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Measuring Cardiac Autonomic Nervous System ANS Activity in Children

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Published on: April 29, 2013

Autonomous Development of Active Binocular and Motion Vision Through Active Efficient Coding.

Alexander Lelais¹, Jonas Mahn¹, Vikram Narayan¹

¹Frankfurt Institute for Advanced Studies, Frankfurt, Germany.

Frontiers in Neurorobotics

|August 6, 2019

Summary

This summary is machine-generated.

This study introduces a novel model for robots to learn active vision, enabling autonomous eye movements for efficient 3D object tracking. The system optimizes visual coding, mimicking mammalian visual cortex properties.

Keywords:

active perception autonomous learning binocular vision efficient coding intrinsic motivation optokinetic nystagmus smooth pursuit

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Area of Science:

Computational Neuroscience
Robotics
Computer Vision

Background:

Classic efficient coding theories focus on passive perception.
Active perception requires integrating sensory processing with motor control.
Existing models often address binocular or motion vision separately.

Purpose of the Study:

To develop a unified model for autonomous, simultaneous learning of active binocular and motion vision.
To investigate the Active Efficient Coding (AEC) framework for active perception.
To explore how intrinsic motivation for coding efficiency drives visual learning and eye movements.

Main Methods:

Utilized the Active Efficient Coding (AEC) framework with sparse coding for 3D object visual signals.
Implemented a model for autonomous learning of eye movements to enhance sensory coding efficiency.
Tested the model on the iCub humanoid robot in simulations.

Main Results:

Demonstrated self-calibration for accurate object fixation and tracking of moving objects.
Showcased continuous improvement until physical system constraints were met.
Observed emerging sensory tuning properties aligning with mammalian visual cortex findings (disparity, motion, motion-in-depth).

Conclusions:

Vergence and tracking eye movements share the objective of maximizing visual system coding efficiency.
AEC enables joint learning and calibration of eye movements.
The model provides insights into the neural basis of active vision and sensorimotor integration.