Development of Neural Network Architectures for Self-Organizing Pattern Recognition and Robotics
Abstract
During the second year of the DARPA ANNT Program contact, new neural network architectures were developed to carry out autonomous real-time preprocessing, segmentation, recognition, timing, and control of both spatial and temporal inputs. These architectures contribute to: (1) preprocessing of visual form and motion signals; (2) preprocessing of acoustic signals; (3) adaptive pattern recognition and categorization in an unsupervised learning context; (4) adaptive pattern recognition and prediction in a supervised learning context; (5) processing of temporal patterns using working memory networks, with applications to 3-D object recognition; (6) adaptive timing for task scheduling; (7) adaptive sensory-motor control using head-centered spatial representations of 3-D target position.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 01, 1992
- Accession Number
- ADA255433
Entities
People
- Gail A. Carpenter
- Stephen Grossberg
Organizations
- Boston University