Computational modelling of episodic memory in the context of Post-traumatic Stress Disorder

In humans, episodic memory describes the ability to recall events one has experienced in the past. Computational models of episodic memory have been proposed, in which artificial neurons, arranged in biologically plausible networks, communicate in ways conjectured to correspond to how the biological brain stores or retrieves such memories. Briefly, it is widely believed that patterns of neural activity in the brain's neocortex, initiated by sensory stimuli, reshape synaptic connections between neurons in the hippocampus. This reshaping is thought to occur in a way that later enables the collective activity of the hippocampal neurons to re-instantiate the pattern of neocortical activity. In computational models, this population activity can be represented as a point in a high-dimensional vector space; during the recall of a specific memory, the hippocampal activity may be thought of traversing a low-dimensional subregion of that space - called a `continuous attractor'.

Investigating plausible computational models of cognitive processes may be a means of validating or formulating hypotheses about the neural underpinnings of mental disorders, which, in many cases, are insufficiently understood. For instance, this approach has notably been chosen in the context of memory to investigate disruptions of working (or short-term) memory in Schizophrenia. Inspired by these research efforts, we propose to formulate and study a computational model of episodic memory to gain theoretical insights into the neural mechanisms underlying Post-traumatic Stress Disorder (PTSD).

Sparked by traumatic, high-impact life events, PTSD has a twofold effect on episodic memory. On the one hand, the traumatic episode may be re-experienced as nightmares or intrusive memories. On the other hand, PTSD patients have commonly reported deficits in episodic memory unrelated to the episode. Although these effects have been observed across many studies, their neural correlates are not precisely...