Paper: Spike-timing-dependent learning rule

[Masahiko Yoshioka]

Dear Connectionists,

I am pleased to announce the availability of my recent paper
and of one potentially related paper.

Recent paper:
-------------

"The spike-timing-dependent learning rule to encode spatiotemporal
patterns in a network of spiking neurons"

M. Yoshioka, Phys. Rev. E (in press) 

Available at

   http://arXiv.org/abs/cond-mat/0110070 (preprint)

Abstract:

We study associative memory neural networks based on the Hodgkin-Huxley
type of spiking neurons. We introduce the spike-timing-dependent
learning rule, in which the time window with the negative part
as well as the positive part is used to describe the biologically
plausible synaptic plasticity. The learning rule is applied to
encode a number of periodical spatiotemporal patterns, which
are successfully reproduced in the periodical firing pattern
of spiking neurons in the process of memory retrieval. The global
inhibition is incorporated into the model so as to induce the
gamma oscillation. The occurrence of gamma oscillation turns
out to give appropriate spike timings for memory retrieval of
discrete type of spatiotemporal pattern. The theoretical analysis
to elucidate the stationary properties of perfect retrieval state
is conducted in the limit of an infinite number of neurons and
shows the good agreement with the result of numerical simulations.
The result of this analysis indicates that the presence of the
negative and positive parts in the form of the time window contributes
to reduce the size of crosstalk term, implying that the time
window with the negative and positive parts is suitable to encode
a number of spatiotemporal patterns. We draw some phase diagrams,
in which we find various types of phase transitions with change
of the intensity of global inhibition. 


Related paper:
--------------

"Associative memory storing an extensive number of patterns based
on a network of oscillators with distributed natural frequencies
in the presence of external white noise"

M. Yoshioka and M. Shiino, Phys. Rev. E 61, 4732 (2000)

Available at

   http://pre.aps.org/ (subscription is required)
   http://xxx.lanl.gov/abs/cond-mat/9903316 (preprint)

Abstract:

We study associative memory based on temporal coding in which
successful retrieval is realized as an entrainment in a network
of simple phase oscillators with distributed natural frequencies
under the influence of white noise. The memory patterns are assumed
to be given by uniformly distributed random numbers on $[0,2\pi)$
so that the patterns encode the phase differences of the oscillators.
To derive the macroscopic order parameter equations for the network
with an extensive number of stored patterns, we introduce the
effective transfer function by assuming the fixed-point equation
of the form of the TAP equation, which describes the time-averaged
output as a function of the effective time-averaged local field.
Properties of the networks associated  with synchronization phenomena
for a discrete symmetric natural frequency distribution with
three frequency components are studied based on the order parameter
equations, and are shown to be in good agreement with the results
of numerical simulations. Two types of retrieval states are found
to occur with respect to the degree of synchronization, when
the size of the width of the natural frequency distribution is
changed.


Regards,

Masahiko Yoshioka
Brain Science Institute, RIKEN
Hirosawa 2-1, Wako-shi, Saitama, 351-0198, Japan