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Type: Journal article
Title: A nonlinear cable framework for bidirectional synaptic plasticity
Author: Iannella, N.
Launey, T.
Abbott, D.
Tanaka, S.
Citation: PloS one, 2014; 9(8):e102601
Publisher: Public Library of Science
Issue Date: 2014
ISSN: 1932-6203
1932-6203
Abstract: Finding the rules underlying how axons of cortical neurons form neural circuits and modify their corresponding synaptic strength is the still subject of intense research. Experiments have shown that internal calcium concentration, and both the precise timing and temporal order of pre and postsynaptic action potentials, are important constituents governing whether the strength of a synapse located on the dendrite is increased or decreased. In particular, previous investigations focusing on spike timing-dependent plasticity (STDP) have typically observed an asymmetric temporal window governing changes in synaptic efficacy. Such a temporal window emphasizes that if a presynaptic spike, arriving at the synaptic terminal, precedes the generation of a postsynaptic action potential, then the synapse is potentiated; however if the temporal order is reversed, then depression occurs. Furthermore, recent experimental studies have now demonstrated that the temporal window also depends on the dendritic location of the synapse. Specifically, it was shown that in distal regions of the apical dendrite, the magnitude of potentiation was smaller and the window for depression was broader, when compared to observations from the proximal region of the dendrite. To date, the underlying mechanism(s) for such a distance-dependent effect is (are) currently unknown. Here, using the ionic cable theory framework in conjunction with the standard calcium based plasticity model, we show for the first time that such distance-dependent inhomogeneities in the temporal learning window for STDP can be largely explained by both the spatial and active properties of the dendrite.
Keywords: Cerebral Cortex; Neurons; Dendrites; Synapses; Animals; Humans; Calcium; Neuronal Plasticity; Algorithms; Models, Neurological; Synaptic Potentials
RMID: 0030007078
DOI: 10.1371/journal.pone.0102601
Appears in Collections:Electrical and Electronic Engineering publications

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