Neural modeling papers

[Eytan Ruppin]

 Hi,

1. A few recent neural modeling papers are now available on my homepage,  
   http://www.math.tau.ac.il/~ruppin/. Their abstracts are enclosed below.

2. Abstracts of the talks to be given in the TAU workshop on 
  `Memory organization and consolidation: cognitive and computational 
   perspectives' (Tel-Aviv, 28 - 30'th of May), will be available after
   the workshop via http://www.brain.tau.ac.il and via my homepage.
   Both homepages currently include the workshop program. 

 Best wishes,

  Eytan Ruppin.


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                         Abstracts:
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1.  Neuronal-Based Synaptic Compensation: 
 A Computational Study in Alzheimer's Disease       
 ---------------------------------------------

 David Horn, Nir Levy and Eytan Ruppin
 (to appear in Neural Computation 1996)

In the framework of an associative memory model,
we study the interplay between synaptic deletion
and compensation, and memory deterioration,
a clinical hallmark of Alzheimer's disease.
Our study is motivated by  experimental evidence that there are regulatory
mechanisms that take part in the homeostasis of neuronal activity and act
on the {\em neuronal} level.
We show that, following synaptic deletion, synaptic compensation can be
carried out efficiently by a {\em local, dynamic} mechanism, where
each neuron maintains the profile of its incoming post-synaptic current.
Our results open up the possibility that the primary factor in the
pathogenesis of cognitive deficiencies in Alzheimer's disease is the
failure of local neuronal regulatory mechanisms.
Allowing for neuronal death, we observe two pathological routes in AD, leading
to different correlations between the levels of structural damage and
functional decline.                   



 2.  Optimal Firing in Sparsely-connected Low-activity Attractor Networks 
 -------------------------------------------------------------------------- 

       Isaac Meilijson and Eytan Ruppin 
       (to appear in Biological Cybernetics 1996)

 We examine the performance of Hebbian-like attractor neural networks,
recalling stored memory patterns from their distorted versions.
Searching for an activation (firing-rate) function that
maximizes the performance in sparsely-connected
low-activity networks, we show that the optimal activation function is
a {\em Threshold-Sigmoid} of the neuron's input field. This function
is shown to be in close correspondence with the dependence of
the firing rate of cortical neurons on their integrated input current,
as described by neurophysiological recordings and conduction-based models.
It also accounts for the decreasing-density shape of firing
rates that has been reported in the literature.                

 3.  Pathogenesis of Schizophrenic Delusions and Hallucinations: A Neural Model 
  ---------------------------------------------------------------------------

     Eytan Ruppin, James Reggia and David Horn
    ({\em Schizophrenia Bulletin}, 22(1), 105-123, 1996 )

  We implement and study a computational model of Stevens' theory
of the pathogenesis of schizophrenia [1992]. This theory hypothesizes
that the onset of schizophrenia is associated with reactive
synaptic regeneration occurring in brain regions receiving degenerating
temporal lobe projections. Concentrating on one such area, the frontal
cortex, we model a frontal module as an associative memory neural network whose
input synapses represent incoming temporal projections. Modeling Stevens'
hypothesized pathological synaptic changes in this framework results in
adverse side effects reminiscent of
hallucinations and delusions seen in schizophrenia:
spontaneous, stimulus-independent retrieval of stored memories focused on
just a few of the stored patterns. These could account
for the occurrence of schizophrenic delusions and
hallucinations without any apparent
external trigger, and for their tendency to concentrate on a few
central cognitive and perceptual themes. The model explains why
schizophrenic positive symptoms tend to wane as the disease progresses,
why delayed therapeutical intervention leads to a much slower response,
and why delusions and hallucinations may persist for a long duration
when they occur.
                                          
 4. Synaptic Runaway in Associative Networks  
 -------------------------------------------
 (Submitted to NIPS*96)

  Asnat Greenstein-Messica and  Eytan Ruppin 

 Synaptic runaway, the formation of erroneous synapses in the process of
learning new patterns, is studied both analytically and numerically
in binary associative neural networks. It is found that under normal
biological conditions synaptic runaway in these networks
is of fairly moderate magnitude, and is thus different from the
extensive synaptic runaway found previously in analog-firing associative
networks.
However, synaptic runaway may become extensive if the threshold for Hebbian
learning is reduced. The implications of these findings
to the possible role of N-methyl-D-aspartate (NMDA) alterations in the
pathogenesis of schizophrenia are discussed.     



 5.  Neuronal Homeostasis and the Art of Synaptic Maintenance         
 -------------------------------------------------------------
  
   David Horn, Nir Levy and Eytan Ruppin
  (Submitted to NIPS*96)

   We propose a novel mechanism of synaptic maintenance whose goal is
to preserve the performance of an associative memory network
undergoing synaptic degradation, and to prevent the development of
pathologic attractors.  This mechanism is demonstrated by
simulations performed in a low-activity neural model that
implements local neuronal homeostasis.
 It works well even in a network undergoing strongly
inhomogeneous synaptic alterations, and when input patterns are
consecutively stored in the network. Our synaptic
maintenance method strongly supports the idea that memory
consolidation and synaptic maintenance should occur in separate
periods of time, in a repetitive manner. Consequently, we hypothesize
that synaptic maintenance occurs during REM sleep, while
memory consolidation occurs during slow wave sleep.



  6. Neural modeling of psychiatric disorders (A review paper)
 -------------------------------------------------------------

      Eytan Ruppin
    ({\em Network}, 6, 635-656, 1995)

   This paper reviews recent neural modeling studies
of psychiatric disorders. Numerous aspects of
psychiatric disturbances have been investigated, such as
the role of synaptic changes in the pathogenesis
of Alzheimer's disease, the study of spurious attractors as possible
neural correlates of schizophrenic positive symptoms, and the
exploration of the ability of feed-forward and recurrent networks to
quantitatively model the cognitive performance of schizophrenic patients.
Current models all employ considerable simplifications, both on the level of
the behavioral phenomenology they seek to explore, and on the level of
their structure and dynamics.
However, it is encouraging to realize that the disruption of
just a few simple computational mechanisms can lead to
behaviors which correspond to some of the clinical features
of psychiatric disorders, and can shed light on their pathogenesis.

 
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