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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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Clusters of bursts from two different outdoor recordings and their best matching cluster.The examples show matches for burst clusters in response to natural background noise. D defines the distance between the exemplars of the two matched clusters under the spike-time metric. 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-g010: Clusters of bursts from two different outdoor recordings and their best matching cluster.The examples show matches for burst clusters in response to natural background noise. D defines the distance between the exemplars of the two matched clusters under the spike-time metric. The numbers in parentheses give the average distances between Poisson spike trains with identical time-varying firing rate profiles.

Mentions: In a similar way we matched clusters of bursts from different outdoor recording sessions, in which the activity of omega neuron from different animals was recorded at different nights (sometimes in different years). The examples of cluster matching results in Figure 10 show that also under these conditions it is possible to find close matches for some clusters of bursts. Comparing the similarity indices D in Figures 9 and 10 would indicate that some of the cluster matches between different animals in different outdoor recording conditions are closer than the cluster matches between outdoor and laboratory recording conditions. The reason for this might be the lower number of spikes under lab conditions, due to the complete absence of noise. The matching distance is substantially lower than the distance of Poisson spike trains with identical statistics, which indicates that the precision of firing in both preparations is higher than can be explained by a stochastic, purely firing-rate based model.


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 different outdoor recordings and their best matching cluster.The examples show matches for burst clusters in response to natural background noise. D defines the distance between the exemplars of the two matched clusters under the spike-time metric. 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-g010: Clusters of bursts from two different outdoor recordings and their best matching cluster.The examples show matches for burst clusters in response to natural background noise. D defines the distance between the exemplars of the two matched clusters under the spike-time metric. The numbers in parentheses give the average distances between Poisson spike trains with identical time-varying firing rate profiles.
Mentions: In a similar way we matched clusters of bursts from different outdoor recording sessions, in which the activity of omega neuron from different animals was recorded at different nights (sometimes in different years). The examples of cluster matching results in Figure 10 show that also under these conditions it is possible to find close matches for some clusters of bursts. Comparing the similarity indices D in Figures 9 and 10 would indicate that some of the cluster matches between different animals in different outdoor recording conditions are closer than the cluster matches between outdoor and laboratory recording conditions. The reason for this might be the lower number of spikes under lab conditions, due to the complete absence of noise. The matching distance is substantially lower than the distance of Poisson spike trains with identical statistics, which indicates that the precision of firing in both preparations is higher than can be explained by a stochastic, purely firing-rate based model.

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