Alternative knowledge acquisition: Developing a pulse coded neural network
After a Rip-van-Winkle nap of more than 20 years, the ideas of biologically motivated computing are re-emerging. Instrumental to this awakening have been the highly publicized contributions of John Hopfield and major advances in the neurosciences. In 1982, Hopfield showed how a system of maximally coupled neutron-like elements described by a Hamiltonian formalism (a linear, conservative system) could behave in a manner startlingly suggestive of the way humans might go about solving problems and retrieving memories. Continuing advances in the neurosciences are providing a coherent basis in suggesting how nature's neurons might function. A particular model is described for an artificial neural system designed to interact with (learn from and manipulate) a simulated (or real) environment. The model is based on early work by Iben Browning. The Browning model, designed to investigate computer-based intelligence, contains a particular simplification based on observations of frequency coding of information in the brain and information flow from receptors to the brain and back to effectors. The ability to act on and react to the environment was seen as an important principle, leading to self-organization of the system.
- Research Organization:
- Oak Ridge National Lab., TN (USA)
- DOE Contract Number:
- AC05-84OR21400
- OSTI ID:
- 6742527
- Report Number(s):
- CONF-870150-1; ON: DE87005192
- Resource Relation:
- Conference: Long Island University computer technology symposium, Brookville, NY, USA, 23 Jan 1987; Other Information: Portions of this document are illegible in microfiche products
- Country of Publication:
- United States
- Language:
- English
Similar Records
Scaling neural simulations in STACS
High-density electrical and optical probes for neural readout and light focusing in deep brain tissue