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Single-neuron and ensemble contributions to decoding simultaneously recorded spike trains
Book chapter

Single-neuron and ensemble contributions to decoding simultaneously recorded spike trains

Mark Laubach, Nandakumar S Narayanan and Eyal Y Kimchi
Information Processing by Neuronal Populations, pp.120-148
Cambridge University Press
10/23/2008
DOI: 10.1017/CBO9780511541650.006

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Abstract

Decoding simultaneously recorded spike trainsPioneering studies of motor cortex by Georgopoulos and colleagues (e.g. Georgopoulos et al., 1982) established that “population vectors,” constructed from weighted averages of the responses of single neurons, can accurately predict behavioral variables, such as movement direction. This approach has been used to study population coding in a number of cortical systems and has led to the view that cortical neurons act as independent processors of information (e.g. Gochin et al., 1994). However, some recent work has challenged this interpretation of neural population activity. For example, Schneidman et al. (2003) proposed interpreting neural ensemble activity by comparing ensemble information with information represented by the single neurons that comprise the ensemble. In a synergistic coding scheme, ensembles encode more than the sum of the component neurons. The advantage of synergy is that there can be a massive gain in information from the activity of multiple neurons. In a redundant coding scheme, the removal of individual neurons has little effect on encoding and thus the ensembles can be less noisy and less prone to errors. In Narayanan et al. (2005), we adapted the information-theoretical framework proposed by Schneidman et al. (2003) to measures of decoding of the performance of a delayed response task with activity from the rodent motor cortex. The predictive relationship between neural firing rates and a categorical measure of behavior, e.g. correct vs. error performance of a reaction time task, was quantified using statistical classifiers.

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