Limits...
Neuronal precision and the limits for acoustic signal recognition in a small neuronal network.

Neuhofer D, Stemmler M, Ronacher B - J. Comp. Physiol. A Neuroethol. Sens. Neural. Behav. Physiol. (2010)

Bottom Line: By progressively corrupting the envelope of a female song, we determined the critical degradation level at which males failed to recognize a courtship call in behavioral experiments.At consecutive levels of processing, intrinsic variability increased, while the sensitivity to external noise decreased.We followed two approaches to determine critical degradation levels from spike train dissimilarities, and compared the results with the limits of signal recognition measured in behaving animals.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Humboldt-Universität zu Berlin, Invalidenstrasse 43, 10115, Berlin, Germany. neuhofda@cms.hu-berlin.de

ABSTRACT
Recognition of acoustic signals may be impeded by two factors: extrinsic noise, which degrades sounds before they arrive at the receiver's ears, and intrinsic neuronal noise, which reveals itself in the trial-to-trial variability of the responses to identical sounds. Here we analyzed how these two noise sources affect the recognition of acoustic signals from potential mates in grasshoppers. By progressively corrupting the envelope of a female song, we determined the critical degradation level at which males failed to recognize a courtship call in behavioral experiments. Using the same stimuli, we recorded intracellularly from auditory neurons at three different processing levels, and quantified the corresponding changes in spike train patterns by a spike train metric, which assigns a distance between spike trains. Unexpectedly, for most neurons, intrinsic variability accounted for the main part of the metric distance between spike trains, even at the strongest degradation levels. At consecutive levels of processing, intrinsic variability increased, while the sensitivity to external noise decreased. We followed two approaches to determine critical degradation levels from spike train dissimilarities, and compared the results with the limits of signal recognition measured in behaving animals.

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Influence of signal degradation on spike count during syllables and pauses. The box plots show the differences in firing rate (between responses to the call degraded with 9 dB of noise and the response to the original song) within the syllables and within the pauses. Positive changes in the mean firing rate reflect additional spikes, whereas negative changes reflect the suppression of spikes with the degraded signal
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Fig3: Influence of signal degradation on spike count during syllables and pauses. The box plots show the differences in firing rate (between responses to the call degraded with 9 dB of noise and the response to the original song) within the syllables and within the pauses. Positive changes in the mean firing rate reflect additional spikes, whereas negative changes reflect the suppression of spikes with the degraded signal

Mentions: Two factors contribute to the distance between two spike trains, each with different consequences for stimulus coding. The timing of spikes between the spike trains can shift; on the other hand, spikes in one train are not matched by spikes in the other—some spikes are ‘missing’. Both sources of variation lead to non-zero distances between spike trains. If the metric distances are computed at a high temporal resolution (τ = 5 ms), spike time jitter and differences in spike count contribute to the distance. As a next step, we focused on the potential effects of signal degradation on spike count. For this analysis, the original song was segmented into syllables and pauses (see Fig. 1a) and the mean spike rate for each segment was computed. Figure 3 shows how the strongest degradation (9 dB) changed the spike rate within syllables and pauses, relative to the original stimulus. For receptor cells and local neurons, the spike rate during the syllables remained unchanged (REC: t = −0.242, p = 0.813; LN: t = 0.391, p = 0.698, one sample t test; the Kruskal–Wallis test showed no significant deviation from a normal distribution), whereas the spike count increased significantly within the pauses (REC: t = 8.224, p < 0.001, LN: t = 7.699, p < 0.001; one sample t test). For ascending neurons, we observed a small but significant decrease of spike count within the syllables (t = −2.489, p < 0.05) and additional spikes during the pauses (t = 4.057, p < 0.001).Fig. 3


Neuronal precision and the limits for acoustic signal recognition in a small neuronal network.

Neuhofer D, Stemmler M, Ronacher B - J. Comp. Physiol. A Neuroethol. Sens. Neural. Behav. Physiol. (2010)

Influence of signal degradation on spike count during syllables and pauses. The box plots show the differences in firing rate (between responses to the call degraded with 9 dB of noise and the response to the original song) within the syllables and within the pauses. Positive changes in the mean firing rate reflect additional spikes, whereas negative changes reflect the suppression of spikes with the degraded signal
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3040818&req=5

Fig3: Influence of signal degradation on spike count during syllables and pauses. The box plots show the differences in firing rate (between responses to the call degraded with 9 dB of noise and the response to the original song) within the syllables and within the pauses. Positive changes in the mean firing rate reflect additional spikes, whereas negative changes reflect the suppression of spikes with the degraded signal
Mentions: Two factors contribute to the distance between two spike trains, each with different consequences for stimulus coding. The timing of spikes between the spike trains can shift; on the other hand, spikes in one train are not matched by spikes in the other—some spikes are ‘missing’. Both sources of variation lead to non-zero distances between spike trains. If the metric distances are computed at a high temporal resolution (τ = 5 ms), spike time jitter and differences in spike count contribute to the distance. As a next step, we focused on the potential effects of signal degradation on spike count. For this analysis, the original song was segmented into syllables and pauses (see Fig. 1a) and the mean spike rate for each segment was computed. Figure 3 shows how the strongest degradation (9 dB) changed the spike rate within syllables and pauses, relative to the original stimulus. For receptor cells and local neurons, the spike rate during the syllables remained unchanged (REC: t = −0.242, p = 0.813; LN: t = 0.391, p = 0.698, one sample t test; the Kruskal–Wallis test showed no significant deviation from a normal distribution), whereas the spike count increased significantly within the pauses (REC: t = 8.224, p < 0.001, LN: t = 7.699, p < 0.001; one sample t test). For ascending neurons, we observed a small but significant decrease of spike count within the syllables (t = −2.489, p < 0.05) and additional spikes during the pauses (t = 4.057, p < 0.001).Fig. 3

Bottom Line: By progressively corrupting the envelope of a female song, we determined the critical degradation level at which males failed to recognize a courtship call in behavioral experiments.At consecutive levels of processing, intrinsic variability increased, while the sensitivity to external noise decreased.We followed two approaches to determine critical degradation levels from spike train dissimilarities, and compared the results with the limits of signal recognition measured in behaving animals.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Humboldt-Universität zu Berlin, Invalidenstrasse 43, 10115, Berlin, Germany. neuhofda@cms.hu-berlin.de

ABSTRACT
Recognition of acoustic signals may be impeded by two factors: extrinsic noise, which degrades sounds before they arrive at the receiver's ears, and intrinsic neuronal noise, which reveals itself in the trial-to-trial variability of the responses to identical sounds. Here we analyzed how these two noise sources affect the recognition of acoustic signals from potential mates in grasshoppers. By progressively corrupting the envelope of a female song, we determined the critical degradation level at which males failed to recognize a courtship call in behavioral experiments. Using the same stimuli, we recorded intracellularly from auditory neurons at three different processing levels, and quantified the corresponding changes in spike train patterns by a spike train metric, which assigns a distance between spike trains. Unexpectedly, for most neurons, intrinsic variability accounted for the main part of the metric distance between spike trains, even at the strongest degradation levels. At consecutive levels of processing, intrinsic variability increased, while the sensitivity to external noise decreased. We followed two approaches to determine critical degradation levels from spike train dissimilarities, and compared the results with the limits of signal recognition measured in behaving animals.

Show MeSH
Related in: MedlinePlus