Raymond Pavloski, Department of Psychology, presented results of recent research conducted with Charles Lamb, Department of Mathematics, in a paper titled “Simulations Support the Simple Hypothesis that Persistent Coupling of Electrochemical Activity in Recurrent Network Neurons is an Objective Signature of Visual Object Unity.” The presentation was made in a session on Sensory Processing at the 2017 International Joint Conference on Neural Networks held in Anchorage, Alaska.
This conference is organized by the International Neural Network Society in cooperation with the IEEE (Institute of Electrical and Electronics Engineers) Computational Intelligence Society. A paper coauthored by Pavloski and Lamb will soon be published in the Proceedings of the 2017 International Joint Conference on Neural Networks.
The work conducted by Pavloski and Lamb demonstrates that the interactions of neurons in a recurrent neural network create a persistent structure in response to images presented to a simulated retina, and that this structure mimics several important aspects of an experienced visual object. Results of this research have implications for understanding the nature of visual experience. An exciting potential application lies in improving prosthetic vision, which currently provides only a very rudimentary visual experience.