To explore multiple mechanisms of cognitive slowing and memory loss using computational modeling of cortex to link neuronal activity with cognitive content.

Both cognitive slowing and memory loss accompany normal aging, yet understanding is limited at the network and neuronal level. Relating the pathophysiological factors responsible for cognitive slowing, and interpreting its relationship to working memory, requires multiscale computer modeling. We modeled condition 1 of the Stroop recognition task - a task correlated with a degree of cognitive decline - to evaluate hypotheses about response slowing and memory loss.

We developed a simulation of Condition 1 of the Stroop recognition task using the Nengo system, a cognitive simulation environment with a semantic pointer architecture developed to model cognitive tasks using spiking neural networks (Bekolay et al., 2014). We explored the effects of several  factors on the time to recognition: Input noise, axonal loss, neuronal loss, memory duration, and feedback. 

Axonal loss and increased input noise produced profound slowing. High levels of neuronal loss severely impaired memory and paradoxically decreased slowing via the ability to respond more quickly by “releasing” a prior memory. Increased feedback improved memory at the cost of increased slowing. 

Our simulations suggest that significant slowing could be caused by white matter loss (axonal loss) or input signal degradation (which could be caused by visual or other afferent system worsening). As neuronal loss markedly decreased the duration of working memory, we propose that physiological feedback is increased to preserve working memory at the cost of further cognitive slowing. Pharmacologic feedback modulation could be one way to control both slowing and memory loss. 

Work Cited:

Bekolay, Bergstra, Hunsberger, DeWolf, Stewart, Rasmussen, Choo, Voelker & Eliasmith. (2014) Nengo: a Python tool for building large-scale functional brain models. Frontiers in Neuroinformatics 7.