Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Gundavarapu, Anila, Chakravarthy, V. Srinivasa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
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
_version_ 1785053579013259264
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
work_keys_str_mv AT gundavarapuanila modelingthedevelopmentofcorticalresponsesinprimatedorsalwherepathwaytoopticflowusinghierarchicalneuralfieldmodels
AT chakravarthyvsrinivasa modelingthedevelopmentofcorticalresponsesinprimatedorsalwherepathwaytoopticflowusinghierarchicalneuralfieldmodels