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Statistical coding and decoding of heartbeat intervals.

Lucena F, Barros AK, Príncipe JC, Ohnishi N - PLoS ONE (2011)

Bottom Line: Herein we investigate whether the cardiac system makes use of a redundancy reduction strategy to regulate the cardiac rhythm.We show that the filters yield responses that are quantitatively similar to observed heart rate responses during direct sympathetic or parasympathetic nerve stimulation.Our findings suggest that the heart decodes autonomic stimuli according to information theory principles analogous to how perceptual cues are encoded by sensory systems.

View Article: PubMed Central - PubMed

Affiliation: Biological Information Engineering Laboratory, Nagoya University, Nagoya, Aichi, Japan. lucena@ohnishi.nagoya-u.ac.jp

ABSTRACT
The heart integrates neuroregulatory messages into specific bands of frequency, such that the overall amplitude spectrum of the cardiac output reflects the variations of the autonomic nervous system. This modulatory mechanism seems to be well adjusted to the unpredictability of the cardiac demand, maintaining a proper cardiac regulation. A longstanding theory holds that biological organisms facing an ever-changing environment are likely to evolve adaptive mechanisms to extract essential features in order to adjust their behavior. The key question, however, has been to understand how the neural circuitry self-organizes these feature detectors to select behaviorally relevant information. Previous studies in computational perception suggest that a neural population enhances information that is important for survival by minimizing the statistical redundancy of the stimuli. Herein we investigate whether the cardiac system makes use of a redundancy reduction strategy to regulate the cardiac rhythm. Based on a network of neural filters optimized to code heartbeat intervals, we learn a population code that maximizes the information across the neural ensemble. The emerging population code displays filter tuning proprieties whose characteristics explain diverse aspects of the autonomic cardiac regulation, such as the compromise between fast and slow cardiac responses. We show that the filters yield responses that are quantitatively similar to observed heart rate responses during direct sympathetic or parasympathetic nerve stimulation. Our findings suggest that the heart decodes autonomic stimuli according to information theory principles analogous to how perceptual cues are encoded by sensory systems.

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Bias test.(A) Partial representation of a population code composed of (decoding) waveforms learned from ICA using a sparse dataset results in bandpass-like filters. The dataset was drawn from a sub-band modulated signal ensemble constrained to have a 0.05 Hz bandwidth. (B) The center frequency Vs. bandwidth pattern derived from the population code composed of 128 filters illustrates that the learned decoding population have bandwidth centered around 0.05 Hz. (C) Joint time and frequency plane of the decoding population (see Fig. 2 for details).
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pone-0020227-g004: Bias test.(A) Partial representation of a population code composed of (decoding) waveforms learned from ICA using a sparse dataset results in bandpass-like filters. The dataset was drawn from a sub-band modulated signal ensemble constrained to have a 0.05 Hz bandwidth. (B) The center frequency Vs. bandwidth pattern derived from the population code composed of 128 filters illustrates that the learned decoding population have bandwidth centered around 0.05 Hz. (C) Joint time and frequency plane of the decoding population (see Fig. 2 for details).

Mentions: Still, can the decoding filters learned by ICA be a result of misestimating the modulatory frequency contributions underlying sympathetic and vagal activity? This is a difficult question to answer in general, but to evaluate the method we created a synthetic dataset composed of sparse structures drawn from a temporal series that was sub-band modulated by a set of bandpass filters. Each filter has the bandwidth constraint to 0.05 Hz, but displaced to cover a frequency range varying from 0.01 until 0.5 Hz. The decoding population emerging from this dataset using ICA have temporal structures that are similar to bandpass-filters (Fig. 4A). They have an average bandwidth centered around 0.05 Hz (Fig. 4B and 4C). This result supports the accuracy of the estimated decoding population, because the bandwidth of the predicted decoding population matches the design of the bank of filters.


Statistical coding and decoding of heartbeat intervals.

Lucena F, Barros AK, Príncipe JC, Ohnishi N - PLoS ONE (2011)

Bias test.(A) Partial representation of a population code composed of (decoding) waveforms learned from ICA using a sparse dataset results in bandpass-like filters. The dataset was drawn from a sub-band modulated signal ensemble constrained to have a 0.05 Hz bandwidth. (B) The center frequency Vs. bandwidth pattern derived from the population code composed of 128 filters illustrates that the learned decoding population have bandwidth centered around 0.05 Hz. (C) Joint time and frequency plane of the decoding population (see Fig. 2 for details).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3111410&req=5

pone-0020227-g004: Bias test.(A) Partial representation of a population code composed of (decoding) waveforms learned from ICA using a sparse dataset results in bandpass-like filters. The dataset was drawn from a sub-band modulated signal ensemble constrained to have a 0.05 Hz bandwidth. (B) The center frequency Vs. bandwidth pattern derived from the population code composed of 128 filters illustrates that the learned decoding population have bandwidth centered around 0.05 Hz. (C) Joint time and frequency plane of the decoding population (see Fig. 2 for details).
Mentions: Still, can the decoding filters learned by ICA be a result of misestimating the modulatory frequency contributions underlying sympathetic and vagal activity? This is a difficult question to answer in general, but to evaluate the method we created a synthetic dataset composed of sparse structures drawn from a temporal series that was sub-band modulated by a set of bandpass filters. Each filter has the bandwidth constraint to 0.05 Hz, but displaced to cover a frequency range varying from 0.01 until 0.5 Hz. The decoding population emerging from this dataset using ICA have temporal structures that are similar to bandpass-filters (Fig. 4A). They have an average bandwidth centered around 0.05 Hz (Fig. 4B and 4C). This result supports the accuracy of the estimated decoding population, because the bandwidth of the predicted decoding population matches the design of the bank of filters.

Bottom Line: Herein we investigate whether the cardiac system makes use of a redundancy reduction strategy to regulate the cardiac rhythm.We show that the filters yield responses that are quantitatively similar to observed heart rate responses during direct sympathetic or parasympathetic nerve stimulation.Our findings suggest that the heart decodes autonomic stimuli according to information theory principles analogous to how perceptual cues are encoded by sensory systems.

View Article: PubMed Central - PubMed

Affiliation: Biological Information Engineering Laboratory, Nagoya University, Nagoya, Aichi, Japan. lucena@ohnishi.nagoya-u.ac.jp

ABSTRACT
The heart integrates neuroregulatory messages into specific bands of frequency, such that the overall amplitude spectrum of the cardiac output reflects the variations of the autonomic nervous system. This modulatory mechanism seems to be well adjusted to the unpredictability of the cardiac demand, maintaining a proper cardiac regulation. A longstanding theory holds that biological organisms facing an ever-changing environment are likely to evolve adaptive mechanisms to extract essential features in order to adjust their behavior. The key question, however, has been to understand how the neural circuitry self-organizes these feature detectors to select behaviorally relevant information. Previous studies in computational perception suggest that a neural population enhances information that is important for survival by minimizing the statistical redundancy of the stimuli. Herein we investigate whether the cardiac system makes use of a redundancy reduction strategy to regulate the cardiac rhythm. Based on a network of neural filters optimized to code heartbeat intervals, we learn a population code that maximizes the information across the neural ensemble. The emerging population code displays filter tuning proprieties whose characteristics explain diverse aspects of the autonomic cardiac regulation, such as the compromise between fast and slow cardiac responses. We show that the filters yield responses that are quantitatively similar to observed heart rate responses during direct sympathetic or parasympathetic nerve stimulation. Our findings suggest that the heart decodes autonomic stimuli according to information theory principles analogous to how perceptual cues are encoded by sensory systems.

Show MeSH