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A Mathematical Theory of Cortex-Receptor Artificial Extension
1Department of Computer Science and Technology, Nanjing University, Nanjing, 210023, China. youluxing@sina.com.
Scientific Reports
|January 23, 2020
Summary
This study introduces a computational model for extending sensory perception by adding new pathways to artificial neural networks. The model effectively integrates novel sensory information without disrupting existing functions.
Area of Science:
- Computational Neuroscience
- Artificial Intelligence
- Sensory Systems
Background:
- Physiology experiments show artificial extension of cortex and receptor systems can grant new perceptual capabilities.
- Integrating novel sensory information into existing neural networks presents a significant challenge.
Purpose of the Study:
- To propose and examine a computational model for the online addition of new sensory pathways to artificial neural networks.
- To investigate the ability of artificial neural networks to adapt to and process novel perceptual information.
Main Methods:
- Development of a synapse-expandable artificial neuron model capable of growing new synapses.
- Integration of novel sensory pathways directly into the computational model in an online manner.
- Testing the model's capacity to absorb and process new perceptual data.
Main Results:
- The computational model successfully integrated suddenly emerged sensory channels.
- Neural circuits within the model demonstrated reusability for novel modalities.
- Original modality processing remained unaffected by the integration of new sensory channels.
Conclusions:
- The proposed computational model offers an effective method for extending perceptual capabilities in artificial systems.
- The synapse-expandable artificial neuron is a viable component for adaptive neural network architectures.
- This approach facilitates the development of more flexible and adaptable artificial sensory systems.
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