The gamma model-A new neural model for temporal processing

Neural Networks

Volumn 5, Issue 4, 1992, Pages 565-576

  Vries, Bert de ^a   Principe, Jose C ^a  

^a UNIVERSITY OF FLORIDA   (United States)

Author keywords

ARMA model; Dynamic neural nets; Focused back propagation; Gamma model; Short term memory; Temporal processing

Indexed keywords

DIFFERENTIAL EQUATIONS; LEARNING SYSTEMS; SIGNAL PROCESSING;

ARMA MODEL; DYNAMIC NEURAL NETS; FOCUSED BACK PROPAGATION; GAMMA MODEL; SHORT TERM MEMORY; TEMPORAL PROCESSING;

NEURAL NETWORKS;

EID: 0026895542     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0893-6080(05)80035-8     Document Type: Article

References (27)

1. Learning the architecture of neural networks for speech recognition
2. A theory for neural nets with time delays
3. Finding structure in time
4. Reductibilite des systemes hereditaires a des systemes dynamiques (regis par des equations differentielles ou aux derivees partielles)
5. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition by shift in position
6. BPS: A learning algorithm for capturing the dynamic nature of speech
7. Atractor dynamics and parallelism in a connectionist sequential machine
8. A time-delay neural network architecture for isolated word recognition
9. Representation of brief temporal patterns, Hebbian synapses, and the left-hemisphere dominance for phoneme recognition
10. A focused back propagation algorithm for temporal pattern recognition
11. Neural network learning time: Effects of network and traning size
12. Learning internal representations by error back-propagation
13. Skeleton filters in the brain
14. Neural integration by short term potentiation
15. A dynamical approach to temporal pattern processing
16. Concentrating information in time: Analog neural networks with applications to speech recognition problems
17. A dynamical systems approach to speech processing
18. Connected-digit speaker-dependent speech recognition using a neural network with time-delayed connections
19. Back propagation through time: What it does and how to do it
20. A learning algorithm for continually running fully connected networks
21. Adaptive state representation and estimation using recurrent connectionist networks