The Gate Theory of Pain Revisited: Modeling Different Pain Conditions with a Parsimonious Neurocomputational Model

Figure 3

Evolution of gate circuit parameters when considering both intrinsic and synaptic plasticity. Five program simulations (5 thin colored lines) are depicted, starting with different initial weights and shifts. (a) Evolution of weights: each coordinate (, , ) represents the set of synaptic weights in each iteration, with the color of the point representing the value of weight . Along iterations, all lines converge to the same coordinate (, , , ) = (1, 0, 0.5, 0.5). (b) After 5000 iterations, the shift parameters of the activation function of the SG neuron () and of the T neuron () also converge to a certain point (0.5, 0.27). (c) With the final set of weights and shift parameters, the probability of a CT neuron’s firing is given by the table being the mechanoreceptor input probability and the nociceptive input probability.

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