We investigate how geometric properties translate into functional properties in sparse networks of computing elements. Specifically, we determine how the eigenvalues of the interconnection graph (which in turn reflect connectivity properties) relate to the quantities, number of items stored, amount of error-correction, radius of attraction, and rate of convergence, in an associative memory model consisting of a sparse network of threshold elements or neurons.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Networks and Communications
- Computational Theory and Mathematics
- Applied Mathematics