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Adaptation and selective information transmission in the cricket auditory neuron AN2.

Wimmer K, Hildebrandt KJ, Hennig RM, Obermayer K - PLoS Comput. Biol. (2008)

Bottom Line: The spike responses were thus reduced for low-intensity sounds.Most remarkably, and in contrast to the infomax principle, adaptation actually reduces the amount of encoded information when considering the whole range of input signals.The response curve changes are also not consistent with the selective coding hypothesis, because the amount of information conveyed about the loudest part of the signal does not increase as predicted but remains nearly constant.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Electrical Engineering, Technische Universität Berlin, Berlin, Germany. klaus@cs.tu-berlin.de

ABSTRACT
Sensory systems adapt their neural code to changes in the sensory environment, often on multiple time scales. Here, we report a new form of adaptation in a first-order auditory interneuron (AN2) of crickets. We characterize the response of the AN2 neuron to amplitude-modulated sound stimuli and find that adaptation shifts the stimulus-response curves toward higher stimulus intensities, with a time constant of 1.5 s for adaptation and recovery. The spike responses were thus reduced for low-intensity sounds. We then address the question whether adaptation leads to an improvement of the signal's representation and compare the experimental results with the predictions of two competing hypotheses: infomax, which predicts that information conveyed about the entire signal range should be maximized, and selective coding, which predicts that "foreground" signals should be enhanced while "background" signals should be selectively suppressed. We test how adaptation changes the input-response curve when presenting signals with two or three peaks in their amplitude distributions, for which selective coding and infomax predict conflicting changes. By means of Bayesian data analysis, we quantify the shifts of the measured response curves and also find a slight reduction of their slopes. These decreases in slopes are smaller, and the absolute response thresholds are higher than those predicted by infomax. Most remarkably, and in contrast to the infomax principle, adaptation actually reduces the amount of encoded information when considering the whole range of input signals. The response curve changes are also not consistent with the selective coding hypothesis, because the amount of information conveyed about the loudest part of the signal does not increase as predicted but remains nearly constant. Less information is transmitted about signals with lower intensity.

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Time course of adaptation and recovery of a T. leo cell (A1,A2) and of a T. oceanicus cell (B1,B2).The response to the test stimulus is plotted against the duration of the adapting stimulus (A1,B1) and the delay between the adapting and the test stimulus (A2,B2). Displayed are the average spike counts in the 200 ms time window of the test stimulus (cf. Figure 4). The intensity of the test stimulus was equal to the average intensity of the adapting stimulus (0 dB relative intensity). The error bars denote the standard deviation. Solid lines indicate the exponential function with the set of parameters with the highest value of the posterior distribution (see Methods, Bayesian data analysis).
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pcbi-1000182-g006: Time course of adaptation and recovery of a T. leo cell (A1,A2) and of a T. oceanicus cell (B1,B2).The response to the test stimulus is plotted against the duration of the adapting stimulus (A1,B1) and the delay between the adapting and the test stimulus (A2,B2). Displayed are the average spike counts in the 200 ms time window of the test stimulus (cf. Figure 4). The intensity of the test stimulus was equal to the average intensity of the adapting stimulus (0 dB relative intensity). The error bars denote the standard deviation. Solid lines indicate the exponential function with the set of parameters with the highest value of the posterior distribution (see Methods, Bayesian data analysis).

Mentions: To quantify the time course of adaptation and recovery we analyzed the neural responses to test stimuli that had the same relative intensity as the adapting stimuli (0 dB). Additional cells were recorded with a reduced version of the stimulus protocol that only included these 0 dB test stimuli (the total number of cells available for each species and each stimulus protocol is stated in Table 1). In order to determine the adaptation and recovery time constants τa and τr, we fitted an exponential decay model to the neural responses (see Methods, Bayesian data analysis). Figure 6A and 6B show examples of recorded data and exponential fits for a T. oceanicus and a T. leo cell. Both time constants lie in the range of 1 second for both of these cells. This is considerably longer than the short-term firing rate adaptation, which operates on a time scale of 40 ms.


