3 papers on Multi-modular associative N.Networks.

[Alfonso Renart]

Dear Connectionists:

The following 3 papers on are available at the website:

http://www.ft.uam.es/neurociencia/GRUPO/publications_group.html

They deal with the subject of autoassociative recurrent networks
in systems of several modules and their aplication to the study 
of working memory mechanisms in delay tasks.

Sincerely,

Alfonso Renart.

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Renart A., Parga N. and Rolls E. T.
Backprojections in the cerebral cortex: implications for memory
storage 
Neural Computation 11 (6): 1349-1388, 1999. 

Abstract:

Cortical areas are characterized by forward and backward connections
between adjacent cortical areas in a processing stream. Within each
area there are recurrent collateral connections between the pyramidal
cells. We analyze the properties of this architecture for memory
storage and processing. Hebb-like synaptic modifiability in the
connections, and attractor states, are incorporated. We show the
following: (1) The number of memories that can be stored in the
connected modules is of the same order of magnitude as the number that
can be stored in any one module using the recurrent collateral
connections, and is proportional to the number of effective connections
per neuron. (2) Cooperation between modules leads to a small increase
in the memory capacity.  (3) Cooperation can also help retrieval in a
module which is cued with a noisy or incomplete pattern. (4) If the
connection strength between modules is strong, then global memory
states which reflect the pairs of patterns on which the modules were
trained together are found. (5) If the intermodule connection
strengths are weaker, then separate, local, memory states can exist in
each module.  (6) The boundaries between the global and local
retrieval states, and the non-retrieval state, are delimited. All
these properties are analyzed quantitatively with the techniques of
statistical physics.     

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Renart A., Parga N. and Rolls E. T.
Associative memory properties of multiple cortical modules 
NETWORK 10: 237-255, 1999. 

Abstract:

The existence of recurrent collateral connections between pyramidal
cells within a cortical area and, in addition, reciprocal connections
between connected cortical areas, is well established.  In this work
we analyze the properties of a tri-modular architecture of this type
in which two input modules have convergent connections to a third
module (which in the brain might be the next module in cortical
processing or a bi-modal area receiving connections from two different
processing pathways). Memory retrieval is analyzed in this system
which has Hebb-like synaptic modifiability in the connections and
attractor states.  Local activity features are stored in the
intra-modular connections while the associations between corresponding
features in different modules present during training are stored in
the inter-modular connections.  The response of the network when tested
with corresponding and contradictory stimuli to the two input pathways
is studied in detail.
The model is solved quantitatively using techniques of statistical
physics. In one type of test, a sequence of stimuli was applied, with
a delay between them. It is found that if the coupling between the
modules is low a regime exists in which they retain the capability to
retrieve any of their stored features independently of the features
being retrieved by the other modules.  Although independent in this
sense, the modules still influence each other in this regime through
persistent modulatory currents which are strong enough to initiate
recall in the whole network when only a single module is stimulated,
and to raise the mean firing rates of the neurons in the attractors if
the features in the different modules are corresponding. Some of these         
mechanisms might be useful for the description of many phenomena
observed in single neuron activity recorded during short term memory
tasks such as delayed match-to-sample. It is also shown that with
contradictory stimulation of the two input modules the model accounts
for many of the phenomena observed in the McGurk effect, in which
contradictory auditory and visual inputs can lead to misperception.

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Renart A., Parga N. and E.T. Rolls         
A recurrent model of the interaction between PF and IT 
cortex in delay memory tasks 
Proceedings of: NEURAL INFORMATION PROCESSING SYSTEMS, 1999
(NIPS99) (Denver. Nov. 29 - Dec 4, 1999). 

Abstract:

A very simple model of two reciprocally connected attractor neural
networks is studied analytically in situations similar to those
encountered in delay match-to-sample tasks with intervening stimuli
and in tasks of memory guided attention. The model qualitatively
reproduces many of the experimental data on these types of tasks and
provides a framework for the understanding of the experimental
observations in the context of the attractor neural network scenario.