Paper on LTP and Learning Algorithms

[Stavros Zanos]

(Neural Nets: Foundations to Applications)

The following paper is now available to anyone who sends a request at the 
following adress (use the word "reqLTP3" at the subject field):
 
stavrosz at antigoni.med.auth.gr
 
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AU: Zanos Stavros, 3rd year medical student
AT: University of Thessaloniki School of Medicine
    Thessaloniki, Greece
TI: Quantal Analysis of Hippocampal Long-Term Synaptic Potentiation , and 
    Application to the Design of Biologically Plausible Learning Algorithms 
    for Artificial Neural Networks

AB: Quantal analysis (QA) of synaptic function has been used to examine whether
the expression of long-term potentiation (LTP) in central synapses is mediated 
by a pre- or postsynaptic mechanism. However, it can also be used as a 
physiological model of synaptic transmission and plasticity; use of 
physiological models in network simulations provides reasonably accurate 
approximates of various biological parameters in a computationally efficient 
manner. We describe a stochastic algorithm of synaptic transmission and 
plasticity based on QA data from CA1 hippocampus LTP experiments. We also 
describe the application of such an algorithm in a typical CA1-region 
simulation (a simple self-organizing competitive matrix), and discuss the 
possible benefits of using noisy network elements (in this case, "synapses"). 
We show that the fluctuations in postsynaptic responses under constant static 
synaptic weights introduced by such an algorithm increase the storing capacity 
and the ability of the network to orthogonalize input vectors. A decrease in 
the number of required iterations for every learned input vector is also 
reported. Finally we examine the issue of a hypothetical "computational 
equivalence" of different optimization techniques when applied to similar 
problems, often met in the literature, since our simulation studies suggest 
that even small differences in the learning algorithms used could provide the 
network with a kind of "preference" to specific patterns of performance.
 
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The above paper will appear at the 2nd European Conference of Medical Students 
(May 96), and it has been edited using MS Word-7 (for Win95). Those who 
adressed a request will receive the paper through email as an attachment 
compressed file. Detailed mathematical formalizations used in the simulations 
are available upon request. We welcome questions and/or remarks.
 
Zanos Stavros
Aristotle University of Thessaloniki
School of Life Sciences, Faculty of Medicine