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» Polychronization: Computation with Spikes
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FUIN
2006
105views more  FUIN 2006»
13 years 7 months ago
Spiking Neural P Systems
This paper is an attempt to incorporate the idea of spiking neurons into the area of membrane computing, and to this aim we introduce a class of neural-like P systems which we cal...
Mihai Ionescu, Gheorghe Paun, Takashi Yokomori
NIPS
2003
13 years 9 months ago
Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model
Recent work has examined the estimation of models of stimulus-driven neural activity in which some linear filtering process is followed by a nonlinear, probabilistic spiking stag...
Jonathan Pillow, Liam Paninski, Eero P. Simoncelli
CICC
2011
106views more  CICC 2011»
12 years 7 months ago
A 45nm CMOS neuromorphic chip with a scalable architecture for learning in networks of spiking neurons
Efforts to achieve the long-standing dream of realizing scalable learning algorithms for networks of spiking neurons in silicon have been hampered by (a) the limited scalability of...
Jae-sun Seo, Bernard Brezzo, Yong Liu, Benjamin D....
NECO
2007
112views more  NECO 2007»
13 years 7 months ago
Distortion of Neural Signals by Spike Coding
Analog neural signals must be converted into spike trains for transmission over electrically leaky axons. This spike encoding and subsequent decoding leads to distortion. We quant...
David H. Goldberg, Andreas G. Andreou
IJCNN
2008
IEEE
14 years 2 months ago
Biologically realizable reward-modulated hebbian training for spiking neural networks
— Spiking neural networks have been shown capable of simulating sigmoidal artificial neural networks providing promising evidence that they too are universal function approximat...
Silvia Ferrari, Bhavesh Mehta, Gianluca Di Muro, A...