Models of neural networks
Analog and digital processing in single nerve cells: dendritic integration and axonal propagation
Single neuron computation
Reading neuronal synchrony with depressing synapses
Neural Computation
Synaptic delay learning in pulse-coupled neurons
Neural Computation
Evolution of time coding systems
Neural Computation
Temporally asymmetric Hebbian learning, spike timing and neuronal response variability
Proceedings of the 1998 conference on Advances in neural information processing systems II
Spike-based compared to rate-based Hebbian learning
Proceedings of the 1998 conference on Advances in neural information processing systems II
Plasticity of Neocortical Synapses Enables Transitions between Rate and Temporal Coding
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Hebbian Delay Adaptation in a Network of Integrate-and-Fire Neurons
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
A spiking neuron model: applications and learning
Neural Networks
Temporal binding as an inducer for connectionist recruitment learning over delayed lines
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Polychronization: Computation with Spikes
Neural Computation
What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?
Neural Computation
Delay learning and polychronization for reservoir computing
Neurocomputing
Motion Detection Using Spiking Neural Network Model
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Frequency selectivity emerging from spike-timing-dependent plasticity
Neural Computation
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Systematic temporal relations between single neuronal activities or population activities are ubiquitous in the brain. No experimental evidence, however, exists for a direct modification of neuronal delays during Hebbian-type stimulation protocols. We show that in fact an explicit delay adaptation is not needed if one assumes that the synaptic strengths are modified according to the recently observed temporally asymmetric learning rule with the downregulating branch dominating the upregulating branch. During development, slow, unbiased fluctuations in the transmission time, together with temporally correlated network activity, may control neural growth and implicitly induce drifts in the axonal delays and dendritic latencies. These delays and latencies become optimally tuned in the sense that the synaptic response tends to peak in the soma of the postsynaptic cell if this is most likely to fire. The nature of the selection process requires unreliable synapses in order to give successful synapses an evolutionary advantage over the others. The width of the learning function also determines the preferred dendritic delay and the preferred width of the postsynaptic response. Hence, it may implicitly determine whether a synaptic connection provides a precisely timed or a broadly tuned "contextual" signal.