Neural Modeling of an Internal Clock
Neural Computation
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Generative Modeling of Autonomous Robots and their Environments using Reservoir Computing
Neural Processing Letters
Robust reservoir generation by correlation-based learning
Advances in Artificial Neural Systems
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
A neural model for the adaptive control of saccadic eye movements
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Learning inverse kinematics for pose-constraint bi-manual movements
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
Towards spatio-temporal pattern recognition using evolving spiking neural networks
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - 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
Survey: Reservoir computing approaches to recurrent neural network training
Computer Science Review
A model for complex sequence learning and reproduction in neural populations
Journal of Computational Neuroscience
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We examined closely the cerebellar circuit model that we have proposed previously. The model granular layer generates a finite but very long sequence of active neuron populations without recurrence, which is able to represent the passage of time. For all the possible binary patterns fed into mossy fibres, the circuit generates the same number of different sequences of active neuron populations. Model Purkinje cells that receive parallel fiber inputs from neurons in the granular layer learn to stop eliciting spikes at the timing instructed by the arrival of signals from the inferior olive. These functional roles of the granular layer and Purkinje cells are regarded as a liquid state generator and readout neurons, respectively. Thus, the cerebellum that has been considered to date as a biological counterpart of a perceptron is reinterpreted to be a liquid state machine that possesses powerful information processing capability more than a perceptron.