Research article summary (published 19 Oct 2009):

Embedding multiple trajectories in simulated recurrent neural networks in a self-organizing manner.

Full Abstract

Complex neural dynamics produced by the recurrent architecture of neocortical circuits is critical to the cortex's computational power. However, the synaptic learning rules underlying the creation of stable propagation and reproducible neural trajectories within recurrent networks are not understood. Here, we examined synaptic learning rules with the goal of creating recurrent networks in which evoked activity would: (1) propagate throughout the entire network in response to a brief stimulus while avoiding runaway excitation; (2) exhibit spatially and temporally sparse dynamics; and (3) incorporate multiple neural trajectories, i.e., different input patterns should elicit distinct trajectories. We established that an unsupervised learning rule, termed presynaptic-dependent scaling (PSD), can achieve the proposed network dynamics. To quantify the structure of the trained networks, we developed a recurrence index, which revealed that presynaptic-dependent scaling generated a functionally feedforward network when training with a single stimulus. However, training the network with multiple input patterns established that: (1) multiple non-overlapping stable trajectories can be embedded in the network; and (2) the structure of the network became progressively more complex (recurrent) as the number of training patterns increased. In addition, we determined that PSD and spike-timing-dependent plasticity operating in parallel improved the ability of the network to incorporate multiple and less variable trajectories, but also shortened the duration of the neural trajectory. Together, these results establish one of the first learning rules that can embed multiple trajectories, each of which recruits all neurons, within recurrent neural networks in a self-organizing manner.

Author information

Author/s: Liu, Jian K (JK); Buonomano, Dean V (DV);

Affiliation: Department of Mathematics and Neurobiology, University of California, Los Angeles, Los Angeles, California 90095, USA.

Journal and publication information

Publication Type: Journal Article

Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience (J Neurosci), published in United States. (Language: eng)

Reference: 2009-Oct; vol 29 (issue 42) : pp 13172-81

Dates: Created 2009/10/22; Completed 2009/11/06;

PMID: 19846705, status: MEDLINE (last retrieval date: 11/6/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Action Potentials - physiology Computer Simulation Humans Learning - physiology Models, Neurological Nerve Net - physiology

Nerve Net - physiology Neural Networks (Computer) Neuronal Plasticity - physiology Neurons - physiology Nonlinear Dynamics Time Factors

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