M
Michael J. Berry
Researcher at Princeton University
Publications - 89
Citations - 13433
Michael J. Berry is an academic researcher from Princeton University. The author has contributed to research in topics: Retinal ganglion & Population. The author has an hindex of 41, co-authored 89 publications receiving 12630 citations. Previous affiliations of Michael J. Berry include Harvard University.
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Book
Data Mining Techniques: For Marketing, Sales, and Customer Support
TL;DR: One of the first practical guides to mining business data, Data Mining Techniques describes techniques for detecting customer behavior patterns useful in formulating marketing, sales, and customer support strategies.
Journal ArticleDOI
Weak pairwise correlations imply strongly correlated network states in a neural population.
TL;DR: It is shown, in the vertebrate retina, that weak correlations between pairs of neurons coexist with strongly collective behaviour in the responses of ten or more neurons, and it is found that this collective behaviour is described quantitatively by models that capture the observed pairwise correlations but assume no higher-order interactions.
Book
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
TL;DR: Data Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results.
Journal ArticleDOI
The structure and precision of retinal spike trains
TL;DR: The reproducibility of retinal responses to repeated visual stimuli, in both tiger salamander and rabbit, is measured to show that the timing of a firing event conveyed several times more visual information than its spike count.
Journal ArticleDOI
Synergy, Redundancy, and Independence in Population Codes
TL;DR: This work distinguishes questions about how information is encoded by a population of neurons from how that information can be decoded, and shows that these measures form an interrelated framework for evaluating contributions of signal and noise correlations to the joint information conveyed about the stimulus.