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SUMMARY:Stability versus Neuronal Specialization for STDP: Long-Tail Weigh
 t Distributions Solve the Dilemma
DTSTART:20110516T141500
DTSTAMP:20260406T224454Z
UID:7e5a38991204009adac748139b6ef3ca894359f55a70b8fadf7de773
CATEGORIES:Conferences - Seminars
DESCRIPTION:Matthieu GILSON\, Lab for Neural Circuit Theory\, RIKEN Brain 
 Science Institute\nSpike timing-dependent plasticity (STDP) is hypothesise
 d to structure neuronal networks in the brain depending on their precise s
 piking activity. In this talk\, we focus on the functional implications of
  an experimentally observed property of STDP\, the dependence of the weigh
 t update on the current strength of the synaptic weight. This weight depen
 dence crucially shapes the distribution of plastic synaptic strengths. We 
 propose a model of weight-dependent STDP that can generate long-tail (e.g.
 \, lognormal) distribution of synaptic strengths that have been observed i
 n recent experiments. We show that our new model offers a balance between 
 stability for the weight distribution and competition induced by input spi
 ke-time correlations\, taking the "best" of additive and multiplicative ST
 DP. This allows a neuron to robustly and quickly adapt to changes in the s
 tructure of input spike trains\, e.g.\, in a context of short-term memory 
 for example. In a recurrently connected network\, our new model allows the
  persistence of a stable weight structure susceptible to represent the str
 ucture of input spike trains. 
LOCATION:SV 3510
STATUS:CONFIRMED
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