Cellular-Neural Associative Memory: limiting capability.
- List of the participants:
- Summary:
- The VLSI implementation of Artificial Neural Networks is limited by
the necessity of implement the full-graph interneuron communication. The
limitation may be removed if the interconnection structure is reduced to
a cellular structure with a certain size of neuron neighborhood. Up till
now there is no answer what are the directions of searching the ways to
compensate the decrease of interneuron communication so, that the space-time
characteristics of the algorithms remain acceptable.
The aim of the project is to develop a formal model of cellular-neural
algorithm, which is the combination of cellular automaton connection structure
with neuron networks connectionist character of computation. Based on such
a model some investigations of cellular-neural associative memory of Hopfield
type are to be performed. The goal of the investigations is to determine
limiting storing capacity and restoring capability in relation to the size
of the neuron neighborhood and the properties of stored patterns.
- Following investigations are intended to be done:
- To perform the comparative analysis of existing methods of Hopfield
Associative memory learning from the point of view of the compatibility
to cellular nature of interconnections, and chose the most suitable principle
for cellular case adaptation.
- To modify the chosen method for the cellular case, prove its correctness
and test it by simulating.
- To study the properties of stability and degree of attraction of stored
patterns both theoretically (obtaining necessary and sufficient conditions
of individual and asymptotical stability) and experimentally by simulation.
- To develop a method for cellular neural algorithm synthesys at given
technical and qualitative restrictions.
- List of publications:
- O.Bandman Cellular-Neural Computations.
Formal Model and Possible Applications // Lecture Notes in Computer
Science, 964, 1995, p.21-35.
- O.Bandman. Cellular-Neural Computation. Formal Model and Possible
Applications // Bulletin of the Novosibirsk Computing Center, series:
Computer Science, issue 2 (1994). - P.25-44.
- O.Bandman. Cellular-Neural Algorithms (methods of representation
and computer simulation) // Proceedings of the Conference "New
information technologies in discrete structures study". Ekaterinburg,
1996.- P.162-168.
- O.L.Bandman, S.G.Pudov. Stability
of stored patterns in cellular-neural associative memory.// Bulletin
of the Novosibirsk Computer Center. Series: Computer Science, issue 4 (1996),
pp.1-16.
- Olga Bandman, Sergey Pudov. Design and Simulations of Cellular Neural-like
Associative Memory. // IEEE Proceedings of First International Workshop
on Distributed Interactive Simulation and Real Time Applications (DIS-RTA'97),
January 9-10 Eilat, Israel, pp.49-56.
- S.G.Pudov. Learning of Cellular-Neural
Associative Memory. // Avtometriya, N2, 1997, pp.107-120.
- S.G.Pudov. Influence of Self-Connection Weights on Cellular-Neural
Network Stability. // Lecture Notes in Computer Science, 1277, pp.
76-82.
Please contact Dr. Olga Bandman for all the questions
concerning this project.
E-mail: bandman@ssd.sscc.ru
Last update: October 22, 1999