How advances in neural recording affect data analysis - PubMed (original) (raw)

How advances in neural recording affect data analysis

Ian H Stevenson et al. Nat Neurosci. 2011 Feb.

Abstract

Over the last five decades, progress in neural recording techniques has allowed the number of simultaneously recorded neurons to double approximately every 7 years, mimicking Moore's law. Such exponential growth motivates us to ask how data analysis techniques are affected by progressively larger numbers of recorded neurons. Traditionally, neurons are analyzed independently on the basis of their tuning to stimuli or movement. Although tuning curve approaches are unaffected by growing numbers of simultaneously recorded neurons, newly developed techniques that analyze interactions between neurons become more accurate and more complex as the number of recorded neurons increases. Emerging data analysis techniques should consider both the computational costs and the potential for more accurate models associated with this exponential growth of the number of recorded neurons.

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Conflict of interest statement

COMPETING FINANCIAL INTERESTS

The authors declare no competing financial interests.

Figures

Figure 1

Figure 1

Exponential growth in the number of recorded neurons. (a) Examining 56 studies published over the last five decades, we found that the number of simultaneously recorded neurons doubled approximately every 7 years. (b) A timeline of recording technologies during this period shows the development from single-electrode recordings to multi-electrode arrays and in vivo imaging techniques. Images of recording techniques reprinted from refs. – with permission of Elsevier, Springer Science + Business Media, and Am. Physiol. Soc. Image of Utah array reprinted from ref. , © 1999 IEEE. Ca2+ imaging reprinted from ref. 33, © 2003 Natl. Acad. Sci. USA.

Figure 2

Figure 2

Approaches to neural data analysis and the scaling of spike prediction accuracy. (a) There are two main approaches to modeling multi-electrode data: mapping tuning properties to describe how neurons relate to stimuli or movement and mapping interactions between neurons. These techniques aim to predict spiking based on either external variables or other neural signals. (b) In data recorded from motor cortex (top) and visual cortex (bottom), spike prediction accuracy grows when modeling interactions between neurons, but is constant when modeling tuning curves. Shaded regions denote ± s.e.m. across neurons. (c) An alternative approach is to consider simultaneously recorded neural activity as an expression of a latent, low-dimensional state space. These spaces can be extracted by first estimating smooth firing rates for each neuron and then using a dimensionality reduction technique such as factor analysis. Features of these state spaces can then be used to predict reaction times or reach targets on a trial-by-trial basis or to describe neural variability. Purple and green ellipses represent neural variability at target onset and movement onset, respectively.

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