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» Expressive Models for Synaptic Plasticity
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NIPS
2004
13 years 8 months ago
Maximising Sensitivity in a Spiking Network
We use unsupervised probabilistic machine learning ideas to try to explain the kinds of learning observed in real neurons, the goal being to connect abstract principles of self-or...
Anthony J. Bell, Lucas C. Parra
NECO
2006
98views more  NECO 2006»
13 years 6 months ago
Comment on "Characterization of Subthreshold Voltage Fluctuations in Neuronal Membranes, " by M. Rudolph and A. Destexhe
In two recent papers, Rudolph and Destexhe (Neural Comp. 15, 2577-2618, 2003; Neural Comp. in press, 2005) studied a leaky integrator model (i.e. an RC-circuit) driven by correlat...
Benjamin Lindner, André Longtin
IJON
2006
62views more  IJON 2006»
13 years 6 months ago
Dependence of the spike-triggered average voltage on membrane response properties
The spike-triggered average voltage (STV) is an experimentally measurable quantity that is determined by both the membrane response properties and the statistics of the synaptic d...
Laurent Badel, Wulfram Gerstner, Magnus J. E. Rich...
GECCO
2004
Springer
113views Optimization» more  GECCO 2004»
14 years 3 days ago
Implications of Epigenetic Learning Via Modification of Histones on Performance of Genetic Programming
Extending the notion of inheritable genotype in genetic programming (GP) from the common model of DNA into chromatin (DNA and histones), we propose an approach of embedding in GP a...
Ivan Tanev, Kikuo Yuta
CAD
2008
Springer
13 years 6 months ago
Geometrically exact dynamic splines
In this paper, we propose a complete model handling the physical simulation of deformable 1D objects. We formulate continuous expressions for stretching, bending and twisting ener...
Adrien Theetten, Laurent Grisoni, Claude Andriot, ...