Storage of 7+/-2 Short-Term Memories in oscillatory subcycles

Lester Ingber ingber at alumni.caltech.edu
Wed Apr 26 10:27:38 EST 1995


In article <3nln1a$38m9 at ns1.cc.lehigh.edu>,  <x011 at Lehigh.EDU> wrote:
:In Science 10 March 1995: Lisman and Idiart modeled short term memory
:with oscillatory subcycles.  The model proved to be reliable if there
:were an inhibitory phase to vector firing oscillations in the
:next subcycle.  The NN supports Hebbian reverberation model.
:Ron Blue x011 at lehigh.edu

This article indeed presents interesting information, but their
conclusions are not entirely warranted by their investigations,
as I point out in smni95_40hz.ps.Z
        %A L. Ingber
        %T Statistical mechanics of neocortical interactions:
           Constraints on 40 Hz models of short-term memory
        %J Phys. Rev. E
        %V
        %N
        %D 1995
        %P (submitted)
in my archive.  The file can be retrieved as follows:
Interactively [brackets signify machine prompts]:
   [your_machine%] ftp ftp.alumni.caltech.edu
   [Name (...):] anonymous
   [Password:] your_e-mail_address
   [ftp>] cd pub/ingber
   [ftp>] binary
   [ftp>] ls
   [ftp>] get file_of_interest
   [ftp>] quit
The 00index file contains an index of the  other  files.
This  archive  also   can   be   accessed   via   WWW   path
http://alumni.caltech.edu/~ingber

In my conclusion I have
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
SMNI details the capability of minicolumnar activity to develop
multiple memories of capacity 7\(+-2 with duration on the order
of tenths of a second.  These memories are described as most likely
states of an evolving probability distribution of minicolumnar firings.
It also details explanation for related phenomena, e.g., the primacy
versus frequency of short-term memories and the often observed random
access phenomena of these memories.  SMNI does not detail any specific
synaptic or neuronal mechanisms that might take refresh these most
likely states to reinforce multiple short term memories.  However,
the calculated evolution of states is consistent with the observation
that an oscillatory subcycle of 40 Hz may be the bare minimal threshold
of self-sustaining minicolumnar firings before they begin to degrade.
 
The mechanism of ADP details a specific synaptic mechanism that,
when coupled with additional proposals of neuronal oscillatory cycles
of 5\-12 Hz and oscillatory subcycles of 40 Hz, can sustain these
memories for longer durations on the order of seconds.  By itself, ADP
does not provide a constraint such as the 7\(+-2 rule.  For example,
it is well known from many neural network studies that ensembles of
neurons can simultaneously support multiple memories, so that it is
unlikely that just serially connecting 40 Hz subcycles within larger
oscillatory cycles can provide such a constraint.  The SMNI calculation
supports a straightforward statistical argument for the 7\(+-2 rule,
and yields a duration for access to all memories within the the epoch
of the larger oscillatory cycles.
 
It also it difficult to see how invoking the ADP mechanism can explain
the primacy versus frequency rule, since this mechanism assumes that
newly acquired memories simply replace old ones, and it also assumes
that memories are scanned serially.
 
The SMNI and ADP models are complementary to the understanding of STM.
Research is in progress to implement various synaptic mechanics into
the SMNI framework.  For example, The SMNI approach has been proposed
to study minicolumnar interactions across macrocolumns and across
regions.  This could be approached with a mesoscopic neural network
using a confluence of techniques drawn from SMNI, modern methods of
functional stochastic calculus defining nonlinear Lagrangians, adaptive
simulated annealing (ASA), and parallel-processing computation, to
develop a generic nonlinear stochastic mesoscopic neural network (MNN).
Other developments of SMNI, utilizing coarser statistical scaling
than presented here, have been used to more directly interface with
EEG phenomena, including the spatial and temporal filtering observed
experimentally.  Now, MNN can be used to overlap the spatial scales
studied by SMNI with the finer spatial scales typically studied by
other relatively more microscopic neural networks.
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
-- 
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