Towards associative memory in convolutional neural networks for in silico neurodegenerative diseases

COEX 

Convolutional neural networks (CNNs) have emerged as a popular choice of deep learning architecture for modeling visual processing, as their hierarchical structure and flow of information processing closely resembles the human ventral stream [LeCun, Y. (1989)]. While CNNs have been used to model healthy visual cognition, there remain limitations in biologically plausible in silico modeling of cognitive decline in neurodegenerative diseases, such as Alzheimer's (AD). Previously, we developed methods to simulate neurodegeneration of the visual system through iterative synaptic injury in CNNs [Tuladhar, A. (2021), Moore, J. (2023)]. However, the limitation of CNNs lies in the lack of biologically meaningful learning mechanisms that are similar to cognitive functions, such as memory. These mechanisms are essential for accurately capturing the neuropathogenesis. For instance, the deposition of beta-amyloid peptide and neurofibrillary tangles of tau polymers in the hippocampus leads to cognitive decline in memory tasks among AD patients.

Building on our prior work, in this study, we equipped a CNN with associative memory to enhance biological plausibility, combining two critical cognitive functions of the brain: visual processing of the ventral stream and associative memory of the hippocampus. The model demonstrates intriguing and beneficial properties, including (1) robustness to noisy or occluded image queries and (2) interpretable and sparse representations in network weights. We argue that this model is an improved in silico framework for a healthy brain, as well as the cognitive profiles of AD progression.