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Sequential sparse autoencoder for dynamic heading representation in ventral intraparietal area.

Wei Gao1, Jiangrong Shen2, Yipeng Lin1

  • 1Department of Neurology and Psychiatry of the Second Affiliated Hospital, College of Biomedical Engineering and Instrument Science, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, 268 Kaixuan Road, Jianggan District, Hangzhou, 310029, China.

Computers in Biology and Medicine
|June 17, 2023
PubMed
Summary
This summary is machine-generated.

Predicting heading perception from brain signals is crucial for navigation. A novel sequential sparse autoencoder (SSAE) model accurately decodes heading information from neural population responses in the ventral intraparietal area (VIP).

Keywords:
HeadingNeural decodingNeural dynamicsSparse autoencoderVentral intraparietal areaVestibular

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Robotics and Navigation

Background:

  • Real-time heading prediction from neural signals is vital for spatial navigation.
  • The ventral intraparietal area (VIP) is a key brain region for processing vestibular and visual information for heading perception.
  • Current methods for decoding heading from VIP population responses are limited by complex spatiotemporal dynamics and neural heterogeneity.

Purpose of the Study:

  • To explore how heading perception is represented at the population level in the VIP.
  • To develop and validate a novel decoding method for VIP neural population activity.
  • To achieve high-accuracy, real-time heading prediction for navigation.

Main Methods:

  • Recorded neural responses from 210 VIP neurons in rhesus monkeys during a heading perception task.
  • Developed a sequential sparse autoencoder (SSAE) model utilizing unsupervised learning to extract spatiotemporal heading features.
  • Employed a softmax classifier for decoding headings from the extracted features.

Main Results:

  • The SSAE model achieved a leading decoding accuracy of 96.8% ± 2.1% for heading perception.
  • Demonstrated robustness, low storage, and computational efficiency suitable for real-time applications.
  • The model learned neurobiologically plausible features representing dynamic navigational information.

Conclusions:

  • The SSAE model offers a powerful and efficient approach for population-level decoding of heading perception from VIP neural activity.
  • This method addresses limitations of existing decoding techniques by effectively modeling neural dynamics.
  • The findings support the development of advanced brain-computer interfaces for navigation and robotic control.