Paper Info
Title
Learning Precise Spike Train-to-Spike Train Transformations in Multilayer Feedforward Neuronal Networks.
Abstract
We derive a synaptic weight update rule for learning temporally precise spike train–to–spike train transformations in multilayer feedforward networks of spiking neurons. The framework, aimed at seamlessly generalizing error backpropagation to the deterministic spiking neuron setting, is based strictly on spike timing and avoids invoking concepts pertaining to spike rates or probabilistic models of...
Year
DOI
Venue
2016
10.1162/NECO_a_00829
Neural Computation
Field
DocType
Volume
Synapse,Gradient descent,Spike train,Computer science,Excitatory postsynaptic potential,Artificial intelligence,Probabilistic logic,Backpropagation,Synaptic weight,Machine learning,Feed forward
Journal
28
Issue
ISSN
Citations 
5
0899-7667
3
PageRank 
References 
Authors
0.42
8
1
Name
Order
Citations
PageRank
Arunava Banerjee131329.18