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Depression-Biased Reverse Plasticity Rule Is Required for Stable Learning at Top-Down Connections

Overview of attention for article published in PLoS Computational Biology, March 2012
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Title
Depression-Biased Reverse Plasticity Rule Is Required for Stable Learning at Top-Down Connections
Published in
PLoS Computational Biology, March 2012
DOI 10.1371/journal.pcbi.1002393
Pubmed ID
Authors

Kendra S. Burbank, Gabriel Kreiman

Abstract

Top-down synapses are ubiquitous throughout neocortex and play a central role in cognition, yet little is known about their development and specificity. During sensory experience, lower neocortical areas are activated before higher ones, causing top-down synapses to experience a preponderance of post-synaptic activity preceding pre-synaptic activity. This timing pattern is the opposite of that experienced by bottom-up synapses, which suggests that different versions of spike-timing dependent synaptic plasticity (STDP) rules may be required at top-down synapses. We consider a two-layer neural network model and investigate which STDP rules can lead to a distribution of top-down synaptic weights that is stable, diverse and avoids strong loops. We introduce a temporally reversed rule (rSTDP) where top-down synapses are potentiated if post-synaptic activity precedes pre-synaptic activity. Combining analytical work and integrate-and-fire simulations, we show that only depression-biased rSTDP (and not classical STDP) produces stable and diverse top-down weights. The conclusions did not change upon addition of homeostatic mechanisms, multiplicative STDP rules or weak external input to the top neurons. Our prediction for rSTDP at top-down synapses, which are distally located, is supported by recent neurophysiological evidence showing the existence of temporally reversed STDP in synapses that are distal to the post-synaptic cell body.

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Geographical breakdown

Country Count As %
United States 2 3%
United Kingdom 1 2%
Switzerland 1 2%
Unknown 61 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 32%
Researcher 7 11%
Student > Master 5 8%
Student > Bachelor 5 8%
Student > Doctoral Student 4 6%
Other 15 23%
Unknown 8 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 20%
Neuroscience 10 15%
Engineering 8 12%
Computer Science 7 11%
Physics and Astronomy 6 9%
Other 14 22%
Unknown 7 11%