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Probing real sensory worlds of receivers with unsupervised clustering.

Pfeiffer M, Hartbauer M, Lang AB, Maass W, Römer H - PLoS ONE (2012)

Bottom Line: Our results show that the reliability of burst coding in the time domain is so high that identical stimuli lead to extremely similar spike pattern responses, even for different preparations on different dates, and even if one of the preparations is recorded outdoors and the other one in the sound proof lab.Our study shows that burst coding can provide a reliable mechanism for acoustic insects to classify and discriminate signals under very noisy real-world conditions.This gives new insights into the neural mechanisms potentially used by bushcrickets to discriminate conspecific songs from sounds of predators in similar carrier frequency bands.

View Article: PubMed Central - PubMed

Affiliation: Institute for Theoretical Computer Science, TU Graz, Graz, Austria. pfeiffer@ini.phys.ethz.ch

ABSTRACT
The task of an organism to extract information about the external environment from sensory signals is based entirely on the analysis of ongoing afferent spike activity provided by the sense organs. We investigate the processing of auditory stimuli by an acoustic interneuron of insects. In contrast to most previous work we do this by using stimuli and neurophysiological recordings directly in the nocturnal tropical rainforest, where the insect communicates. Different from typical recordings in sound proof laboratories, strong environmental noise from multiple sound sources interferes with the perception of acoustic signals in these realistic scenarios. We apply a recently developed unsupervised machine learning algorithm based on probabilistic inference to find frequently occurring firing patterns in the response of the acoustic interneuron. We can thus ask how much information the central nervous system of the receiver can extract from bursts without ever being told which type and which variants of bursts are characteristic for particular stimuli. Our results show that the reliability of burst coding in the time domain is so high that identical stimuli lead to extremely similar spike pattern responses, even for different preparations on different dates, and even if one of the preparations is recorded outdoors and the other one in the sound proof lab. Simultaneous recordings in two preparations exposed to the same acoustic environment reveal that characteristics of burst patterns are largely preserved among individuals of the same species. Our study shows that burst coding can provide a reliable mechanism for acoustic insects to classify and discriminate signals under very noisy real-world conditions. This gives new insights into the neural mechanisms potentially used by bushcrickets to discriminate conspecific songs from sounds of predators in similar carrier frequency bands.

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Illustration of modified spike metrics, and spike train distance matrices.A, B) Illustration of the burst-shift operator. In A, the two bursts appear to be very different under the standard spike-time metric from [15], because there is a single (purple) spike before the pattern of two spike triplets, and so all subsequent spike times are shifted. The burst-shift operator in B deletes the first (purple dashed) spike and re-aligns the new first spikes of the two bursts. The distance between the two spike train then results from a relatively cheap series of spike shifts (green dotted lines), plus the cost for deleting the initial spike. C, D) Distance matrices for a single preparation in the natural habitat before (C) and after clustering (D). Light pixels indicate high similarity of bursts, whereas dark pixels show larger distances. The clustering process leads to the clear formation of groups of similar bursts.
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pone-0037354-g006: Illustration of modified spike metrics, and spike train distance matrices.A, B) Illustration of the burst-shift operator. In A, the two bursts appear to be very different under the standard spike-time metric from [15], because there is a single (purple) spike before the pattern of two spike triplets, and so all subsequent spike times are shifted. The burst-shift operator in B deletes the first (purple dashed) spike and re-aligns the new first spikes of the two bursts. The distance between the two spike train then results from a relatively cheap series of spike shifts (green dotted lines), plus the cost for deleting the initial spike. C, D) Distance matrices for a single preparation in the natural habitat before (C) and after clustering (D). Light pixels indicate high similarity of bursts, whereas dark pixels show larger distances. The clustering process leads to the clear formation of groups of similar bursts.

Mentions: The result of the clustering is illustrated in Figure 6C and D. Here the distance matrix is shown before and after the clustering process (dark indicates large distance). Before the clustering, the distance matrix for 1000 randomly picked bursts is displayed, where the ordering of the burst indices corresponds to the order in which they were recorded. The clustering process rearranged the order of the bursts, by grouping them into homogeneous clusters, which are displayed in the arbitrary order that is produced by the affinity propagation algorithm (see Materials and Methods). One can clearly recognize these groups from the distance matrix, by observing blocks that have low inter-cluster distance, and larger distance to other groups of bursts.


