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TNN
1998
77views more  TNN 1998»
14 years 10 months ago
Self-organization of spiking neurons using action potential timing
Berthold Ruf, Michael Schmitt
NECO
2006
103views more  NECO 2006»
14 years 10 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
15 years 2 days 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»
13 years 10 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»
14 years 10 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...