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A comprehensive computational model of animal biosonar signal processing.
Chen Ming1, Stephanie Haro2, Andrea Megela Simmons3
1Department of Neuroscience and Carney Institute for Brain Science, Brown University Providence, United States of America.
Plos Computational Biology
|February 17, 2021
Summary
Computational models simulate animal biosonar, like bats and dolphins, for target tracking. The Spectrogram Correlation and Transformation (SCAT) model processes echoes using auditory mechanisms to reconstruct object distance and shape, effectively rejecting clutter.
Area of Science:
- Bioacoustics
- Computational Neuroscience
- Animal Behavior
Background:
- Echolocating animals exhibit superior real-time target tracking and clutter rejection capabilities.
- Understanding the computational mechanisms behind animal biosonar is crucial for replicating these abilities.
Purpose of the Study:
- To develop and present the Spectrogram Correlation and Transformation (SCAT) model.
- To replicate key aspects of biosonar imaging in bats and dolphins using auditory mechanisms.
Main Methods:
- Acquiring broadband biosonar broadcasts and echoes.
- Representing signals as time-frequency spectrograms via parallel bandpass filters.
- Estimating echo range delay using threshold-crossing times and amplitude-latency trading.
Main Results:
- The SCAT model processes time-frequency spectrograms to estimate echo delay.
- Target shape is extracted from echo spectrum nulls and merged with range delay estimates.
- Image blurring from clutter echoes acts as an anticorrelation process for clutter rejection.
Conclusions:
- The SCAT model successfully replicates biosonar imaging principles.
- Amplitude-latency trading is a key physiological feature for accurate echo processing.
- The model demonstrates effective clutter rejection through a blurring mechanism.
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