{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["3(3)"],"submitter":["Abedi Khoozani P"],"pubmed_abstract":["Allocentric (landmark-centered) and egocentric (eye-centered) visual codes are fundamental for spatial cognition, navigation, and goal-directed movement. Neuroimaging and neurophysiology suggest these codes are initially segregated, but then reintegrated in frontal cortex for movement control. We created and validated a theoretical framework for this process using physiologically constrained inputs and outputs. To implement a general framework, we integrated a convolutional neural network (CNN) of the visual system with a multilayer perceptron (MLP) model of the sensorimotor transformation. The network was trained on a task where a landmark shifted relative to the saccade target. These visual parameters were input to the CNN, the CNN output and initial gaze position to the MLP, and a decod"],"journal":["Cerebral cortex communications"],"pagination":["tgac026"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9334293"],"repository":["biostudies-literature"],"pubmed_title":["Integration of allocentric and egocentric visual information in a convolutional/multilayer perceptron network model of goal-directed gaze shifts."],"pmcid":["PMC9334293"],"pubmed_authors":["Wildes RP","Crawford JD","Bharmauria V","Abedi Khoozani P","Schutz A"],"additional_accession":[]},"is_claimable":false,"name":"Integration of allocentric and egocentric visual information in a convolutional/multilayer perceptron network model of goal-directed gaze shifts.","description":"Allocentric (landmark-centered) and egocentric (eye-centered) visual codes are fundamental for spatial cognition, navigation, and goal-directed movement. Neuroimaging and neurophysiology suggest these codes are initially segregated, but then reintegrated in frontal cortex for movement control. We created and validated a theoretical framework for this process using physiologically constrained inputs and outputs. To implement a general framework, we integrated a convolutional neural network (CNN) of the visual system with a multilayer perceptron (MLP) model of the sensorimotor transformation. The network was trained on a task where a landmark shifted relative to the saccade target. These visual parameters were input to the CNN, the CNN output and initial gaze position to the MLP, and a decod","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-26T16:17:35.675Z","creation":"2025-04-06T15:08:08.104Z"},"accession":"S-EPMC9334293","cross_references":{"pubmed":["35909704"],"doi":["10.1093/texcom/tgac026"]}}