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126
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NIPS
2004
15 years 3 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
133
Voted
UAI
2004
15 years 3 months ago
Dependent Dirichlet Priors and Optimal Linear Estimators for Belief Net Parameters
A Bayesian belief network is a model of a joint distribution over a finite set of variables, with a DAG structure representing immediate dependencies among the variables. For each...
Peter Hooper
WSCG
2004
143views more  WSCG 2004»
15 years 3 months ago
View Dependent Stochastic Sampling for Efficient Rendering of Point Sampled Surfaces
In this paper we present a new technique for rendering very large datasets representing point-sampled surfaces. Rendering efficiency is considerably improved by using stochastic s...
Sushil Bhakar, Liang Luo, Sudhir P. Mudur
93
Voted
NIPS
2003
15 years 3 months ago
Analytical Solution of Spike-timing Dependent Plasticity Based on Synaptic Biophysics
Spike timing plasticity (STDP) is a special form of synaptic plasticity where the relative timing of post- and presynaptic activity determines the change of the synaptic weight. O...
Bernd Porr, Ausra Saudargiene, Florentin Wörg...
121
Voted
WSC
1997
15 years 3 months ago
Modeling Dependencies in Stochastic Simulation Inputs
We discuss some basic techniques for modeling dependence between the random variables that are inputs to a simulation model, with the main emphasis being continuous bivariate dist...
James R. Wilson