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Spikes in a haystack: dimensionality reduction for neural data and unsupervised detection of (spiking) patterns and sequences

Abstract

The availability of large scale (100s to 10000s of neurons) recording of neural ensembles in the  brain poses new challenges to the data analyst as many of the traditional paradigms of neural  data analysis do not scale well to large neural populations, and others do not provide readily  interpretable pictures. To make sense of these neural data, and to find association with behavioral  and other external variables, a key strategy is to produce a low-dimensional representation that  retains the important features about information processing, learning and computation present in  the data. Simple linear methods (e.g. PCA, ICA) have provided some initial success but new mathematical strategies are needed to address the complexities and peculiarities of neural data. In addition, the emphasis is gradually shifting towards unsupervised methods, which can be  applied to the study of spontaneous activity, or to all cases where a simple behavioral correlate is  not readily available (for example, higher order associative cortical area).

In this workshop we will cover several novel approaches to pattern detection and characterization of large neural ensemble data.

 

Schedule