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.

We show that times of spikes can be very precise. In the cerebral cortex, where each nerve cell is affected by thousands of others, it is the common belief that the exact time of a spike is random up to an averaged firing rate over tens of milliseconds. In a brain slice, precise time relations of several neurons have been observed. It remained unclear whether this phenomenon can also be observed in brains of behaving animals. Here we show, in behaving monkeys, that time intervals between spikes, measured in correspondence to a specific behavior, may be controlled to within the milliseconds range. data mining ͉ motor cortex ͉ neural codes ͉ precise timing ͉ single units A s known, most nerve cells in the brain communicate with each other by standard pulses called action potentials (or spikes). In the cerebral cortex, where each nerve cell is affected by thousands of others (1, 2), the common belief, so far, is that each neuron represents one aspect of the mental processes not by precise firing time, but by elevating its firing rate (3). However, if time relations among different neurons could be precisely controlled and read out, complex representations could be built from simpler ones efficiently and very fast (4-6). In a brain slice, precise time relations among several neurons have been observed (7). Could this phenomenon be also observed in brains of behaving animals? Here, we use data-mining techniques and rigorous statistic testing to test the precision of time intervals between spikes of different neurons. We show, in behaving monkeys, that when time intervals between spikes of different neurons are measured in correspondence to a specific behavior, timing may be controlled to the milliseconds range with the best case reaching 0.5 ms. Experiments Description and Drawings AnalysisIn our experiments, single unit activity was recorded from eight microelectrodes inserted into the motor and premotor cortices of a monkey while it was freely scribbling. Spike data analysis was carried for two sets of measurements. In the first set (consisting of 3 experimental days), time resolution of recording was 1 ms, whereas in the second set (consisting of 5 other days), it was 0.1 ms.Repeated scribbling paths were extracted by data-mining algorithms (8, 9). These paths are called drawing components. In a typical day there are 12-25 such drawing components. Fig. 1 illustrates the monkey's drawings and two simple drawing components. The Main IdeaTo determine whether there are any precise timing relations between the spikes of two neurons and the drawing, we selected a time slice before the start of each drawing component. For a given pair of neurons, we counted how many times a spike in the first neuron was followed by a spike in the second neuron within each of 50 particular time intervals. For the first set of measurements, these intervals were 0-1 ms, 2-3 ms, . . . , 98-99 ms, and for the second set they were 0-0.9 ms, 1-1.9 ms, 2-2.9 ms, . . . , 49-49.9 ms. The interval that repeated the largest number of times was hypot...

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Data Mining, or knowledge discovery, is the computer-assisted process of digging through and analyzing large quantity of data in order to extract meaningful knowledge. Data mining methods are used in many studies to identify phenomena quicker and better than human experts. One class of these methods was designed for dealing with time series data. However, when several channels of data are collected simultaneously, data mining algorithms encounter numerous difficulties since channels may be measured in different units, may be recorded at different sampling-rates, or may have completely different characteristics. Furthermore, as the size of these data increases, the amount of irrelevant data usually increases as well and the process becomes impractical. Hence, in such cases, the analyst must be capable of focusing on the informational parts while ignoring the noise data. These kinds of difficulties complicate the analysis of multichannel data as compared to the analysis of single-channel data. This paper presents a useful technique for preprocessing multi channel data. Our technique supplies tools for coping with all the above-mentioned difficulties, and prepares the data for further analysis (using common algorithms, especially from the data mining field). The paper is divided as follows. After the introduction (Section I) we describe the state of the art (Section II), follows by the main section-methodology (Section III) which is divided to four steps (3.2-3.5). The results are described in a separate section (Section IV). Then, a discussion and conclusions of the proposed methodology are given in (Sections V and VI). Acknowledgements and the references follow.