Learning and Neuroscience
This is code for biological data analysis using a multi-state diluted discrete perceptron.
The image segmentation problem, applied to neural tissues, consists in identifying the individual structures (cell bodies, dendrites and axons) in a 3-dimensional scannerized image of a brain portion. Having a good automatic procedure for performing this task (i.e. as good as a human expert is) with good scaling properties would be useful to be able to reconstruct the connection structure of large portions of the brain (the so called "connectome"), which is supposedly a crucial part of the information needed to understand the brain functioning.
Wed, 21/10/2009 - 16:30
Generalization Learning in a Perceptron with Binary Synapses. Journal of Statistical Physics. 2009;136(5).
A review of the locust olfactory bulb, based on the following papers: 1) Gilles Laurent, Katrina MacLeod, Mark Stopfer, et al. Spatiotemporal Structure of Olfactory Inputs to the Mushroom Bodies Learn. Mem. 1998. 2) Stijn Cassenaer and Gilles Laurent Hebbian STDP in mushroom bodies facilitates the synchronous flow of olfactory information in locusts Nature 2007. 3) Laurent Moreaux and Gilles Laurent Estimating firing rates from calcium signals in locust projection neurons in vivo frontiers in neural circuits 2007.
Wed, 03/12/2008 - 12:30
Efficient supervised learning in networks with binary synapses. Proceedings of the National Academy of Sciences. 2007;104:11079-84.
SBPI is an algorithm implementing the extremely simple learning protocol for networks with binary synapses reported in Efficient supervised learning in networks with binary synapses.
The version named SBPI-MPI is a parallelized version which can be run on a cluster, using the MPI parallelization library.
This is the code associated with Learning by message-passing in networks of discrete synapses