Spike sorting is one of the most important data analysis problems in neurophysiology. The precision in all steps of the spike-sorting procedure critically affects the accuracy of all subsequent analyses. After data preprocessing and spike detection have been carried out properly, both feature extraction and spike clustering are the most critical subsequent steps of the spike-sorting procedure. The proposed spike sorting approach comprised a new feature extraction method based on shape, phase, and distribution features of each spike (hereinafter SS-SPDF method), which reveal significant information of the neural events under study. In addition, we applied an efficient clustering algorithm based on K-means and template optimization in phase space (hereinafter K-TOPS) that included two integrative clustering measures (validity and error indices) to verify the cohesion-dispersion among spike events during classification and the misclassification of clustering, respectively. The proposed method/algorithm was tested on both simulated data and real neural recordings. The results obtained for these datasets suggest that our spike sorting approach provides an efficient way for sorting both single-unit spikes and overlapping waveforms. By analyzing raw extracellular recordings collected from the rostral-medial prefrontal cortex (rmPFC) of behaving rabbits during classical eyeblink conditioning, we have demonstrated that the present method/algorithm performs better at classifying spikes and neurons and at assessing their modulating properties than other methods currently used in neurophysiology.

We were interested in determining whether rostral medial prefrontal cortex (rmPFC) neurons participate in the measurement of conditioned stimulus-unconditioned stimulus (CS-US) time intervals during classical eyeblink conditioning. Rabbits were conditioned with a delay paradigm consisting of a tone as CS. The CS started 50, 250, 500, 1000, or 2000 ms before and coterminated with an air puff (100 ms) directed at the cornea as the US. Eyelid movements were recorded with the magnetic search coil technique and the EMG activity of the orbicularis oculi muscle. Firing activities of rmPFC neurons were recorded across conditioning sessions. Reflex and conditioned eyelid responses presented a dominant oscillatory frequency of Ϸ12 Hz. The firing rate of each recorded neuron presented a single peak of activity with a frequency dependent on the CS-US interval (i.e., Ϸ12 Hz for 250 ms, Ϸ6 Hz for 500 ms, andϷ3 Hz for 1000 ms). Interestingly, rmPFC neurons presented their dominant firing peaks at three precise times evenly distributed with respect to CS start and also depending on the duration of the CS-US interval (only for intervals of 250, 500, and 1000 ms). No significant neural responses were recorded at very short (50 ms) or long (2000 ms) CS-US intervals. rmPFC neurons seem not to encode the oscillatory properties characterizing conditioned eyelid responses in rabbits, but are probably involved in the determination of CS-US intervals of an intermediate range (250 -1000 ms). We propose that a variable oscillator underlies the generation of working memories in rabbits.

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