New paper on the spike-timing-dependent plasticity

[Hide Cateau]

Dear All,

I would like to announce the availability of the following paper at my
site:http://brain.inf.eng.tamagawa.ac.jp/cateau/hide.index.html

Hideyuki Cateau & Tomoki Fukai, A stochastic method to predict the consequence of 
arbitrary forms of spike-timing-dependent plasticity, 
Neural Computation (2002) in press.

This paper enables us to predict the consequence of arbitrary forms the
spike-timing-dependent plasticity without doing any simulations. 

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A stochastic method to predict the consequence of arbitrary forms of
spike-timing-dependent plasticity

Hideyuki Cateau† and Tomoki Fukai†#
†Core Research for the Evolutional Science and Technology
Program(CREST),JST, Tokyo 1948610, Japan
# Department of Engineering, Tamagawa University, Tokyo 1948610,Japan

Abstract
Synapses in various neural preparations exhibit spike-timing-dependent
plasticity (STDP) with a variety of learning window functions. The
window functions determine the magnitude and the polarity of synaptic
change according to the time difference of pre- and postsynaptic spikes.
 Numerical experiments revealed that STDP learning with a single-exponential
 window function resulted in a bimodal distribution of synaptic
conductances as a consequence of competition between synapses. A
slightly modified window function, however, resulted in a unimodal
distribution, rather than a bimodal distribution. Since various window
functions have been observed in neural preparations, we develop an
unambiguous mathematical method to calculate the conductance distribution
 for any given window function. Our method is based on the Fokker-Planck
 equation to determine the conductance distribution and on the
Ornstein-Uhlenbeck process to characterize the membrane potential
fluctuations.
Demonstrating that our method reproduces the known quantitative results
of STDP learning, we apply the method to the type of STDP learning found
 recently in the CA1 region of the rat hippocampus. We find that this
learning can result in nearly optimized competition between synapses.
Meanwhile, we find that the type of STDP learning found in the cerebellum-like
 structure of electric fish can result in all-or-none synapses, i.e.,
either all the synaptic conductances are maximized or none of them
become significantly large. Our method also determines the window
function that optimizes synaptic competition.


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Hideyuki Cateau
Core Research for the Evolutional
Science and Technology Program(CREST), JST
Lab. for mathematical information engineering,
Dept. Info-Communication Engineering,
Tamagawa Univ.
6-1-1 Tamagawa-Gakuen, Machida-shi, Tokyo 1948610, Japan
cateau at brain.inf.eng.tamagawa.ac.jp
http://brain.inf.eng.tamagawa.ac.jp/members.html
phone: +81-42-739-8434, fax:+81-42-739-7135
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