PhD Thesis Available

[erikf@sans.kth.se]

My PhD thesis is available at my home page:
  http://www.nada.kth.se/~erikf/publications.html

It is also available for anonymous ftp downloading:
  ftp://ftp.nada.kth.se/pub/documents/SANS/reports/ps/ef-thesis.tar.Z
  ftp://ftp.nada.kth.se/pub/documents/SANS/reports/ps/ef-thesis-summary.ps.Z

The complete thesis is 2.4Mb and un-tars into 15.5Mb postscript files.
The summary is 530kb and prints on 68 pages.


	Biophysical Simulation of Cortical Associative Memory

			   Erik Fransen 

		Studies of Artificial Neural Systems
	Department of Numerical Analysis and Computing Science
      Royal Institute of Technology, S-100 44 Stockholm, Sweden
			 erikf at sans.kth.se


The associative memory function of the brain is an active area of
experimental and theoretical research. This thesis describes the
construction of a model of cortical auto-associative
memory. Conceptually, it is based on Hebb's cell assembly
hypothesis. The quantitative description comes from a class of
artificial neural networks, ANN, with recurrent connectivity and
attractor dynamics. More specifically, this work has concentrated on
problems related to how this formal network description could be
translated into a neurobiological model. In this work I have used a
relatively detailed description of the neurons which includes changes
over time for the potential and current distributions of the different
parts of the cell, as well as calcium ion flux and some of its
electrophysiological effects.
  The features of this associative memory model are interpreted in
Gestalt psychological terms and discussed in relation to features of
priming, as gained from memory psychological experiments. The model
output is compared to single cell recordings in working memory
experiments as well as to results from a slice preparation of the
hippocampus region.  A hypothesis for the functional role of the
variable resting potentials and background activities that are seen in
experiments has been put forward. This hypothesis is based on the bias
values which are produced by the learning in an ANN and result in
different `a priori firing probabilities of the cells. It is
also shown that it is possible to increase the degree of similarity to
the cortical circuitry with the cortical column model. This model can
function as a content-addressable memory, as expected.
  Initially, the network structure and the cell types have to be
determined.  The next part of the work is the identification of what
cell properties should be modeled. The initial results include a
demonstration that cells described at this detail can support the
assembly operations (persistent after-activity, pattern completion and
pattern rivalry) shown for ANNs. The importance of adequate cell
properties for network function was confirmed. For
example, with pyramidal type cells the network produced the desired
assembly operations, but with motoneuron type cells it did not.
  There are also results which are not dependent on the assembly
hypothesis. The network can stabilize in a relatively short time and
at sub-maximal cell firing frequencies despite time delays and the
recurrent connectivity which provides positive feed-back. Further, the
network activity may be controlled by modeling the effects of
neuromodulators such as serotonin. Instances of spike synchronization and
burst synchronization were found in networks that did not have any
inhibitory cells.
  It is concluded that this type of attractor network model can be used
as a valuable tool in the study of cortical associative memory, and
that detailed cell models are very useful for testing the biological
relevance of such models.

Keywords: 
after--activity, attractor network, biologically realistic neural
networks, computational neuroscience, computer simulation, cortical
associative memory, Hebbian cell assemblies, neural modeling,
recurrent artificial neural network, pattern completion, pattern
rivalry

Fransen E. Thesis, 1996
Biophysical Simulation of Cortical Associative Memory.
Dept. of Numerical Analysis and Computing Science,
Royal Institute of Technology, Stockholm, Sweden,
ISBN 91-7170-689-5, TRITA-NA-P96/28



     __---~~~--___     |----------------------------------|     _____________  
  _-~             )----+ Erik Fransen                     +----| Studies of |\ 
 (                 )---+ Department of Numerical Analysis +----| Artificial | |
(     ___--         )--+ and Computing Science            +----| Neural     | |
(___-~           __)   | Royal Institute of Technology    |    | Systems    | |
   (____    _--~~ )    | S-100 44 Stockholm, Sweden       |    |____________|_|
        `~~\ ~--~~     | EMail: erikf at sans.kth.se         |    _____|___|_____ 
            \--\       | http://www.nada.kth.se/~erikf    |   /_+46-8-7906904/ 
                       |----------------------------------|