Learning and Neuroscience
Understanding our human brain better is in itself one of the greatest challenges of our century, but at the same time mimicking the efficiency and robustness by which it represents information has become a core challenge in artificial intelligence research.
Thu, 06/11/2014 - 14:30
HuGeF, Via Nizza 52, 1st floor, old building.
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).