Sorting Overlapping Spike Waveforms from Electrode and Tetrode Recordings.

One of the outstanding problems in the sorting of neuronal spike trains is the resolution of overlapping spikes. Resolving these spikes can significantly improve a range of analyses, such as response variability, correlation, and latency. In this paper, we describe a partially automated method that is capable of resolving overlapping spikes. After constructing template waveforms for well-isolated and distinct single units, we generated pair-wise combinations of those templates at all possible time shifts from each other. Subsequently, overlapping waveforms were identified by cluster analysis, and then assigned to their respective single-unit combinations. We examined the performance of this method using simulated data from an earlier study, and found that we were able to resolve an average of 83% of the overlapping waveforms across various signal-to-noise ratios, an improvement of approximately 32% over the results reported in the earlier study. When applied to additional simulated data sets generated from single-electrode and tetrode recordings, we were able to resolve 91% of the overlapping waveforms with a false positive rate of 0.19% for single-electrode data, and 95% of the overlapping waveforms with a false positive rate of 0.27% for tetrode data. We also applied our method to electrode and tetrode data recorded from the primary visual cortex, and the results obtained for these datasets suggest that our method provides an efficient means of sorting overlapping waveforms. This method can easily be added as an extra step to commonly used spike sorting methods, such as KlustaKwik and MClust software packages, and can be applied to datasets that have already been sorted using these methods.

Responses of neurons in the marmoset primary auditory cortex to interaural level differences: comparison of pure tones and vocalizations.

Interaural level differences (ILDs) are the dominant cue for localizing the sources of high frequency sounds that differ in azimuth. Neurons in the primary auditory cortex (A1) respond differentially to ILDs of simple stimuli such as tones and noise bands, but the extent to which this applies to complex natural sounds, such as vocalizations, is not known. In sufentanil/N2O anesthetized marmosets, we compared the responses of 76 A1 neurons to three vocalizations (Ock, Tsik, and Twitter) and pure tones at cells' characteristic frequency. Each stimulus was presented with ILDs ranging from 20 dB favoring the contralateral ear to 20 dB favoring the ipsilateral ear to cover most of the frontal azimuthal space. The response to each stimulus was tested at three average binaural levels (ABLs). Most neurons were sensitive to ILDs of vocalizations and pure tones. For all stimuli, the majority of cells had monotonic ILD sensitivity functions favoring the contralateral ear, but we also observed ILD sensitivity functions that peaked near the midline and functions favoring the ipsilateral ear. Representation of ILD in A1 was better for pure tones and the Ock vocalization in comparison to the Tsik and Twitter calls; this was reflected by higher discrimination indices and greater modulation ranges. ILD sensitivity was heavily dependent on ABL: changes in ABL by ±20 dB SPL from the optimal level for ILD sensitivity led to significant decreases in ILD sensitivity for all stimuli, although ILD sensitivity to pure tones and Ock calls was most robust to such ABL changes. Our results demonstrate differences in ILD coding for pure tones and vocalizations, showing that ILD sensitivity in A1 to complex sounds cannot be simply extrapolated from that to pure tones. They also show A1 neurons do not show level-invariant representation of ILD, suggesting that such a representation of auditory space is likely to require population coding, and further processing at subsequent hierarchical stages.

Effect of background noise on neuronal coding of interaural level difference cues in rat inferior colliculus.

Humans can accurately localize sounds even in unfavourable signal-to-noise conditions. To investigate the neural mechanisms underlying this, we studied the effect of background wide-band noise on neural sensitivity to variations in interaural level difference (ILD), the predominant cue for sound localization in azimuth for high-frequency sounds, at the characteristic frequency of cells in rat inferior colliculus (IC). Binaural noise at high levels generally resulted in suppression of responses (55.8%), but at lower levels resulted in enhancement (34.8%) as well as suppression (30.3%). When recording conditions permitted, we then examined if any binaural noise effects were related to selective noise effects at each of the two ears, which we interpreted in light of well-known differences in input type (excitation and inhibition) from each ear shaping particular forms of ILD sensitivity in the IC. At high signal-to-noise ratios (SNR), in most ILD functions (41%), the effect of background noise appeared to be due to effects on inputs from both ears, while for a large percentage (35.8%) appeared to be accounted for by effects on excitatory input. However, as SNR decreased, change in excitation became the dominant contributor to the change due to binaural background noise (63.6%). These novel findings shed light on the IC neural mechanisms for sound localization in the presence of continuous background noise. They also suggest that some effects of background noise on encoding of sound location reported to be emergent in upstream auditory areas can also be observed at the level of the midbrain.