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Neural network models of the tactile system develop first-order units with spatially complex receptive fields
Charlie W Zhao1,2, Mark J Daley1,3,4,5,6, J Andrew Pruszynski1,6,7,8,9,10
1Dept. of Computer Science, Western University, London, Ontario, Canada.
Plos One
|June 15, 2018
Summary
Machine learning reveals that complex receptive fields in tactile neurons are common and beneficial. This complexity enhances neural network performance, particularly in noisy conditions and challenging tasks.
Area of Science:
- Neuroscience
- Computational Biology
- Machine Learning
Background:
- First-order tactile neurons possess spatially complex receptive fields.
- The functional significance of this complexity is not fully understood.
Purpose of the Study:
- To investigate the emergence and functional benefits of spatially complex receptive fields in tactile neurons using computational models.
- To determine if this complexity is a normative outcome under biological constraints.
Main Methods:
- Application of machine learning algorithms to analyze neural network architectures.
- Simulation of various training sets to model receptive field development.
- Evaluation of network performance under different task difficulties and noise levels.
Main Results:
- Spatially complex receptive fields arise across diverse training sets and network architectures.
- This complexity significantly improves network performance on complex tasks.
- Performance benefits are particularly pronounced in the presence of sensory noise.
Conclusions:
- Spatially complex receptive fields are a common and advantageous feature in tactile sensory processing.
- The findings support the hypothesis that such complexity is normatively beneficial within the constraints of the biological tactile system.
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