Limits...
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.

Show MeSH

Related in: MedlinePlus

Analysis of the influence of the q-parameter for the Victor Purpura metric.The analysis was performed on a single dataset, consisting of 3936 bursts. A) Separability of different classes for different values of the parameter q for the Victor Purpura metric [15]. The plot shows the difference between the average between-class and within-class distances, separately plotted for the whole dataset (blue), for stimulus-classes only (red), and for noise only (black). The best separability is obtained for q values between 50 and . B) Average within-class distances for difference choices of q for bursts with identical spike count in response to artificial stimuli (red), or in response to noise (black). Our choice of  lies in a region of high similarity for the more stereotypical stimulus bursts, and low similarity of the irregular noise bursts, which is desirable. C) Histograms of spike time distances within the same class, for cost , and for bursts that have the same number of spikes, either in response to artificial stimuli (red), or in response to noise (black).
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3368931&req=5

pone-0037354-g013: Analysis of the influence of the q-parameter for the Victor Purpura metric.The analysis was performed on a single dataset, consisting of 3936 bursts. A) Separability of different classes for different values of the parameter q for the Victor Purpura metric [15]. The plot shows the difference between the average between-class and within-class distances, separately plotted for the whole dataset (blue), for stimulus-classes only (red), and for noise only (black). The best separability is obtained for q values between 50 and . B) Average within-class distances for difference choices of q for bursts with identical spike count in response to artificial stimuli (red), or in response to noise (black). Our choice of lies in a region of high similarity for the more stereotypical stimulus bursts, and low similarity of the irregular noise bursts, which is desirable. C) Histograms of spike time distances within the same class, for cost , and for bursts that have the same number of spikes, either in response to artificial stimuli (red), or in response to noise (black).

Mentions: For the final choice of the q-parameter of the spike-time metric we computed the differences between the average spike train distances of bursts of different classes, compared to the distances within the same class. A high difference indicates that the different classes can be well discriminated. Our analysis for q-values between 0 and showed that bursts of different classes can be best discriminated using values of q between 50 and (see Figure 13A). After visual inspection of the clustering results for several values of q between 50 and , we identified as the parameter which in general led to the best results, based on homogeneity of the clusters, the resulting number of clusters, as well as silhouette values [80], which are indicators of cluster qualities. We also found, that the same value of produces very good results for all recordings, but we did not attempt to optimize q for every single session.


Probing real sensory worlds of receivers with unsupervised clustering.

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

Analysis of the influence of the q-parameter for the Victor Purpura metric.The analysis was performed on a single dataset, consisting of 3936 bursts. A) Separability of different classes for different values of the parameter q for the Victor Purpura metric [15]. The plot shows the difference between the average between-class and within-class distances, separately plotted for the whole dataset (blue), for stimulus-classes only (red), and for noise only (black). The best separability is obtained for q values between 50 and . B) Average within-class distances for difference choices of q for bursts with identical spike count in response to artificial stimuli (red), or in response to noise (black). Our choice of  lies in a region of high similarity for the more stereotypical stimulus bursts, and low similarity of the irregular noise bursts, which is desirable. C) Histograms of spike time distances within the same class, for cost , and for bursts that have the same number of spikes, either in response to artificial stimuli (red), or in response to noise (black).
© Copyright Policy
Related In: Results  -  Collection

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

pone-0037354-g013: Analysis of the influence of the q-parameter for the Victor Purpura metric.The analysis was performed on a single dataset, consisting of 3936 bursts. A) Separability of different classes for different values of the parameter q for the Victor Purpura metric [15]. The plot shows the difference between the average between-class and within-class distances, separately plotted for the whole dataset (blue), for stimulus-classes only (red), and for noise only (black). The best separability is obtained for q values between 50 and . B) Average within-class distances for difference choices of q for bursts with identical spike count in response to artificial stimuli (red), or in response to noise (black). Our choice of lies in a region of high similarity for the more stereotypical stimulus bursts, and low similarity of the irregular noise bursts, which is desirable. C) Histograms of spike time distances within the same class, for cost , and for bursts that have the same number of spikes, either in response to artificial stimuli (red), or in response to noise (black).
Mentions: For the final choice of the q-parameter of the spike-time metric we computed the differences between the average spike train distances of bursts of different classes, compared to the distances within the same class. A high difference indicates that the different classes can be well discriminated. Our analysis for q-values between 0 and showed that bursts of different classes can be best discriminated using values of q between 50 and (see Figure 13A). After visual inspection of the clustering results for several values of q between 50 and , we identified as the parameter which in general led to the best results, based on homogeneity of the clusters, the resulting number of clusters, as well as silhouette values [80], which are indicators of cluster qualities. We also found, that the same value of produces very good results for all recordings, but we did not attempt to optimize q for every single session.

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