Modeling biological refractory periods and synaptic depression in an artificial neuron. (5th February 2019)

• Record Type: Journal Article
• Authors: Hamel, Éric; Labib, Richard
• Is Part Of: Biomedical physics & engineering express. Volume 5:Number 2(2019)
• Publication Date: 2019-02-05
• Subjects: neural nets -- neural models -- pattern recognition -- refractory periods -- synaptic depression -- classification -- neuroscience; Medical physics -- Periodicals; Biophysics -- Periodicals; Biomedical engineering -- Periodicals; Medical sciences -- Periodicals; 610.153
• DOI: 10.1088/2057-1976/ab00a0
• Languages: English
• ISSNs: 2057-1976

Abstract

Abstract: In this paper, we increase the classifying potential of an artificial neuron, the quantron, by considering biologically inspired modifications. Synaptic depression, representing the decreasing phenomenon observed over time in an active neuron is modeled first, resulting in highly nonlinear and convex classification boundaries. Secondly, refractory periods during which receptors are partially inactive are incorporated into the model. Again, convex discriminant functions are obtained. Combining both of these improvements allows the quantron to generate even more complex patterns, resulting in a more versatile classifier.