Affiliation:
1. Baskent University Faculty of Medicine, Department of Psychiatry
2. Hacettepe University, Institute Of Neurological Sciences And Psychiatry
3. Ankara University, Computer Engineering Department
4. Ankara University, Department of Electrical-Electronics Engineering
Abstract
Abstract
Background: The Broadman Area 17 (V1) has a good representation of retinotopic map. Similarity between visual input and the representation of it in V1 would be affected from both an intrinsic noise and the saccadic eye movements. GABA’s role in increasing signal to noise ratio is known but, how GABAergic activity helps to control noise, based on input and saccades, has not been modelled.
Methods: A computational model of V1 was designed by using the MATLAB 2021a platform, and different six images, each containing a circle, triangle, and square, were used to test the model. The developed V1 was constituted of six different orientation columns (OCs). Each OC contains GABAergic and glutamatergic connections. Thus, OCs were activated not only based on afferent image inputs but also on the interaction among fired columns via the sum of glutamate and GABAergic neuron weights. V1 representation states for twelve, twenty and thirty saccades were summed and visualized. Finally, the original and representational forms of the image were compared. In the model, GABA activity levels have been tuned and the results of each level analysed.
Results: It has been shown that level of GABA activity in the orientation columns during saccades is a critical factor for an ideal image representation. Decreased levels of GABA activity can be associated with inadequacy of noise elimination which could impair correct contour perception.
Conclusion: Orientation columns can be conceptualized as microprocessors of V1. In this region, images are represented with high similarity. This similarity seems to need efficient GABAergic activity.
Publisher
Research Square Platform LLC
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