Model neural networks

Miscellaneous · Sun Jan 01 00:00:00 EST 1989

OSTI ID:6093300

Kepler, T B

After a brief introduction to the techniques and philosophy of neural network modeling by spin glass inspired system, the author investigates several properties of these discrete models for autoassociative memory. Memories are represented as patterns of neural activity; their traces are stored in a distributed manner in the matrix of synaptic coupling strengths. Recall is dynamic, an initial state containing partial information about one of the memories evolves toward that memory. Activity in each neuron creates fields at every other neuron, the sum total of which determines its activity. By averaging over the space of interaction matrices with memory constraints enforced by the choice of measure, we show that the exist universality classes defined by families of field distributions and the associated network capacities. He demonstrates the dominant role played by the field distribution in determining the size of the domains of attraction and present, in two independent ways, an expression for this size. He presents a class of convergent learning algorithms which improve upon known algorithms for producing such interaction matrices. He demonstrates that spurious states, or unexperienced memories, may be practically suppressed by the inducement of n-cycles and chaos. He investigates aspects of chaos in these systems, and then leave discrete modeling to implement the analysis of chaotic behavior on a continuous valued network realized in electronic hardware. In each section he combine analytical calculation and computer simulations.

• Research Organization: Brandeis Univ., Waltham, MA (USA)
• OSTI ID: 6093300
• Resource Relation: Other Information: Thesis (Ph. D.)
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
NEURAL NETWORKS
MATHEMATICAL MODELS
ALGORITHMS
BIOELECTRICITY
BRAIN
COMPUTERIZED SIMULATION
DISTRIBUTED DATA PROCESSING
LEARNING
NERVE CELLS
ANIMAL CELLS
BODY
CENTRAL NERVOUS SYSTEM
DATA PROCESSING
ELECTRICITY
MATHEMATICAL LOGIC
NERVOUS SYSTEM
ORGANS
PROCESSING
SIMULATION
SOMATIC CELLS
990200* - Mathematics & Computers