Multiple neural spike train data analysis: state-of-the-art and future challenges

Abstract

Multiple electrodes are now a standard tool in neuroscience research that make it possible to study the simultaneous activity of several neurons in a given brain region or across different regions. The data from multi-electrode studies present important analysis challenges that must be resolved for optimal use of these neurophysiological measurements to answer questions about how the brain works. Here we review statistical methods for the analysis of multiple neural spike-train data and discuss future challenges for methodology research.

Statistical Models of Spike Trains

“…The advantages of this sort of model become more apparent when one considers multiple simultaneously-recorded spike trains (Brown et al, 2004), where interactions among neurons may be modeled by inclusion of additional terms that define the conditional intensity (Chornoboy et al, 1988;Paninski et al, 2004a;Okatan et al, 2005;Truccolo et al, 2005;Kulkarni and Paninski, 2007;Pillow et al, 2008;Czanner et al, 2008). IF models have been successfully employed, for example, to explore the cross-correlation properties of pairs of simultaneously-recorded neurons (Iyengar, 1985;de la Rocha et al, 2007;Carandini et al, 2007).…”

Automated spike sorting using density grid contour clustering and subtractive waveform decomposition

“…Moreover, waveform shifting and spike overlapping can result in distorted waveforms that will not be located within well defined clusters and can show up as outliers in parametrically modeled distributions (figs. 3 and 7 in this paper; Brown et al 2004;Takahashi et al 2003;. The ability to process irregular waveforms including overlaps and shifts provides a more accurate and unbiased representation of neural activity.…”

Techniques for extracting single-trial activity patterns from large-scale neural recordings

“…Unfortunately, gaining statistical power across neurons ( Figure 1C, right column) is not as trivial as gaining statistical power by averaging across trials ( Figure 1C, left column). It requires analysis methods capable of productively reducing the high dimensionality of the recorded responses [7,39,40]. Before committing to that path, it is reasonable to ask whether trial-by-trial variability is, in practice, large enough to pose a problem.…”