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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Neuronal critical degradation levels for isolated response components (i.e., total response, syllable response, pause response and onset response—see inset). a–c Discrimination analysis. The colored curves (see inset) indicate the percentage of cells (ordinate) for which the distances could be discriminated with an accuracy rate of at least 95%. a Receptor cells, b local and c ascending neurons are shown separately. To ease comparison, the cumulative percentages of bCDLs are indicated in gray. d–f Cluster analysis. Frequency of assignments to the ‘noise’ class (ordinate) according to the information-based clustering algorithm in dependence of different degradation levels (abscissa). d Receptor cells, e local and f ascending neurons. Sampling size for b and c: REC = 7, LN = 22, AN = 19
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Fig6: Neuronal critical degradation levels for isolated response components (i.e., total response, syllable response, pause response and onset response—see inset). a–c Discrimination analysis. The colored curves (see inset) indicate the percentage of cells (ordinate) for which the distances could be discriminated with an accuracy rate of at least 95%. a Receptor cells, b local and c ascending neurons are shown separately. To ease comparison, the cumulative percentages of bCDLs are indicated in gray. d–f Cluster analysis. Frequency of assignments to the ‘noise’ class (ordinate) according to the information-based clustering algorithm in dependence of different degradation levels (abscissa). d Receptor cells, e local and f ascending neurons. Sampling size for b and c: REC = 7, LN = 22, AN = 19

Mentions: Animals may ignore many temporal details in sensory spike trains that nonetheless contribute to the metric spike train dissimilarity. The metric analysis does not select stimulus features that could be relevant to the animal and that could potentially be more robustly represented in the spike train, even at high degradation levels. To account for the possibility that a neuronal recognition network in the grasshopper’s brain may, in fact, discard some spikes when it attempts to match an incoming spike train to an internal template, we restricted the analysis to specific domains of the stimulus. Important cues for signal recognition are the pauses, syllable onsets, and syllables (Balakrishnan et al. 2001). Thus, we isolated the corresponding response components in time and repeated the analysis. This manipulation had a strong impact on the spike train distance matrices (shown in Online Resource 6). Isolating the pauses in particular revealed a stronger separation between the degradation levels in the distance matrix, with a clear demarcation line between −3 and 0 dB. Isolating the onsets yielded a border between 3 and 6 dB). According to this analysis, syllable and, most notably, syllable-onset responses were more robust to noise, on average, than responses to pauses, at least for receptors and local neurons (Fig. 6). For the pause response of the receptor cells there is a steep increase of nCDLs starting already at the lowest degradation level whereas the onset response was shifted by at least 3 dB to higher degradation levels (Fig. 6a, d). For the local neurons, the difference between pause and onset responses was less pronounced (Fig. 6b, e). These results suggest that masking the syllable-pause structure might have been a critical factor for song recognition in noise. The discrimination performance based on response components of ascending neurons, however, did not differ from the total response (Fig. 6c). Although the masking of pauses had a strong effect on spike train distances of receptor cells and local neurons, for ascending neurons the degradation of the syllable pauses yielded a minor contribution to spike train dissimilarities (Fig. 6f, compare with Fig. 3).Fig. 6


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)

Neuronal critical degradation levels for isolated response components (i.e., total response, syllable response, pause response and onset response—see inset). a–c Discrimination analysis. The colored curves (see inset) indicate the percentage of cells (ordinate) for which the distances could be discriminated with an accuracy rate of at least 95%. a Receptor cells, b local and c ascending neurons are shown separately. To ease comparison, the cumulative percentages of bCDLs are indicated in gray. d–f Cluster analysis. Frequency of assignments to the ‘noise’ class (ordinate) according to the information-based clustering algorithm in dependence of different degradation levels (abscissa). d Receptor cells, e local and f ascending neurons. Sampling size for b and c: REC = 7, LN = 22, AN = 19
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Fig6: Neuronal critical degradation levels for isolated response components (i.e., total response, syllable response, pause response and onset response—see inset). a–c Discrimination analysis. The colored curves (see inset) indicate the percentage of cells (ordinate) for which the distances could be discriminated with an accuracy rate of at least 95%. a Receptor cells, b local and c ascending neurons are shown separately. To ease comparison, the cumulative percentages of bCDLs are indicated in gray. d–f Cluster analysis. Frequency of assignments to the ‘noise’ class (ordinate) according to the information-based clustering algorithm in dependence of different degradation levels (abscissa). d Receptor cells, e local and f ascending neurons. Sampling size for b and c: REC = 7, LN = 22, AN = 19
Mentions: Animals may ignore many temporal details in sensory spike trains that nonetheless contribute to the metric spike train dissimilarity. The metric analysis does not select stimulus features that could be relevant to the animal and that could potentially be more robustly represented in the spike train, even at high degradation levels. To account for the possibility that a neuronal recognition network in the grasshopper’s brain may, in fact, discard some spikes when it attempts to match an incoming spike train to an internal template, we restricted the analysis to specific domains of the stimulus. Important cues for signal recognition are the pauses, syllable onsets, and syllables (Balakrishnan et al. 2001). Thus, we isolated the corresponding response components in time and repeated the analysis. This manipulation had a strong impact on the spike train distance matrices (shown in Online Resource 6). Isolating the pauses in particular revealed a stronger separation between the degradation levels in the distance matrix, with a clear demarcation line between −3 and 0 dB. Isolating the onsets yielded a border between 3 and 6 dB). According to this analysis, syllable and, most notably, syllable-onset responses were more robust to noise, on average, than responses to pauses, at least for receptors and local neurons (Fig. 6). For the pause response of the receptor cells there is a steep increase of nCDLs starting already at the lowest degradation level whereas the onset response was shifted by at least 3 dB to higher degradation levels (Fig. 6a, d). For the local neurons, the difference between pause and onset responses was less pronounced (Fig. 6b, e). These results suggest that masking the syllable-pause structure might have been a critical factor for song recognition in noise. The discrimination performance based on response components of ascending neurons, however, did not differ from the total response (Fig. 6c). Although the masking of pauses had a strong effect on spike train distances of receptor cells and local neurons, for ascending neurons the degradation of the syllable pauses yielded a minor contribution to spike train dissimilarities (Fig. 6f, compare with Fig. 3).Fig. 6

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