Exploring and explaining properties of motion processing in biological brains using a neural network.
Reuben Rideaux1,2, Andrew E Welchman1,3
1Department of Psychology, University of Cambridge, Cambridge, UK.
Journal of Vision
|February 24, 2021
Summary
This study trains a neural network to understand visual motion perception. The model mimics biological brain processing, explaining phenomena like reverse-phi motion and human underestimation of low-coherence stimuli.
Area of Science:
- Computational Neuroscience
- Visual Perception
- Artificial Intelligence
Background:
- Visual motion perception is crucial for behaviors like navigation and grasping.
- Understanding neural mechanisms of motion processing is limited by biological system access.
Purpose of the Study:
- To explore visual motion perception using a trained neural network.
- To elucidate the neural basis of motion (mis)perception phenomena.
Main Methods:
- Trained a neural network to estimate velocity from image sequences.
- Analyzed network layers (V1, MT) and their spatiotemporal tuning properties.
- Investigated network responses to reverse-phi and low-coherence motion stimuli.
Main Results:
- The network accurately models biological responses to reverse-phi motion direction.
- It replicates over/underestimation of slow/fast reverse-phi motion speeds.
- Network V1 and MT layers show tuning properties similar to biological systems.
- Slow-speed MT units receive input from high spatial/low temporal frequency V1 units.
- Positive correlation found between pattern-motion and speed selectivity in MT units.
- Network captures human underestimation of low-coherence motion due to noise/signal pooling.
Conclusions:
- The neural network provides biologically plausible explanations for visual motion perception phenomena.
- Findings offer testable predictions for future psychophysical and neurophysiological research.
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