Related Experiment Video
Updated: Aug 15, 2026
10:27
Training Synesthetic Letter-color Associations by Reading in Color
Published on: February 21, 2014
A neural network model of lateralization during letter identification
1Department of Computer Science, A.V. Williams Building, University of Maryland, College Park, MD 20742, USA.
Journal of Cognitive Neuroscience
|April 10, 1999
Summary
This study developed a neural network model to explore cerebral lateralization. The model showed that hemisphere asymmetries in size, excitability, or learning rate can cause functional lateralization, with the corpus callosum playing an inhibitory role.
Area of Science:
- Computational neuroscience
- Cognitive neuroscience
- Neuroscience
Background:
- Cerebral lateralization of cognitive functions remains poorly understood.
- Investigating the mechanisms of brain hemisphere specialization is crucial for understanding brain function.
Purpose of the Study:
- To develop a bihemispheric neural network model to investigate the emergence of functional cerebral lateralization.
- To explore how asymmetries in hemisphere parameters influence visual identification task performance.
Main Methods:
- Developed a bihemispheric neural network model with two parallel processing paths.
- Utilized unsupervised (Hebbian) and supervised (Widrow-Hoff) learning rules for training.
- Simulated interactions between hemispheres via the corpus callosum.
Main Results:
- Model demonstrated that asymmetries in hemisphere size, excitability, or learning rate can lead to lateralization.
- Stronger inhibitory connections in the corpus callosum intensified lateralization.
- Lateralization favored hemispheres with higher excitability and learning rates.
Conclusions:
- Multiple asymmetries contribute to the emergence of cerebral lateralization.
- The corpus callosum appears to play a functionally inhibitory role in lateralization.
- Computational models are valuable tools for understanding the mechanisms of brain lateralization.
