TY - GEN
T1 - Neural networks that learn state space trajectories by 'Hebbian' rule
AU - Zhang, Kechen
PY - 1992
Y1 - 1992
N2 - Summary form only given, as follows. A neural network structure has been proposed which can learn state space trajectories (sequential state transitions) by a Hebbian-like rule, but without resorting to time-delayed synaptic connections. The main idea is to use two Hopfield networks, each of which stabilizes its own memories while it drives the other network into state transition. The dynamics of the network was considered. As an emergent property, the state transitions of all individual neurons are synchronous. The learning rate of the network was estimated.
AB - Summary form only given, as follows. A neural network structure has been proposed which can learn state space trajectories (sequential state transitions) by a Hebbian-like rule, but without resorting to time-delayed synaptic connections. The main idea is to use two Hopfield networks, each of which stabilizes its own memories while it drives the other network into state transition. The dynamics of the network was considered. As an emergent property, the state transitions of all individual neurons are synchronous. The learning rate of the network was estimated.
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M3 - Conference contribution
AN - SCOPUS:0026745693
SN - 0780301641
T3 - Proceedings. IJCNN - International Joint Conference on Neural Networks
SP - 976
BT - Proceedings. IJCNN - International Joint Conference on Neural Networks
A2 - Anon, null
PB - Publ by IEEE
T2 - International Joint Conference on Neural Networks - IJCNN-91-Seattle
Y2 - 8 July 1991 through 12 July 1991
ER -