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NECO
2007
108views more  NECO 2007»
13 years 4 months ago
Spike-Frequency Adapting Neural Ensembles: Beyond Mean Adaptation and Renewal Theories
We propose a Markov process model for spike-frequency adapting neural ensembles which synthesizes existing mean-adaptation approaches, population density methods, and inhomogeneou...
Eilif Mueller, Lars Buesing, Johannes Schemmel, Ka...
DELTA
2008
IEEE
13 years 7 months ago
A Spiking Neural Network for Gas Discrimination Using a Tin Oxide Sensor Array
We propose a bio-inspired signal processing method for odor discrimination. A spiking neural network is trained with a supervised learning rule so as to classify the analog outputs...
Maxime Ambard, Bin Guo, Dominique Martinez, Amine ...
NIPS
2004
13 years 6 months ago
Rate- and Phase-coded Autoassociative Memory
Areas of the brain involved in various forms of memory exhibit patterns of neural activity quite unlike those in canonical computational models. We show how to use well-founded Ba...
Máté Lengyel, Peter Dayan
NIPS
2004
13 years 6 months ago
A Temporal Kernel-Based Model for Tracking Hand Movements from Neural Activities
We devise and experiment with a dynamical kernel-based system for tracking hand movements from neural activity. The state of the system corresponds to the hand location, velocity,...
Lavi Shpigelman, Koby Crammer, Rony Paz, Eilon Vaa...
BC
2006
105views more  BC 2006»
13 years 5 months ago
A stochastic population approach to the problem of stable recruitment hierarchies in spiking neural networks
Recruitment learning in hierarchies is an inherently unstable process (Valiant, 1994). This paper presents conditions on parameters for a feedforward network to ensure stable recru...
Cengiz Günay, Anthony S. Maida