SEE theory explains how neural interactions give rise to the objective and subjective phenomena of cortical prosthetic vision.
Interactions mediated by two systems of synapses underlie the visual geometry and lightness interval distribution patterns.
How the visual geometry and lightness interval distribution patterns emerge:
Simulations of a small-scale neural network consisting of spiking integrate and fire neurons that interact via conductance-based synapses and spanning 25 columns of visual cortex behave in accordance with SEE theory.
Raymond P. Pavloski received the B.S. degree in psychology with highest distinction from the Pennsylvania State University, State College, in 1973 and the Ph.D. degree in psychology from McMaster University, Hamilton, Ontario, Canada in 1979. He conducted psychophysiology research supported by a National Science Foundation Summer Fellowship in 1972 and was an Ontario Mental Health Foundation Post-Doctoral Research Fellow in 1979-1980. From 1980-1984 he served on the Special Professional Staff in the Department of Medicine at St. Joseph’s Hospital, Hamilton, and was a Registered Psychologist in Ontario from 1981-1984. He joined the faculty at Indiana University of Pennsylvania in 1984 and retired in 2018 after serving as Professor, Assistant Chair and Chair of the Psychology Department and as Dean’s Associate in the College of Natural Sciences and Mathematics. He received the Academic Excellence and Innovation Award in 2008 and the Distinguished Faculty Award for Research in 2012 and was named Professor Emeritus in 2019. His research and publications over the past 20 years have focused on relationships between interactions in neural networks and perception.
This patent pending subject matter and technology, titled “Sense Element Engagement Process of Cortical Prosthetic Vision by Neural Networks,” relates to cortical prosthetic vision processes and, specifically, to production of cortical prosthetic vision by neural networks via a sense element engagement process. Covered by: US Patent Application No. 18/063,232; PCT International Patent Application No. PCT/US22/81153.
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