  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.
