Adaptively timed conditioned responses and the cerebellum: a neural network approach
Biological Cybernetics
1994 Special Issue: A neural model of timed response learning in the cerebellum
Neural Networks - Special issue: models of neurodynamics and behavior
Reduction of conductance-based models with slow synapses to neural nets
Neural Computation
2007 Special Issue: The cerebellum as a liquid state machine
Neural Networks
Robust reservoir generation by correlation-based learning
Advances in Artificial Neural Systems
Building the cerebellum in a computer
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
A possible mechanism for controlling timing representation in the cerebellar cortex
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
The neural representation of time: An information-theoretic perspective
Neural Computation
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We studied a simple random recurrent inhibitory network. Despite its simplicity, the dynamics was so rich that activity patterns of neurons evolved with time without recurrence due to random recurrent connections among neurons. The sequence of activity patterns was generated by the trigger of an external signal, and the generation was stable against noise. Moreover, the same sequence was reproducible using a strong transient signal, that is, the sequence generation could be reset. Therefore, a time passage from the trigger of an external signal could be represented by the sequence of activity patterns, suggesting that this model could work as an internal clock. The model could generate different sequences of activity patterns by providing different external signals; thus, spatiotemporal information could be represented by this model. Moreover, it was possible to speed up and slow down the sequence generation.