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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.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.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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Related in: MedlinePlus

Clusters of bursts from two recordings in the natural habitat.A) and B) show clusters of bursts obtained from two different preparations on different recording dates. Bursts associated with artificial stimuli are plotted red, bursts associated with noise are plotted in black. On the right is an illustration of the stimulus that is assigned as label to this cluster, or N if the cluster mainly consists of bursts is response to noise signals. The clusters are arranged hierarchically, grouping clusters with similar exemplars together. Longer and more structured bursts form more homogeneous groups than bursts after short pulse signals (e.g. clusters at the bottom of diagram B). Clusters in A) contain between 140 and 501 bursts, and between 145 and 328 bursts in B).
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pone-0037354-g007: Clusters of bursts from two recordings in the natural habitat.A) and B) show clusters of bursts obtained from two different preparations on different recording dates. Bursts associated with artificial stimuli are plotted red, bursts associated with noise are plotted in black. On the right is an illustration of the stimulus that is assigned as label to this cluster, or N if the cluster mainly consists of bursts is response to noise signals. The clusters are arranged hierarchically, grouping clusters with similar exemplars together. Longer and more structured bursts form more homogeneous groups than bursts after short pulse signals (e.g. clusters at the bottom of diagram B). Clusters in A) contain between 140 and 501 bursts, and between 145 and 328 bursts in B).

Mentions: Figure 7 shows the groups of bursts resulting from unsupervised clustering, plotting bursts following stimuli in red, and bursts as a result of background noise in black. On the right side we plot a sketch of the stimulus that is assigned as label to this cluster (or N if the cluster consists mostly of noise bursts). This label was determined as the stimulus class with the highest percentage of bursts in this cluster relative to its average frequency of occurrence in the whole dataset (see Materials and Methods). This avoids a bias towards assigning clusters to noise stimuli, which are four times as frequent as bursts in response to the 5 artificial stimuli.


Probing real sensory worlds of receivers with unsupervised clustering.

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

Clusters of bursts from two recordings in the natural habitat.A) and B) show clusters of bursts obtained from two different preparations on different recording dates. Bursts associated with artificial stimuli are plotted red, bursts associated with noise are plotted in black. On the right is an illustration of the stimulus that is assigned as label to this cluster, or N if the cluster mainly consists of bursts is response to noise signals. The clusters are arranged hierarchically, grouping clusters with similar exemplars together. Longer and more structured bursts form more homogeneous groups than bursts after short pulse signals (e.g. clusters at the bottom of diagram B). Clusters in A) contain between 140 and 501 bursts, and between 145 and 328 bursts in B).
© Copyright Policy
Related In: Results  -  Collection

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

pone-0037354-g007: Clusters of bursts from two recordings in the natural habitat.A) and B) show clusters of bursts obtained from two different preparations on different recording dates. Bursts associated with artificial stimuli are plotted red, bursts associated with noise are plotted in black. On the right is an illustration of the stimulus that is assigned as label to this cluster, or N if the cluster mainly consists of bursts is response to noise signals. The clusters are arranged hierarchically, grouping clusters with similar exemplars together. Longer and more structured bursts form more homogeneous groups than bursts after short pulse signals (e.g. clusters at the bottom of diagram B). Clusters in A) contain between 140 and 501 bursts, and between 145 and 328 bursts in B).
Mentions: Figure 7 shows the groups of bursts resulting from unsupervised clustering, plotting bursts following stimuli in red, and bursts as a result of background noise in black. On the right side we plot a sketch of the stimulus that is assigned as label to this cluster (or N if the cluster consists mostly of noise bursts). This label was determined as the stimulus class with the highest percentage of bursts in this cluster relative to its average frequency of occurrence in the whole dataset (see Materials and Methods). This avoids a bias towards assigning clusters to noise stimuli, which are four times as frequent as bursts in response to the 5 artificial stimuli.

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.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.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
Related in: MedlinePlus