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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

Matching of laboratory burst clusters to outdoor recordings.Clusters of bursts from laboratory recordings (black), matched to clusters of bursts from outdoor recordings (red). Four examples of matched clusters (in response to stimulus classes 4 (top left), 3 (top right), and 1 (bottom left and right) are shown. For these laboratory clusters, closely matching clusters are found in the outdoor recordings. D defines the distance between the exemplars of the two matched clusters under the spike-time metric. As a comparison, the numbers in parentheses give the average distances between Poisson spike trains with identical time-varying firing rate profiles.
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pone-0037354-g009: Matching of laboratory burst clusters to outdoor recordings.Clusters of bursts from laboratory recordings (black), matched to clusters of bursts from outdoor recordings (red). Four examples of matched clusters (in response to stimulus classes 4 (top left), 3 (top right), and 1 (bottom left and right) are shown. For these laboratory clusters, closely matching clusters are found in the outdoor recordings. D defines the distance between the exemplars of the two matched clusters under the spike-time metric. As a comparison, the numbers in parentheses give the average distances between Poisson spike trains with identical time-varying firing rate profiles.

Mentions: In Figure 9 the clusters of bursts found in a laboratory experiment, in which only artificial stimuli of classes 1,3, and 4 (see Figure 1B) were broadcast, were matched to clusters from outdoor recordings (the burst labels are not used for the matching). As can be seen from the comparison of clusters, there are very close matches of laboratory-burst clusters to clusters from outdoor recordings. On the other hand, the responses to the four-pulsed stimulus in the laboratory condition reveal a more precise timing of spikes within the bursts compared to the responses recorded outdoors. This indicates that the specific acoustic conditions of the noisy nocturnal rainforest caused some changes in this timing within bursts. Still, even in the presence of this strong distracting noise, the omega neuron responds with a very similar pattern, that significantly simplifies the decoding tasks for higher processing areas.


Probing real sensory worlds of receivers with unsupervised clustering.

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

Matching of laboratory burst clusters to outdoor recordings.Clusters of bursts from laboratory recordings (black), matched to clusters of bursts from outdoor recordings (red). Four examples of matched clusters (in response to stimulus classes 4 (top left), 3 (top right), and 1 (bottom left and right) are shown. For these laboratory clusters, closely matching clusters are found in the outdoor recordings. D defines the distance between the exemplars of the two matched clusters under the spike-time metric. As a comparison, the numbers in parentheses give the average distances between Poisson spike trains with identical time-varying firing rate profiles.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0037354-g009: Matching of laboratory burst clusters to outdoor recordings.Clusters of bursts from laboratory recordings (black), matched to clusters of bursts from outdoor recordings (red). Four examples of matched clusters (in response to stimulus classes 4 (top left), 3 (top right), and 1 (bottom left and right) are shown. For these laboratory clusters, closely matching clusters are found in the outdoor recordings. D defines the distance between the exemplars of the two matched clusters under the spike-time metric. As a comparison, the numbers in parentheses give the average distances between Poisson spike trains with identical time-varying firing rate profiles.
Mentions: In Figure 9 the clusters of bursts found in a laboratory experiment, in which only artificial stimuli of classes 1,3, and 4 (see Figure 1B) were broadcast, were matched to clusters from outdoor recordings (the burst labels are not used for the matching). As can be seen from the comparison of clusters, there are very close matches of laboratory-burst clusters to clusters from outdoor recordings. On the other hand, the responses to the four-pulsed stimulus in the laboratory condition reveal a more precise timing of spikes within the bursts compared to the responses recorded outdoors. This indicates that the specific acoustic conditions of the noisy nocturnal rainforest caused some changes in this timing within bursts. Still, even in the presence of this strong distracting noise, the omega neuron responds with a very similar pattern, that significantly simplifies the decoding tasks for higher processing areas.

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