The computational modelling of the spinal cord neurons involved in the pain process

  • Karen Prince

Student thesis: Doctoral Thesis


Pain is a personal subjective experience with physiological and psychological components and involves many complex processes. In 1965 Melzack and Wall proposed the influential gate control theory (GCT) of pain and, in general, this has been supported by subsequent research. This theory postulates that cells in the substantia gelatinosa, located within the spinal cord, act like a gate mechanism that modulates the flow of information through the spinal cord to the brain and thus impacts on the pain experience. The abundance of literature and experimental data that is available from pain research supports the development and testing of computational models for the simulation and exploration of the pain process. Despite the fact that pain is an ideal candidate for modeling, it is an area that has received little attention. One of the few published models (Britton and Skevington, 1989; Britton et al., 1996) translated the explicitness of the GCT and its well-defined architecture into a basic mathematical model. The aim of this research is to develop a biologically appropriate computational model of pain, capable of modelling both acute and chronic pain states, and describe applications and simulations appropriate to such a model. Therefore this research firstly replicates a mathematical model of pain (Britton and Skevington, 1989; Britton et al., 1996) to explore its adequacy and to assess its potential for further development. The original model is then developed and extended to produce a more biologically plausible representation of the pain processes involved in the Gate Control mechanism. The improvements in the computational model have enabled a clinically plausible simulation of a pain modulatory technique, transcutaneous electrical nerve stimulation (TENS), which validates the model’s representation of the GCT and provides insight into how pain modulation can occur. Other developments to this model show its unique ability to represent symptoms of chronic pain, such as allodynia and hyperalgesia, which are associated with pathological pain states developed through the loss of inhibition and glial cell activation
Date of Award2006
Original languageEnglish
Awarding Institution
  • University of Northampton
SupervisorScott Turner (Supervisor) & Philip Picton (Supervisor)

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