Back in April, I posted the abstract below of a paper I sub-
mitted to Physical Review A, stating that I'd be glad to e-mail a
uuencoded compressed PostScript draft to interested people. The
paper is scheduled to appear in the 15 September issue of that
journal.
In the context of mathematical models of memory being
queried currently in this forum, this paper, in part, deals with
statistical constraints imposed on short-term memory by the phy-
sics of interactions at the underlying scale of neuronal
activity.
========================================
Statistical mechanics of neocortical interactions:
A scaling paradigm applied to electroencephalography
A series of papers has developed a statistical mechanics of
neocortical interactions (SMNI), deriving aggregate behavior of
experimentally observed columns of neurons from statistical
electrical-chemical properties of synaptic interactions. While
not useful to yield insights at the single neuron level, SMNI has
demonstrated its capability in describing large-scale properties
of short-term memory and electroencephalographic (EEG) systemat-
ics. The necessity of including nonlinear and stochastic struc-
tures in this development has been stressed. In this paper, a
more stringent test is placed on SMNI: The algebraic and numeri-
cal algorithms previously developed in this and similar systems
are brought to bear to fit large sets of EEG/evoked potential
data being collected to investigate genetic predispositions to
alcoholism and to extract brain "signatures" of short-term
memory. Using the numerical algorithm of Very Fast Simulated
Re-Annealing, it is demonstrated that SMNI can indeed fit this
data within experimentally observed ranges of its underlying
neuronal-synaptic parameters, and use the quantitative modeling
results to examine physical neocortical mechanisms to discrim-
inate between high-risk and low-risk populations genetically
predisposed to alcoholism. Since this first study is a control
to span relatively long time epochs, similar to earlier attempts
to establish such correlations, this discrimination is incon-
clusive because of other neuronal activity which can mask such
effects. However, the SMNI model is shown to be consistent with
EEG data during selective attention tasks and with neocortical
mechanisms describing short-term memory (STM) previously pub-
lished using this approach. This paper explicitly identifies
similar nonlinear stochastic mechanisms of interaction at the
microscopic-neuronal, mesoscopic-columnar and macroscopic-
regional scales of neocortical interactions. These results give
strong quantitative support for an accurate intuitive picture,
portraying neocortical interactions as having common
algebraic/physics mechanisms that scale across quite disparate
spatial scales and functional/behavioral phenomena, i.e.,
describing interactions among neurons, columns of neurons, and
regional masses of neurons.
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Domain: curtiss at umiacs.umd.edu Phillip Curtiss
UUCP: uunet!mimsy!curtiss UMIACS - Univ. of Maryland
Phone: +1-301-405-6710 College Park, Md 20742
