Linked Life Data

16822008

Source:http://linkedlifedata.com/resource/pubmed/id/16822008

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2006-7-6
pubmed:abstractText
It is believed that both Hebbian and homeostatic mechanisms are essential in neural learning. While Hebbian plasticity selectively modifies synaptic connectivity according to activity experienced, homeostatic plasticity constrains this change so that neural activity is always within reasonable physiological limits. Recent experiments reveal spike timing-dependent plasticity (STDP) as a new type of Hebbian learning with high time precision and heterosynaptic plasticity (HSP) as a new homeostatic mechanism acting directly on synapses. Here, we study the effect of STDP and HSP on randomly connected neural networks. Despite the reported successes of STDP to account for neural activities at the single-cell level, we find that, surprisingly, at the network level, networks trained using STDP alone cannot seem to generate realistic neural activities. For instance, STDP would stipulate that past sensory experience be maintained forever if it is no longer activated. To overcome this difficulty, motivated by the fact that HSP can induce strong competition between sensory experiences, we propose a biophysically plausible learning rule by combining STDP and HSP. Based on the Fokker-Planck theory and extensive numerical computations, we demonstrate that HSP and STDP operated on different time scales can complement each other, resulting in more realistic network activities. Our finding may provide fresh insight into the learning mechanism of the brain.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1054-1500
pubmed:author
pubmed:issnType
Print
pubmed:volume
16
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
023105
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Cooperation of spike timing-dependent and heterosynaptic plasticities in neural networks: a Fokker-Planck approach.
pubmed:affiliation
School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S.