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In vivo coincidence detection in mammalian sound localization generates phase delays

Nature Neuroscience volume 18, pages 444–452 (2015)Cite this article

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Abstract

Sound localization critically depends on detection of differences in arrival time of sounds at the two ears (acoustic delay). The fundamental mechanisms are debated, but all proposals include a process of coincidence detection and a separate source of internal delay that offsets the acoustic delay and determines neural tuning. We used in vivo patch-clamp recordings of binaural neurons in the Mongolian gerbil and pharmacological manipulations to directly compare neuronal input to output and to separate excitation from inhibition. Our results cannot be accounted for by existing models and reveal that coincidence detection is not an instantaneous process, but is instead shaped by the interaction of intrinsic conductances with preceding synaptic activity. This interaction generates an internal delay as an intrinsic part of the process of coincidence detection. The multiplication and time-shifting stages thought to extract synchronous activity in many brain areas can therefore be combined in a single operation.

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Figure 1: EPSPs, IPSPs and spikes of MSO neurons in vivo.
Figure 2: Anatomical location of MSO neurons.
Figure 3: ITD tuning of MSO neurons often deviates from instantaneous coincidence detection.
Figure 4: Concentration dependent effects of strychnine on ITD functions and intrinsic physiology.
Figure 5: Symmetry of monaural excitatory inputs.
Figure 6: Deviation from instantaneous coincidence detection is related to variation in preceding Vm.
Figure 7: Preceding Vm influences spike probability and Kv1 channel activation.

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Acknowledgements

We thank P.H. Smith for the camera lucida drawings shown in Figure 2a and for feedback on the manuscript. We thank M. Brecht, N. Priebe and S. Agarwala for discussions and advice. This work was supported by a Ph. D. fellowship of the Research Foundation-Flanders (FWO) to T.P.F., project grants from FWO (G.0714.09, G.0961.11) and Research Fund KU Leuven (OT/09/50) to P.X.J., and US National Institutes of Health grants DC011403 (N.L.G. and P.X.J.) and DC006788 (N.L.G.).

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Authors and Affiliations

  1. Laboratory of Auditory Neurophysiology, KU Leuven, Leuven, Belgium

    Tom P Franken, Liting Wei & Philip X Joris

  2. Department of Neuroscience and Center for Learning and Memory, University of Texas at Austin, Austin, Texas, USA

    Michael T Roberts & Nace L Golding

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  1. Tom P Franken
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Contributions

T.P.F. performed the in vivo experiments and analyzed the data. T.P.F., M.T.R., N.L.G. and P.X.J. developed in vivo patch-clamping methods. M.T.R. performed in vitro experiments and analyzed the data. T.P.F. and L.W. performed histological processing. T.P.F., M.T.R., N.L.G. and P.X.J. designed the study and wrote the manuscript.

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Correspondence to Philip X Joris.

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Integrated supplementary information

Supplementary Figure 1 Shift of ITD tuning is observed with different methods of prediction.

(a) rITDfs and predITDfs corresponding to a positive and negative binaural beat (resp. ipsi/contra stimulus frequency 300 Hz/301 Hz and 300 Hz/299 Hz). Shift is unrelated to the sign of the binaural beat. This shows that the shift is dependent on ITD and excludes a dynamic artifact of the stimulus. Characteristic frequency = 4.6 kHz. (b) PredITDf for different prediction threshold values. Black line is rITDf. Stimulus: 300/301 Hz 70 dB (characteristic frequency = 1895 Hz). (c) Difference between BD of rITDf and predITDfs from b as a function of threshold. Predicted BD is not dependent on threshold. (d,e) ITD function is compared with the monaural prediction for two datasets. PredITDf is similar whether the complete monaural responses are summed and then thresholded (solid red line; as in Fig. 3), or the monaural EPSP period histograms are crosscorrelated (dashed red line). The latter method of prediction uses only the timing of the EPSP peaks and diminishes the weight of the amplitude differences between EPSPs, and ignores other aspects of the monaural input, such as hyperpolarizing events. Stimulus 400/401 Hz 70 dB (d) and 400/401 Hz 80 dB (e). Characteristic frequencies: 1741 Hz (d) and 1149 Hz (e). (f) Comparison of shift for both methods of prediction as in d and e at the population level. Only datasets with significant suprathreshold ITD tuning were included (Rayleigh test α <= 0.001). Symbols correspond to Figure 3b,d. Dashed line is linear fit (linear correlation; t(56) = 6.23; 58 datasets from 24 neurons).

Supplementary Figure 2 Individual traces preceding supraEPSPs and subEPSPs for ITDs with the same supraEPSP rate.

ITD1 and ITD2 as indicated in Figure 6a. Rows correspond to the datasets in Figure 6a-c. Data traces are aligned at the peak of the first derivative of the EPSP. In the datasets with a shift (top two rows), subEPSPs are preceded more frequently by smaller EPSPs (arrows), corresponding to the relative depolarization in Figure 6b.

Supplementary Figure 3 Leading depolarization hinders spiking in response to broadband noise.

(a-h) SupraEPSPs (black) and subEPSPs (red) are stacked for eight MSO neurons during binaural stimulation with broadband noise. Events were aligned on the peak of the first derivative of the EPSP. Responses were pooled across binaural conditions (e.g. different ITDs, correlated and uncorrelated noise). Right panels show the averages for supraEPSPs (black) and subEPSPs (red) separately. As for stimulation with tones, preceding relative depolarizations are associated with spike failures. CF is indicated in the right panels when known. (i) Average Vm preceding subEPSPs and supraEPSPs 1 to 0.5 ms before the maximum of the first derivative of the EPSP for the neurons in a-h. Grey lines connect individual values. Red and black lines are population averages. Significance was assessed using a one-tailed paired t-test (t(7) = 7.46). Characteristic frequency is indicated in panels a-h when known.

Supplementary Figure 4 Shift relative to preceding depolarizations 0.7 to 0.5 ms before the main EPSP peak.

There was no significant correlation between the shift and the ratio of preceding EPSPs 0.5 to 0.7 ms before the main EPSP, consistent with the effect of timing in the dynamic clamp experiments (Fig. 7f). Linear correlation (t(69) = 1.16; 71 datasets from 28 neurons)

Supplementary Figure 5 Coincidence detection in mammalian sound localization is adaptive rather than instantaneous.

(a) Left, scheme of instantaneous coincidence detection. Output results directly from events coinciding within a narrow time window (coincidence window, cw). Right, adaptive coincidence detection takes into account preceding EPSPs and is not simply predicted by coincidence of events in the coincidence window. (b) Top two rows: cycle histograms for symmetrical (first row) and asymmetrical (second row) inputs at different ITDs. Bottom: ITD functions corresponding to symmetrical inputs (black), instantaneous coincidence detection for asymmetrical inputs (dashed red line) and adaptive coincidence detection for asymmetrical inputs (solid red line). For the asymmetrical inputs, the contralateral ear evokes fewer early EPSPs. With instantaneous coincidence detection this shifts the ITD curve to the right, favoring sounds that arrive at the contralateral ear first. With adaptive coincidence detection, the small group of leading EPSPs in the contralateral input decrease spiking when sounds arrive at the contralateral ear first, thereby shifting the ITD curve to the left.

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Franken, T., Roberts, M., Wei, L. et al. In vivo coincidence detection in mammalian sound localization generates phase delays. Nat Neurosci 18, 444–452 (2015). https://doi.org/10.1038/nn.3948

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