Neural Computation
Neural networks: a systematic introduction
Neural networks: a systematic introduction
Forward models for physiological motor control
Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
Pulsed neural networks
Spikes: exploring the neural code
Spikes: exploring the neural code
What are the computations of the cerebellum, the basal ganglia and the cerebral cortex?
Neural Networks - Special issue on organisation of computation in brain-like systems
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?
Neural Computation
Improved spiking neural networks for EEG classification and epilepsy and seizure detection
Integrated Computer-Aided Engineering
A gradient descent rule for spiking neurons emitting multiple spikes
Information Processing Letters - Special issue on applications of spiking neural networks
Analysis of the ReSuMe Learning Process For Spiking Neural Networks
International Journal of Applied Mathematics and Computer Science - Special Section: Selected Topics in Biological Cybernetics, Special Editors: Andrzej Kasiński and Filip Ponulak
Learning beyond finite memory in recurrent networks of spiking neurons
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Simple model of spiking neurons
IEEE Transactions on Neural Networks
SPAN: a neuron for precise-time spike pattern association
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
Neural Processing Letters
Classification of distorted patterns by feed-forward spiking neural networks
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
NeuCube evospike architecture for spatio-temporal modelling and pattern recognition of brain signals
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
A spike-timing-based integrated model for pattern recognition
Neural Computation
Supervised learning in multilayer spiking neural networks
Neural Computation
A new supervised learning algorithm for spiking neurons
Neural Computation
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Learning from instructions or demonstrations is a fundamental property of our brain necessary to acquire new knowledge and develop novel skills or behavioral patterns. This type of learning is thought to be involved in most of our daily routines. Although the concept of instruction-based learning has been studied for several decades, the exact neural mechanisms implementing this process remain unrevealed. One of the central questions in this regard is, How do neurons learn to reproduce template signals (instructions) encoded in precisely timed sequences of spikes? Here we present a model of supervised learning for biologically plausible neurons that addresses this question. In a set of experiments, we demonstrate that our approach enables us to train spiking neurons to reproduce arbitrary template spike patterns in response to given synaptic stimuli even in the presence of various sources of noise. We show that the learning rule can also be used for decision-making tasks. Neurons can be trained to classify categories of input signals based on only a temporal configuration of spikes. The decision is communicated by emitting precisely timed spike trains associated with given input categories. Trained neurons can perform the classification task correctly even if stimuli and corresponding decision times are temporally separated and the relevant information is consequently highly overlapped by the ongoing neural activity. Finally, we demonstrate that neurons can be trained to reproduce sequences of spikes with a controllable time shift with respect to target templates. A reproduced signal can follow or even precede the targets. This surprising result points out that spiking neurons can potentially be applied to forecast the behavior (firing times) of other reference neurons or networks.