Learning invariance from transformation sequences
Neural Computation
1994 Special Issue: A model of hippocampal function
Neural Networks - Special issue: models of neurodynamics and behavior
Memory encoding by theta phase precession in the hippocampal network
Neural Computation
Cognitive Map Formation Through Sequence Encoding by Theta Phase Precession
Neural Computation
Journal of Cognitive Neuroscience
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Neural dynamics of the "theta phase precession" in the hippocampus are known to have a computational advantage with respect to memory encoding. Computational studies have shown that a combination of theta phase precession and spike-timing-dependent plasticity (STDP) can serve as recurrent networks in various methods of memory storage. Conversely, the proposed dynamics of neurons and synapses appear too complicated to give any clear perspective on the network formation in the case of a large number of neurons (1000). In this paper, we theoretically analyzed the evolution of synaptic weights under a given input sequence. We present our results as a simple equation demonstrating that the magnitude of the slow component of an input sequence giving successive coactivation results in asymmetric connection weights. Further comparison with computer experiments confirms the predictability of network formation.