A learning algorithm to teach spatiotemporal patterns to recurrent neural networks

Biological Cybernetics

Volumn 62, Issue 3, 1990, Pages 259-263

  Sato, Masa aki ^a  

^a ADVANCED TELECOMMUNICATIONS RESEARCH INSTITUTE INTERNATIONAL   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; LEARNING; NERVE CELL NETWORK; THEORETICAL STUDY;

EID: 0025095180     PISSN: 03401200     EISSN: 14320770     Source Type: Journal    
DOI: 10.1007/BF00198101     Document Type: Article

References (13)

1. Absolute stability of global pattern formation and parallel memory storage by competitive neural networks
2. Learning internal representations by error propagation
3. Neural networks and physical systems with emergent collective computational abilities
4. Neurons with graded response have collective computational properties like those of two-state neurons
5. Energy learning in neural network model which reconstructs image from noisy data
6. Network model of shape-fromshading: neural function arises from both receptive and projective fields
7. Gain variation in recurrent error propagation networks
8. Learning state space trajectories in recurrent neural network
9. Generalization of back-propagation to recurrent neural networks
10. A learning algorithm for continually running fully recurrent neural networks