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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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Typical examples of stimulus response curves after adaptation to the bimodal and to the trimodal stimulus distributions (A1,B1,C1) and posterior densities of the corresponding response curve parameters (A2,B2,C2).(A1,A2,C1,C2) Results for AN2 cells of T. leo. (B1,B2) Results for an AN2 cell of a T. oceanicus. (A1,B1,C1) Circles and squares denote the mean spike counts in a 200 ms time window of the test stimulus after adaptation to the bimodal and trimodal distributions, measured for 9 different relative intensities of the test stimulus (cf. protocol of Figure 3C). Error bars denote the standard deviation. Solid lines indicate the expected response curve, i.e., the response curve with the set of parameters with the mean value of the posterior distribution (see Methods, Bayesian data analysis). The shaded areas depict the intensity distribution of the stimuli (dark: bimodal stimulus distribution, light: additional peak of the trimodal stimulus distribution). (A2,B2,C2) Marginal posterior densities (cf. Methods, Bayesian data analysis) of the response curve parameters B50 (location) and S50 (slope). The posterior densities after adaptation to the bimodal (solid lines) and trimodal (dotted lines) stimulus distributions are shown in the top panels and the corresponding posterior densities of the changes (ΔB50, ΔS50) between stimulus conditions in the bottom panels. Solid (dotted) lines on top of the figures depict the 95% posterior intervals. Significant changes between stimulus conditions are indicated by a star.
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pcbi-1000182-g010: Typical examples of stimulus response curves after adaptation to the bimodal and to the trimodal stimulus distributions (A1,B1,C1) and posterior densities of the corresponding response curve parameters (A2,B2,C2).(A1,A2,C1,C2) Results for AN2 cells of T. leo. (B1,B2) Results for an AN2 cell of a T. oceanicus. (A1,B1,C1) Circles and squares denote the mean spike counts in a 200 ms time window of the test stimulus after adaptation to the bimodal and trimodal distributions, measured for 9 different relative intensities of the test stimulus (cf. protocol of Figure 3C). Error bars denote the standard deviation. Solid lines indicate the expected response curve, i.e., the response curve with the set of parameters with the mean value of the posterior distribution (see Methods, Bayesian data analysis). The shaded areas depict the intensity distribution of the stimuli (dark: bimodal stimulus distribution, light: additional peak of the trimodal stimulus distribution). (A2,B2,C2) Marginal posterior densities (cf. Methods, Bayesian data analysis) of the response curve parameters B50 (location) and S50 (slope). The posterior densities after adaptation to the bimodal (solid lines) and trimodal (dotted lines) stimulus distributions are shown in the top panels and the corresponding posterior densities of the changes (ΔB50, ΔS50) between stimulus conditions in the bottom panels. Solid (dotted) lines on top of the figures depict the 95% posterior intervals. Significant changes between stimulus conditions are indicated by a star.

Mentions: A representative example of adapted response curves of an AN2 neuron is shown in Figure 10A1, where the input-response function is plotted for the parameters A, B50, and C, which correspond to the expected parameter values (posterior means). After adaptation to the bimodally distributed stimulus (filled symbols), the cell fired with 50% of its maximal rate (parameter B50) at about 1.75 dB. Adaptation to the trimodally distributed stimulus (open symbols) shifted the response curve to higher stimulus intensities while the slope of the response curve changed only slightly. In fact, the results of the Bayesian parameter estimation, depicted in Figure 10A2, revealed that the response curve parameter B50 significantly increased for the trimodal stimulus distribution. The mean of the posterior density changed from 1.74 dB to 3.23 dB (see Methods, Bayesian data analysis for the definition of statistical significance using Bayesian posterior intervals), while there was no significant change for the slope S50 (14% decrease from 0.160 dB−1). Figure 10B shows data from a second cell. The mean value of the parameter B50 is 3.47 dB for the bimodally adapted response curve, and increased by 1.86 dB through adaptation to the trimodally distributed stimulus. The increase of B50 was again significant. The slope increased by 15% (from 0.161 dB−1 for adaptation to the bimodal stimulus) but Bayesian analysis revealed that the increase in slope was not significant. Figure 10C shows data from a third cell. This cell showed a significant albeit less pronounced change in parameter B50 of +1.06 dB accompanied by a significant decrease in the slope S50 (decrease of the posterior mean by 23.5%).


