(found 2 matches in 0.001756s)
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The Importance of Forgetting: Limiting Memory Improves Recovery of Topological Characteristics From Neural Data
(2018)
Samir Chowdhury, Bowen Dai, Facundo Mémoli
Abstract
We develop of a line of work initiated by Curto and Itskov towards understanding the amount of information contained in the spike trains of hippocampal place cells via topology considerations. Previously, it was established that simply knowing which groups of place cells fire together in an animal’s hippocampus is sufficient to extract the global topology of the animal’s physical environment. We model a system where collections of place cells group and ungroup according to short-term plasticity rules. In particular, we obtain the surprising result that in experiments with spurious firing, the accuracy of the extracted topological information decreases with the persistence (beyond a certain regime) of the cell groups. This suggests that synaptic transience, or forgetting, is a mechanism by which the brain counteracts the effects of spurious place cell activity.
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Topological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition
(2007)
Gurjeet Singh, Facundo Mémoli, Gunnar Carlsson
Abstract
We present a computational method for extracting simple descriptions of high dimensional data sets in the form of simplicial complexes. Our method, called Mapper, is based on the idea of partial clustering of the data guided by a set of functions defined on the data. The proposed method is not dependent on any particular clustering algorithm, i.e. any clustering algorithm may be used with Mapper. We implement this method and present a few sample applications in which simple descriptions of the data present important information about its structure.