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A Computational Framework for Understanding the Impact of Prior Experiences on Pain Perception and Neuropathic Pain
Fig 5
Results from hierarchical Kalman filter simulations of how the value of 
(solid blue lines in upper plots) at the time of a nerve injury and the level of sensory disruption (i.e., value of R, dash-dotted purple line in upper plot) may play a role in the characteristics of subsequent neuropathic pain.
Simulations were run for three different initial values of 
. The top panels show the inferred value of 
over time, and the bottom panels show the corresponding level of perceived pain, 
. For R = 0.82 (left), the perceived pain (solid red lines in the lower plots) is strongly influenced by sensory input and remains close to 0. For R = 82 (second from the left), 
regardless of the initial value, and the perceived pain is tonically at an intermediate intensity with little variance. For R = 8002 (right), the value of 
is no longer at all influenced by sensory input and reduces to a random walk centered at the initial value 
and variance equal to the noise in the internal model. In this scenario, if 
the pain is likely to stay tonically high, and similarly if 
the level of pain is likely to drop to stay at 0. If 
, the perceived level of pain may vary widely as the value of 
fluctuates above and below 0. For R = 802 (second from the right) the model displays a mixture of the behavior described for R = 82 and R = 8002. The shaded areas indicate the interquartile ranges.
doi: https://doi.org/10.1371/journal.pcbi.1012097.g005