Temporally Precise Cortical Firing Patterns Are Associated With Distinct Action Segments

Abstract

Despite many reports indicating the existence of precise firing sequences in cortical activity, serious objections have been raised regarding the statistics used to detect them and the relations of these sequences to behavior. We show that in behaving monkeys, pairs of spikes from different neurons tend to prefer certain time delays when measured in relation to a specific behavior. Single-unit activity was recorded from eight microelectrodes inserted into the motor and premotor cortices of two monkeys while they were performing continuous drawinglike hand movements. Repeated scribbling paths, termed drawing components, were extracted by data-mining techniques. The set of the least predictable relations between drawing components and pairs of neurons was determined and represented by one statistic termed the relations score. The chance probability of the relations score was evaluated by teetering the spike times: 1,000 surrogates were generated by randomly teetering the original time of each spike in a small window. In nine of 13 experimental days the precision was better than 12 ms and, in the best case, spike precision reached 0.5 ms.

Detecting cell assemblies in large neuronal populations

“…The methods reviewed here can be applied to large neuronal populations, and neuronal activity is assessed as a whole through the use of eigenvalue analysis. Additionally, the Marčenko-Pastur distribution opens the possibility of using an analytical and reliable statistics instead of surrogate methods employed in previous frameworks (Abeles, 2009;Abeles and Gat, 2001;Abeles and Gerstein, 1988;Humphries, 2011;Maldonado et al, 2008;Shmiel et al, 2006). In addition to being computationally demanding, a problem inherent to the use of surrogates is the fact that there is no consensus about which statistical properties should be preserved in these control data (Berger et al, 2010;Grun, 2009).…”

Data-Driven Significance Estimation for Precise Spike Correlation

“…Dithering (Gerstein 2004;Maldonado et al 2008;Pazienti et al , 2008, in the literature also called jittering (Abeles and Gat 2001;Butts et al 2007;Date et al 1998;Hatsopoulos et al 2003;Jones et al 2004), or teetering (Shmiel et al 2006), randomly displaces each individual spike within a small time window around its original position. We prefer to use the term dithering for indicating the manipulation of the data in contrast to the temporal jitter of joint spike events inherent in the experimental data.…”

Conditional modeling and the jitter method of spike resampling

“…In various guises, jitter and related spike resampling methods are in common use (Abeles and Gat 2001;Amarasingham 2004;Baker and Lemon 2000;Butts et al 2007;Date et al 1999;Dragoi and Buzsáki 2006;Fujisawa et al 2008;Furukawa and Middlebrooks 2002;Gerstein 2004;Grün 2009;Grün et al 2009;Gütig et al 2002;Harrison 2005;Harrison and Geman 2009;Hatsopoulos et al 2003;Ikegaya et al 2004;Jones et al 2004;Lu and Wang 2003;Maldonado et al 2009;Nádasdy et al 1999;Oram et al 1999;Pazienti et al 2007Pazienti et al , 2008Pipa et al 2007;Renart et al 2010;Rieke et al 1997;Rokem et al 2006;Shmiel et al 2005Shmiel et al , 2006Smith and Kohn 2008;Stark and Abeles 2009; see also Amarasingham et al 2011).…”