Perspectives of Monge properties in optimization
Discrete Applied Mathematics
Spikes: exploring the neural code
Spikes: exploring the neural code
Selected topics on assignment problems
Discrete Applied Mathematics
K-means clustering via principal component analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Neural Computation
An information-geometric framework for statistical inferences in the neural spike train space
Journal of Computational Neuroscience
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The metrization of the space of neural responses is an ongoing research program seeking to find natural ways to describe, in geometrical terms, the sets of possible activities in the brain. One component of this program is spike metrics---notions of distance between two spike trains recorded from a neuron. Alignment spike metrics work by identifying “equivalent” spikes in both trains. We present an alignment spike metric having underlying geometrical structure; the version is Euclidean and is suitable for further embedding in Euclidean spaces by multidimensional scaling methods or related procedures. We show how to implement a fast algorithm for the computation of this metric based on bipartite graph matching theory.