Relationship between an input sequence and asymmetric connections formed by theta phase precession and STDP

Abstract

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. © 2009 Springer Berlin Heidelberg.