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Understanding the computation of time using neural network models
Zedong Bi,
Changsong Zhou
*
*
Corresponding author for this work
Department of Physics
Institute of Computational and Theoretical Studies
Centre for Nonlinear Studies
Research output
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Contribution to journal
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Journal article
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peer-review
44
Citations (Scopus)
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Keyphrases
Neural Network Model
100%
Non-temporal
100%
Working Memory
50%
Temporal Signal
50%
State Evolution
50%
Neural Network
25%
Spatial Data
25%
Firing Rate
25%
Network Structure
25%
Changing World
25%
Four Factors
25%
Temporal Structure
25%
Neural Mechanisms
25%
Evolutionary Rate
25%
Monotonic Increase
25%
Time of Use
25%
State Trajectory
25%
Temporal Processing
25%
Supervised Training
25%
Spatial Decision
25%
Recurrent Neural Network Model
25%
Right Time
25%
Computational Principles
25%
Elapsed Time
25%
Computer Science
Temporal Information
100%
Neural Network Model
100%
Decision-Making
50%
Neural Network
50%
Recurrent Neural Network
50%
Spatial Information
50%
Network Structure
50%
Orthogonal Subspace
50%
State Trajectory
50%
Neural Mechanism
50%
Generalizability
50%
Psychology
Neural Network
100%
Network Model
100%
Working Memory
66%
Decision Making
33%
Firing Rate
33%
Supervised Training
33%
Neuroscience
Neural Network
100%
Working Memory
100%
Firing Rate
50%
Decision-Making
50%
Recurrent Neural Network
50%