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TNN
1998
77views more  TNN 1998»
13 years 4 months ago
Self-organization of spiking neurons using action potential timing
Berthold Ruf, Michael Schmitt
NECO
2006
103views more  NECO 2006»
13 years 4 months ago
Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning
In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimi...
Jean-Pascal Pfister, Taro Toyoizumi, David Barber,...
NIPS
2004
13 years 6 months ago
Reducing Spike Train Variability: A Computational Theory Of Spike-Timing Dependent Plasticity
Experimental studies have observed synaptic potentiation when a presynaptic neuron fires shortly before a postsynaptic neuron, and synaptic depression when the presynaptic neuron ...
Sander M. Bohte, Michael C. Mozer
BSN
2011
IEEE
233views Sensor Networks» more  BSN 2011»
12 years 4 months ago
Compressive Sensing of Neural Action Potentials Using a Learned Union of Supports
—Wireless neural recording systems are subject to stringent power consumption constraints to support long-term recordings and to allow for implantation inside the brain. In this ...
Zainul Charbiwala, Vaibhav Karkare, Sarah Gibson, ...
IJON
2002
130views more  IJON 2002»
13 years 4 months ago
Error-backpropagation in temporally encoded networks of spiking neurons
For a network of spiking neurons that encodes information in the timing of individual spike times, we derive a supervised learning rule, SpikeProp, akin to traditional errorbackpr...
Sander M. Bohte, Joost N. Kok, Johannes A. La Pout...