / Q000502:physiology