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Dual Coding with STDP in a Spiking Recurrent Neural Network Model of the Hippocampus

Overview of attention for article published in PLoS Computational Biology, July 2010
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Title
Dual Coding with STDP in a Spiking Recurrent Neural Network Model of the Hippocampus
Published in
PLoS Computational Biology, July 2010
DOI 10.1371/journal.pcbi.1000839
Pubmed ID
Authors

Daniel Bush, Andrew Philippides, Phil Husbands, Michael O'Shea

Abstract

The firing rate of single neurons in the mammalian hippocampus has been demonstrated to encode for a range of spatial and non-spatial stimuli. It has also been demonstrated that phase of firing, with respect to the theta oscillation that dominates the hippocampal EEG during stereotype learning behaviour, correlates with an animal's spatial location. These findings have led to the hypothesis that the hippocampus operates using a dual (rate and temporal) coding system. To investigate the phenomenon of dual coding in the hippocampus, we examine a spiking recurrent network model with theta coded neural dynamics and an STDP rule that mediates rate-coded Hebbian learning when pre- and post-synaptic firing is stochastic. We demonstrate that this plasticity rule can generate both symmetric and asymmetric connections between neurons that fire at concurrent or successive theta phase, respectively, and subsequently produce both pattern completion and sequence prediction from partial cues. This unifies previously disparate auto- and hetero-associative network models of hippocampal function and provides them with a firmer basis in modern neurobiology. Furthermore, the encoding and reactivation of activity in mutually exciting Hebbian cell assemblies demonstrated here is believed to represent a fundamental mechanism of cognitive processing in the brain.

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Geographical breakdown

Country Count As %
Germany 6 5%
United States 5 5%
United Kingdom 5 5%
Brazil 2 2%
China 2 2%
Canada 2 2%
Chile 1 <1%
Belarus 1 <1%
France 1 <1%
Other 4 4%
Unknown 81 74%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 33%
Researcher 23 21%
Student > Master 9 8%
Professor 7 6%
Professor > Associate Professor 7 6%
Other 16 15%
Unknown 12 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 22%
Neuroscience 23 21%
Computer Science 23 21%
Engineering 11 10%
Medicine and Dentistry 5 5%
Other 11 10%
Unknown 13 12%