Simulating alphabet recitation under thalamic lesions
DOI:
https://doi.org/10.36505/ExLing-2019/10/0039/000401Keywords:
spiking neural network, language, lesionsAbstract
We utilize the Semantic Pointer Architecture, a neurocognitive architecture in order to model language impairments. Constructed is a spiking neural network to investigate the effect of neural deficits in the basal ganglia and thalamus on the retrieval of an ordered sequence of unique symbols. The model includes four subnetworks: associative memory, working memory, basal ganglia and thalamus. A lesion is simulated by reducing the number of available neurons in the thalamus and attenuating its input from the basal ganglia. The model remains mostly successful in the ordered retrieval of the alphabet but ‘stutters’: working memory ‘forgets’ the current letter and ‘steps back’ several letters before continuing correctly.
References
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Stewart, T.C., Tripp, B., Eliasmith, C. 2009. Python scripting in the Nengo simulator. Frontiers in Neuroinformatics, 3, 7.
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Articles are published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
