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Modeling the development of cortical responses in primate dorsal (“where”) pathway to optic flow using hierarchical neural field models
Although there is a plethora of modeling literature dedicated to the object recognition processes of the ventral (“what”) pathway of primate visual systems, modeling studies on the motion-sensitive regions like the Medial superior temporal area (MST) of the dorsal (“where”) pathway are relatively sc...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10239834/ https://www.ncbi.nlm.nih.gov/pubmed/37284658 http://dx.doi.org/10.3389/fnins.2023.1154252 |
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author | Gundavarapu, Anila Chakravarthy, V. Srinivasa |
author_facet | Gundavarapu, Anila Chakravarthy, V. Srinivasa |
author_sort | Gundavarapu, Anila |
collection | PubMed |
description | Although there is a plethora of modeling literature dedicated to the object recognition processes of the ventral (“what”) pathway of primate visual systems, modeling studies on the motion-sensitive regions like the Medial superior temporal area (MST) of the dorsal (“where”) pathway are relatively scarce. Neurons in the MST area of the macaque monkey respond selectively to different types of optic flow sequences such as radial and rotational flows. We present three models that are designed to simulate the computation of optic flow performed by the MST neurons. Model-1 and model-2 each composed of three stages: Direction Selective Mosaic Network (DSMN), Cell Plane Network (CPNW) or the Hebbian Network (HBNW), and the Optic flow network (OF). The three stages roughly correspond to V1-MT-MST areas, respectively, in the primate motion pathway. Both these models are trained stage by stage using a biologically plausible variation of Hebbian rule. The simulation results show that, neurons in model-1 and model-2 (that are trained on translational, radial, and rotational sequences) develop responses that could account for MSTd cell properties found neurobiologically. On the other hand, model-3 consists of the Velocity Selective Mosaic Network (VSMN) followed by a convolutional neural network (CNN) which is trained on radial and rotational sequences using a supervised backpropagation algorithm. The quantitative comparison of response similarity matrices (RSMs), made out of convolution layer and last hidden layer responses, show that model-3 neuron responses are consistent with the idea of functional hierarchy in the macaque motion pathway. These results also suggest that the deep learning models could offer a computationally elegant and biologically plausible solution to simulate the development of cortical responses of the primate motion pathway. |
format | Online Article Text |
id | pubmed-10239834 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102398342023-06-06 Modeling the development of cortical responses in primate dorsal (“where”) pathway to optic flow using hierarchical neural field models Gundavarapu, Anila Chakravarthy, V. Srinivasa Front Neurosci Neuroscience Although there is a plethora of modeling literature dedicated to the object recognition processes of the ventral (“what”) pathway of primate visual systems, modeling studies on the motion-sensitive regions like the Medial superior temporal area (MST) of the dorsal (“where”) pathway are relatively scarce. Neurons in the MST area of the macaque monkey respond selectively to different types of optic flow sequences such as radial and rotational flows. We present three models that are designed to simulate the computation of optic flow performed by the MST neurons. Model-1 and model-2 each composed of three stages: Direction Selective Mosaic Network (DSMN), Cell Plane Network (CPNW) or the Hebbian Network (HBNW), and the Optic flow network (OF). The three stages roughly correspond to V1-MT-MST areas, respectively, in the primate motion pathway. Both these models are trained stage by stage using a biologically plausible variation of Hebbian rule. The simulation results show that, neurons in model-1 and model-2 (that are trained on translational, radial, and rotational sequences) develop responses that could account for MSTd cell properties found neurobiologically. On the other hand, model-3 consists of the Velocity Selective Mosaic Network (VSMN) followed by a convolutional neural network (CNN) which is trained on radial and rotational sequences using a supervised backpropagation algorithm. The quantitative comparison of response similarity matrices (RSMs), made out of convolution layer and last hidden layer responses, show that model-3 neuron responses are consistent with the idea of functional hierarchy in the macaque motion pathway. These results also suggest that the deep learning models could offer a computationally elegant and biologically plausible solution to simulate the development of cortical responses of the primate motion pathway. Frontiers Media S.A. 2023-05-22 /pmc/articles/PMC10239834/ /pubmed/37284658 http://dx.doi.org/10.3389/fnins.2023.1154252 Text en Copyright © 2023 Gundavarapu and Chakravarthy. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Gundavarapu, Anila Chakravarthy, V. Srinivasa Modeling the development of cortical responses in primate dorsal (“where”) pathway to optic flow using hierarchical neural field models |
title | Modeling the development of cortical responses in primate dorsal (“where”) pathway to optic flow using hierarchical neural field models |
title_full | Modeling the development of cortical responses in primate dorsal (“where”) pathway to optic flow using hierarchical neural field models |
title_fullStr | Modeling the development of cortical responses in primate dorsal (“where”) pathway to optic flow using hierarchical neural field models |
title_full_unstemmed | Modeling the development of cortical responses in primate dorsal (“where”) pathway to optic flow using hierarchical neural field models |
title_short | Modeling the development of cortical responses in primate dorsal (“where”) pathway to optic flow using hierarchical neural field models |
title_sort | modeling the development of cortical responses in primate dorsal (“where”) pathway to optic flow using hierarchical neural field models |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10239834/ https://www.ncbi.nlm.nih.gov/pubmed/37284658 http://dx.doi.org/10.3389/fnins.2023.1154252 |
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