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

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

Typical examples of stimulus response curves after adaptation to the bimodal and to the trimodal stimulus distributions (A1,B1,C1) and posterior densities of the corresponding response curve parameters (A2,B2,C2).(A1,A2,C1,C2) Results for AN2 cells of T. leo. (B1,B2) Results for an AN2 cell of a T. oceanicus. (A1,B1,C1) Circles and squares denote the mean spike counts in a 200 ms time window of the test stimulus after adaptation to the bimodal and trimodal distributions, measured for 9 different relative intensities of the test stimulus (cf. protocol of Figure 3C). Error bars denote the standard deviation. Solid lines indicate the expected response curve, i.e., the response curve with the set of parameters with the mean value of the posterior distribution (see Methods, Bayesian data analysis). The shaded areas depict the intensity distribution of the stimuli (dark: bimodal stimulus distribution, light: additional peak of the trimodal stimulus distribution). (A2,B2,C2) Marginal posterior densities (cf. Methods, Bayesian data analysis) of the response curve parameters B50 (location) and S50 (slope). The posterior densities after adaptation to the bimodal (solid lines) and trimodal (dotted lines) stimulus distributions are shown in the top panels and the corresponding posterior densities of the changes (ΔB50, ΔS50) between stimulus conditions in the bottom panels. Solid (dotted) lines on top of the figures depict the 95% posterior intervals. Significant changes between stimulus conditions are indicated by a star.
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getmorefigures.php?uid=PMC2527132&req=5

pcbi-1000182-g010: Typical examples of stimulus response curves after adaptation to the bimodal and to the trimodal stimulus distributions (A1,B1,C1) and posterior densities of the corresponding response curve parameters (A2,B2,C2).(A1,A2,C1,C2) Results for AN2 cells of T. leo. (B1,B2) Results for an AN2 cell of a T. oceanicus. (A1,B1,C1) Circles and squares denote the mean spike counts in a 200 ms time window of the test stimulus after adaptation to the bimodal and trimodal distributions, measured for 9 different relative intensities of the test stimulus (cf. protocol of Figure 3C). Error bars denote the standard deviation. Solid lines indicate the expected response curve, i.e., the response curve with the set of parameters with the mean value of the posterior distribution (see Methods, Bayesian data analysis). The shaded areas depict the intensity distribution of the stimuli (dark: bimodal stimulus distribution, light: additional peak of the trimodal stimulus distribution). (A2,B2,C2) Marginal posterior densities (cf. Methods, Bayesian data analysis) of the response curve parameters B50 (location) and S50 (slope). The posterior densities after adaptation to the bimodal (solid lines) and trimodal (dotted lines) stimulus distributions are shown in the top panels and the corresponding posterior densities of the changes (ΔB50, ΔS50) between stimulus conditions in the bottom panels. Solid (dotted) lines on top of the figures depict the 95% posterior intervals. Significant changes between stimulus conditions are indicated by a star.
Mentions: A representative example of adapted response curves of an AN2 neuron is shown in Figure 10A1, where the input-response function is plotted for the parameters A, B50, and C, which correspond to the expected parameter values (posterior means). After adaptation to the bimodally distributed stimulus (filled symbols), the cell fired with 50% of its maximal rate (parameter B50) at about 1.75 dB. Adaptation to the trimodally distributed stimulus (open symbols) shifted the response curve to higher stimulus intensities while the slope of the response curve changed only slightly. In fact, the results of the Bayesian parameter estimation, depicted in Figure 10A2, revealed that the response curve parameter B50 significantly increased for the trimodal stimulus distribution. The mean of the posterior density changed from 1.74 dB to 3.23 dB (see Methods, Bayesian data analysis for the definition of statistical significance using Bayesian posterior intervals), while there was no significant change for the slope S50 (14% decrease from 0.160 dB−1). Figure 10B shows data from a second cell. The mean value of the parameter B50 is 3.47 dB for the bimodally adapted response curve, and increased by 1.86 dB through adaptation to the trimodally distributed stimulus. The increase of B50 was again significant. The slope increased by 15% (from 0.161 dB−1 for adaptation to the bimodal stimulus) but Bayesian analysis revealed that the increase in slope was not significant. Figure 10C shows data from a third cell. This cell showed a significant albeit less pronounced change in parameter B50 of +1.06 dB accompanied by a significant decrease in the slope S50 (decrease of the posterior mean by 23.5%).

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