Associative Memory Impairments arising from Neurodegenerative Diseases and Traumatic Brain Injuries in a Hopfield Network Model

TL;DR

This paper extends a Hopfield network model to simulate how focal axonal swellings from neurodegenerative diseases and TBI impair associative memory, linking biological injury data to functional deficits in memory recall.

Contribution

It introduces a biophysically calibrated model incorporating FAS effects into associative memory networks, bridging experimental injury data with cognitive dysfunction simulation.

Findings

Memory recall decreases with FAS severity.

Model accurately predicts memory impairment stages.

Framework suggests potential diagnostic markers.

Abstract

Neurodegenerative diseases and traumatic brain injuries (TBI) are among the main causes of cognitive dysfunction in humans. Both manifestations exhibit the extensive presence of focal axonal swellings (FAS). FAS compromises the information encoded in spike trains, thus leading to potentially severe functional deficits. Complicating our understanding of the impact of FAS is our inability to access small scale injuries with non-invasive methods, the overall complexity of neuronal pathologies, and our limited knowledge of how networks process biological signals. Building on Hopfield's pioneering work, we extend a model for associative memory to account for FAS and its impact on memory encoding. We calibrate all FAS parameters from biophysical observations of their statistical distribution and size, providing a framework to simulate the effects of brain disorders on memory recall performance. A face recognition example is used to demonstrate and validate the functionality of the novel model. Our results link memory recall ability to observed FAS statistics, allowing for a description of different stages of brain disorders within neuronal networks. This provides a first theoretical model to bridge experimental observations of FAS in neurodegeneration and TBI with compromised memory recall, thus closing the large gap between theory and experiment on how biological signals are processed in damaged, high-dimensional functional networks. The work further lends new insight into positing diagnostic tools to measure cognitive deficits.