Probing real sensory worlds of receivers with unsupervised clustering.

Pfeiffer M, Hartbauer M, Lang AB, Maass W, Römer H - PLoS ONE (2012)

Illustration of modified spike metrics, and spike train distance matrices.A, B) Illustration of the burst-shift operator. In A, the two bursts appear to be very different under the standard spike-time metric from [15], because there is a single (purple) spike before the pattern of two spike triplets, and so all subsequent spike times are shifted. The burst-shift operator in B deletes the first (purple dashed) spike and re-aligns the new first spikes of the two bursts. The distance between the two spike train then results from a relatively cheap series of spike shifts (green dotted lines), plus the cost for deleting the initial spike. C, D) Distance matrices for a single preparation in the natural habitat before (C) and after clustering (D). Light pixels indicate high similarity of bursts, whereas dark pixels show larger distances. The clustering process leads to the clear formation of groups of similar bursts.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3368931&req=5

pone-0037354-g006: Illustration of modified spike metrics, and spike train distance matrices.A, B) Illustration of the burst-shift operator. In A, the two bursts appear to be very different under the standard spike-time metric from [15], because there is a single (purple) spike before the pattern of two spike triplets, and so all subsequent spike times are shifted. The burst-shift operator in B deletes the first (purple dashed) spike and re-aligns the new first spikes of the two bursts. The distance between the two spike train then results from a relatively cheap series of spike shifts (green dotted lines), plus the cost for deleting the initial spike. C, D) Distance matrices for a single preparation in the natural habitat before (C) and after clustering (D). Light pixels indicate high similarity of bursts, whereas dark pixels show larger distances. The clustering process leads to the clear formation of groups of similar bursts.
Mentions: The result of the clustering is illustrated in Figure 6C and D. Here the distance matrix is shown before and after the clustering process (dark indicates large distance). Before the clustering, the distance matrix for 1000 randomly picked bursts is displayed, where the ordering of the burst indices corresponds to the order in which they were recorded. The clustering process rearranged the order of the bursts, by grouping them into homogeneous clusters, which are displayed in the arbitrary order that is produced by the affinity propagation algorithm (see Materials and Methods). One can clearly recognize these groups from the distance matrix, by observing blocks that have low inter-cluster distance, and larger distance to other groups of bursts.

Bottom Line: Our results show that the reliability of burst coding in the time domain is so high that identical stimuli lead to extremely similar spike pattern responses, even for different preparations on different dates, and even if one of the preparations is recorded outdoors and the other one in the sound proof lab.Our study shows that burst coding can provide a reliable mechanism for acoustic insects to classify and discriminate signals under very noisy real-world conditions.This gives new insights into the neural mechanisms potentially used by bushcrickets to discriminate conspecific songs from sounds of predators in similar carrier frequency bands.

View Article: PubMed Central - PubMed

Affiliation: Institute for Theoretical Computer Science, TU Graz, Graz, Austria. pfeiffer@ini.phys.ethz.ch

ABSTRACT
The task of an organism to extract information about the external environment from sensory signals is based entirely on the analysis of ongoing afferent spike activity provided by the sense organs. We investigate the processing of auditory stimuli by an acoustic interneuron of insects. In contrast to most previous work we do this by using stimuli and neurophysiological recordings directly in the nocturnal tropical rainforest, where the insect communicates. Different from typical recordings in sound proof laboratories, strong environmental noise from multiple sound sources interferes with the perception of acoustic signals in these realistic scenarios. We apply a recently developed unsupervised machine learning algorithm based on probabilistic inference to find frequently occurring firing patterns in the response of the acoustic interneuron. We can thus ask how much information the central nervous system of the receiver can extract from bursts without ever being told which type and which variants of bursts are characteristic for particular stimuli. Our results show that the reliability of burst coding in the time domain is so high that identical stimuli lead to extremely similar spike pattern responses, even for different preparations on different dates, and even if one of the preparations is recorded outdoors and the other one in the sound proof lab. Simultaneous recordings in two preparations exposed to the same acoustic environment reveal that characteristics of burst patterns are largely preserved among individuals of the same species. Our study shows that burst coding can provide a reliable mechanism for acoustic insects to classify and discriminate signals under very noisy real-world conditions. This gives new insights into the neural mechanisms potentially used by bushcrickets to discriminate conspecific songs from sounds of predators in similar carrier frequency bands.

Show MeSH