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.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

Related in: MedlinePlus

Firing and bursting rates in the natural habitat.Firing (top) and bursting rates (bottom) of the spike activity of the omega-neuron from 21.20 hrs to 0.40 hrs at night in the natural habitat. The fluctuation in both rates is high, but firing and burst rates are correlated with a correlation coefficient of . The mean firing rate over the entire night is 11.5 Hz, and the mean bursting rate is 0.33 Hz. (Bin size: 1 sec for firing rate, 100 sec for bursting rate).
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3368931&req=5

pone-0037354-g003: Firing and bursting rates in the natural habitat.Firing (top) and bursting rates (bottom) of the spike activity of the omega-neuron from 21.20 hrs to 0.40 hrs at night in the natural habitat. The fluctuation in both rates is high, but firing and burst rates are correlated with a correlation coefficient of . The mean firing rate over the entire night is 11.5 Hz, and the mean bursting rate is 0.33 Hz. (Bin size: 1 sec for firing rate, 100 sec for bursting rate).

Mentions: Figure 3 shows firing and bursting rates in one recording over a longer time period after sunset, and it is obvious that while the recordings are very stable over multiple hours, there are considerable fluctuations on shorter time scales, mainly due to background noise. In this preparation, firing rates vary from 7 Hz to 17 Hz over the time period of 200 minutes of recording, and burst rates vary from about 0.2 to 0.8 Hz. The curves for firing and bursting rates are visibly correlated (correlation coefficient ). Artificial stimuli are only played every 10 seconds, and preparations typically respond with a single burst to these signals. From the fact that the bursting rate is always greater than 0.1 Hz, one can see that a large majority of the bursts result from background noise (in this recording of the bursts are noise bursts).


Probing real sensory worlds of receivers with unsupervised clustering.

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

Firing and bursting rates in the natural habitat.Firing (top) and bursting rates (bottom) of the spike activity of the omega-neuron from 21.20 hrs to 0.40 hrs at night in the natural habitat. The fluctuation in both rates is high, but firing and burst rates are correlated with a correlation coefficient of . The mean firing rate over the entire night is 11.5 Hz, and the mean bursting rate is 0.33 Hz. (Bin size: 1 sec for firing rate, 100 sec for bursting rate).
© Copyright Policy
Related In: Results  -  Collection

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

pone-0037354-g003: Firing and bursting rates in the natural habitat.Firing (top) and bursting rates (bottom) of the spike activity of the omega-neuron from 21.20 hrs to 0.40 hrs at night in the natural habitat. The fluctuation in both rates is high, but firing and burst rates are correlated with a correlation coefficient of . The mean firing rate over the entire night is 11.5 Hz, and the mean bursting rate is 0.33 Hz. (Bin size: 1 sec for firing rate, 100 sec for bursting rate).
Mentions: Figure 3 shows firing and bursting rates in one recording over a longer time period after sunset, and it is obvious that while the recordings are very stable over multiple hours, there are considerable fluctuations on shorter time scales, mainly due to background noise. In this preparation, firing rates vary from 7 Hz to 17 Hz over the time period of 200 minutes of recording, and burst rates vary from about 0.2 to 0.8 Hz. The curves for firing and bursting rates are visibly correlated (correlation coefficient ). Artificial stimuli are only played every 10 seconds, and preparations typically respond with a single burst to these signals. From the fact that the bursting rate is always greater than 0.1 Hz, one can see that a large majority of the bursts result from background noise (in this recording of the bursts are noise 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
Related in: MedlinePlus