[MURG] from binary to spiking neurons: a statistical mechanics approach to network modelling. (fwd from vale@mth.kcl.ac.uk)

[Eugen Leitl]

----- Forwarded message from Valeria Del Prete <vale at mth.kcl.ac.uk> -----

From: Valeria Del Prete <vale at mth.kcl.ac.uk>
Date: Thu, 6 Nov 2003 09:29:08 +0100
To: comp-neuro at neuroinf.org
Subject: from binary to spiking neurons: a statistical mechanics approach to
 network modelling. 
Reply-To: Valeria Del Prete <vale at mth.kcl.ac.uk>

Dear all,

I want to announce two recent works on network and spiking neuron models.
you find the titles and abstracts below. they are both available for
download at 

http://www.mth.kcl.ac.uk/~vale/research.html

best,
Vale


**************************************************************************
Valeria Del Prete                         Strand, WC2R2LS, London, UK
Department of Mathematics                 tel. +44 (0)20 7848 1197
King's college London                     fax. +44 (0)20 7848 2017
e-mail vale at mth.kcl.ac.uk                 http://www.mth.kcl.ac.uk/~vale/
**************************************************************************
              
            "..in girum imus nocte, ecce, et consumimur igni"

V Del Prete and ACC Coolen (2003) NON EQUILIBRIUM STATISTICAL MECHANICS 
OF RECURRENT NETWORKS WITH REALISTIC NEURONS. Proceedings of the CNS03
meeting.

Experimental evidence suggests that spike timing might be used by neurons
to process and store information. Unfortunately, the mathematical analysis
of recurrent networks with spiking neurons is highly non trivial. Most
analytical studies have therefore focused on rate-based models, whereas
spiking models tend to be studied numerically. In order to bridge this
gap, we propose an effective spiking neuron model which still allows for
the application of non-equilibrium statistical mechanical techniques. 
The model is flexible and its parameters can be adjusted in order to match 
real data. We analyze the population dynamics in the simple case of
constant excitatory synapses.

ACC Coolen and V Del Prete (2003). STATISTICAL MECHANICS BEYOND 
THE HOPFIELD MODEL: SOLVABLE PROBLEMS IN NEURAL NETWORK THEORY.
Reviews in the Neurosciences, 14 (1-2), 181-193.

We present four 'case study' examples of solvable problems in the theory
of recurrent neural networks, which are relevant to our understanding of
information processing in the brain, but which are also interesting from a
purely statistical mechanical point of view, even at the level of simple
models (which helps in stimulating interdisciplinary work). The examples
concern issues in network dynamics, network connectivity, spike timing and
synaptic plasticity.









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