A computational model of familiarity detection for natural pictures, abstract images, and random patterns: Combination of deep learning and anti-Hebbian training. (November 2021)

• Record Type: Journal Article
• Authors: Kazanovich, Yakov; Borisyuk, Roman
• Is Part Of: Neural networks. Volume 143(2021)
• Page Start: 628
• Page End: 637
• Publication Date: 2021-11
• Subjects: Recognition memory -- Familiarity recognition -- Deep learning -- Anti-Hebbian rule -- Memorization; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2021.07.022
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: We present a neural network model for familiarity recognition of different types of images in the perirhinal cortex ( the FaRe model ). The model is designed as a two-stage system. At the first stage, the parameters of an image are extracted by a pretrained deep learning convolutional neural network. At the second stage, a two-layer feed forward neural network with anti-Hebbian learning is used to make the decision about the familiarity of the image. FaRe model simulations demonstrate high capacity of familiarity recognition memory for natural pictures and low capacity for both abstract images and random patterns. These findings are in agreement with psychological experiments.