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Found 5 papers, 2 papers with code
On the convergence of the MLE as an estimator of the learning rate in the Exp3 algorithm
When fitting the learning data of an individual to algorithm-like learning models, the observations are so dependent and non-stationary that one may wonder what the classical Maximum Likelihood Estimator (MLE) could do, even if it is the usual tool applied to experimental cognition.
Event-scheduling algorithms with Kalikow decomposition for simulating potentially infinite neuronal networks
These algorithms are based on Ogata's thinning strategy \cite{Oga81}, which always needs to simulate the whole network to access the behaviour of one particular neuron of the network.
Large scale Lasso with windowed active set for convolutional spike sorting
Spike sorting is a fundamental preprocessing step in neuroscience that is central to access simultaneous but distinct neuronal activities and therefore to better understand the animal or even human brain.
Exponential inequality for chaos based on sampling without replacement
We are interested in the behavior of particular functionals, in a framework where the only source of randomness is a sampling without replacement.
Statistics Theory Probability Statistics Theory
Microscopic approach of a time elapsed neural model
The spike trains are the main components of the information processing in the brain.