Adaptation and selective information transmission in the cricket auditory neuron AN2.

Wimmer K, Hildebrandt KJ, Hennig RM, Obermayer K - PLoS Comput. Biol. (2008)

Time course of adaptation and recovery of a T. leo cell (A1,A2) and of a T. oceanicus cell (B1,B2).The response to the test stimulus is plotted against the duration of the adapting stimulus (A1,B1) and the delay between the adapting and the test stimulus (A2,B2). Displayed are the average spike counts in the 200 ms time window of the test stimulus (cf. Figure 4). The intensity of the test stimulus was equal to the average intensity of the adapting stimulus (0 dB relative intensity). The error bars denote the standard deviation. Solid lines indicate the exponential function with the set of parameters with the highest value of the posterior distribution (see Methods, Bayesian data analysis).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2527132&req=5

pcbi-1000182-g006: Time course of adaptation and recovery of a T. leo cell (A1,A2) and of a T. oceanicus cell (B1,B2).The response to the test stimulus is plotted against the duration of the adapting stimulus (A1,B1) and the delay between the adapting and the test stimulus (A2,B2). Displayed are the average spike counts in the 200 ms time window of the test stimulus (cf. Figure 4). The intensity of the test stimulus was equal to the average intensity of the adapting stimulus (0 dB relative intensity). The error bars denote the standard deviation. Solid lines indicate the exponential function with the set of parameters with the highest value of the posterior distribution (see Methods, Bayesian data analysis).
Mentions: To quantify the time course of adaptation and recovery we analyzed the neural responses to test stimuli that had the same relative intensity as the adapting stimuli (0 dB). Additional cells were recorded with a reduced version of the stimulus protocol that only included these 0 dB test stimuli (the total number of cells available for each species and each stimulus protocol is stated in Table 1). In order to determine the adaptation and recovery time constants τa and τr, we fitted an exponential decay model to the neural responses (see Methods, Bayesian data analysis). Figure 6A and 6B show examples of recorded data and exponential fits for a T. oceanicus and a T. leo cell. Both time constants lie in the range of 1 second for both of these cells. This is considerably longer than the short-term firing rate adaptation, which operates on a time scale of 40 ms.

Bottom Line: The spike responses were thus reduced for low-intensity sounds.Most remarkably, and in contrast to the infomax principle, adaptation actually reduces the amount of encoded information when considering the whole range of input signals.The response curve changes are also not consistent with the selective coding hypothesis, because the amount of information conveyed about the loudest part of the signal does not increase as predicted but remains nearly constant.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Electrical Engineering, Technische Universität Berlin, Berlin, Germany. klaus@cs.tu-berlin.de

ABSTRACT
Sensory systems adapt their neural code to changes in the sensory environment, often on multiple time scales. Here, we report a new form of adaptation in a first-order auditory interneuron (AN2) of crickets. We characterize the response of the AN2 neuron to amplitude-modulated sound stimuli and find that adaptation shifts the stimulus-response curves toward higher stimulus intensities, with a time constant of 1.5 s for adaptation and recovery. The spike responses were thus reduced for low-intensity sounds. We then address the question whether adaptation leads to an improvement of the signal's representation and compare the experimental results with the predictions of two competing hypotheses: infomax, which predicts that information conveyed about the entire signal range should be maximized, and selective coding, which predicts that "foreground" signals should be enhanced while "background" signals should be selectively suppressed. We test how adaptation changes the input-response curve when presenting signals with two or three peaks in their amplitude distributions, for which selective coding and infomax predict conflicting changes. By means of Bayesian data analysis, we quantify the shifts of the measured response curves and also find a slight reduction of their slopes. These decreases in slopes are smaller, and the absolute response thresholds are higher than those predicted by infomax. Most remarkably, and in contrast to the infomax principle, adaptation actually reduces the amount of encoded information when considering the whole range of input signals. The response curve changes are also not consistent with the selective coding hypothesis, because the amount of information conveyed about the loudest part of the signal does not increase as predicted but remains nearly constant. Less information is transmitted about signals with lower intensity.

Show MeSH
Related in: MedlinePlus