Update articleA new approach to spike sorting for multi-neuronal activities recorded with a tetrode—how ICA can be practical
Introduction
Multi-neuronal recording has been used as a powerful electrophysiological technique to record the activities of multiple neurons simultaneously. Especially in recording from working brains, i.e. behaving animals, extracellular multi-neuronal recording can be used and signals from closely neighboring multiple neurons that construct a part of the local circuits can be detected with one electrode that has multiple recording tips near the neurons. The tetrode (Recce and O'keefe, 1989, Wilson and McNaughton, 1993) is an electrode constructed from four microwires insulated to the tips; it is one of the most reliable electrode for such extracellular multi-neuronal recording.
In order to identify the individual neuronal activities from the recorded multi-neuronal activity, techniques referred to as “spike sorting” are necessary (for a recent review see Lewicki, 1998). The ordinary spike sorting techniques use the different spike amplitudes and waveforms to classify single neuronal activities and such techniques are much needed when multi-neuronal activities are recorded using tetrodes. However, it is known that action potentials of individual neurons sometimes have varying shapes (Fee et al., 1996a). Moreover, we have no way of knowing whether some closely neighboring neurons generate simultaneous action potentials or not, because spike waveforms overlap on a common electrode when two or more neurons fire simultaneously. The overlapped waveforms cannot be separated by the ordinary spike sorting methods. These problems limit the ability to sort multi-neuronal activities recorded with tetrodes and the ability to detect precise spike timings (1–2 ms) among closely neighboring neurons around the microwires of the tetrode.
In this paper, we discuss the problems of the ordinary spike sorting methods and report a new procedure for spike sorting, by combining independent component analysis (ICA) (Comon, 1994) with an ordinary spike sorting technique; the new procedure is suitable for detecting the precise interactions among neighboring neurons.
Section snippets
Problems of ordinary spike sorting
There are many situations in which different neurons generate action potentials having very similar shapes in the recorded waveform. This happens when the neurons are similar in morphology and about equally distant from the different electrode tips. Moreover, it is well known that some extracellular recordings show the common occurrence of characteristic bursts called complex spikes. A stereotyped feature of these bursts is the progressive variation in spike shape during the burst. These
Automatic sorting using ICA and k-means clustering
To overcome both the non-stationarity and the spike-overlapping problems, we have developed an automatic procedure using a combination of ICA and k-means clustering.
The tetrode integrates both axonal and dendritic spikes
Due to the three-dimensional structure of the neuron, action potentials are not generated from a point source of the neuron, and extracellular electrodes may record the active backpropagation of dendritic action potentials. Several researchers, especially Buzsáki and colleagues, have suggested that the tetrode might be used to record action potentials not only from axons but also from dendrites by using a combination of intradendritic, intracellular and extracellular recordings in vivo (Buzsáki
Conclusions
The present procedure using a combination of ICA and k-means clustering can precisely detect single neurons even when they show brief bursts of high-frequency firing, which may reliably transmit signals to postsynaptic targets (Lisman, 1997) and have an increased possibility of generating overlapping spikes and non-stationary spike waveforms. The theory of synfire chains (Abeles, 1991), spike timing dependent plasticity (Bi and Poo, 1998) and the occurrence of dendritic backpropagating spikes,
Acknowledgements
This work was supported by the Sasakawa Scientific Research Grant to Dr Takahashi, and by the Special Coordination Funds for Promoting Science and Technology, the Research for the Future program, and RR21 program to Dr Sakurai.
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