Detecting dynamical changes within a simulated neural ensemble using a measure of representational quality

Jadin C. Jackson, A. David Redish

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Technological advances allowing simultaneous recording of neuronal ensembles have led to many developments in our understanding of how the brain performs neural computations. One key technique for extracting information from neural populations has been population reconstruction. While reconstruction is a powerful tool, it only provides a value and gives no indication of the quality of the representation itself. In this paper, we present a mathematically and statistically justified measure for assessing the quality of a representation in a neuronal ensemble. Using a simulated neural network, we show that this measure can distinguish between system states and identify moments of dynamical change within the system. While the examples used in this paper all derive from a standard network model, the measure itself is very general. It requires only a representational space, measured tuning curves, and neural ensembles.

Original languageEnglish (US)
Pages (from-to)629-645
Number of pages17
JournalNetwork: Computation in Neural Systems
Volume14
Issue number4
DOIs
StatePublished - Nov 2003

Bibliographical note

Funding Information:
We thank Dr C Bingham and Dr J Baxter for helpful discussions on statistical issues and probability theory, as well as N C Schmitzer-Torbert for generally helpful dialogues. This work was supported by NIH MH68029-01. JCJ was supported by NSF-IGERT training grant No 9870633.

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