Similar perturbation techniques are commonly used in computational physiology to simulate stimulation of excitable cells [ 63, 64].
For each learning rate and inverse temperature value tested, we ran the model with and without simulated depolarizing stimulation to determine how the stimulation affected the learning rate and inverse temperature estimated from the model's behaviour.
The model reproduces the observed Ipc oscillatory bursting in response to simulated visual stimulation.
However, simulated depolarizing stimulation increases the relative influence of pre-stimulus bias on choices and thereby increases choice stochasticity (t(24) = −5.59, p < 0.05, r = 0.75).
Pairwise comparisons revealed that simulated depolarizing stimulation increased the mean baseline firing rate of pyramidal cells (from 5.81 Hz to 7.71 Hz), but also that of interneurons (0.75 Hz to 1.21 Hz).
Similarly, simulated hyperpolarizing stimulation (which hyperpolarized the pyramidal populations and depolarized the interneuron population) decreased firing rates in all populations (to 4.14 Hz for pyramidal cells and, 0.39 Hz for interneurons; all changes in firing rates Z > 3.5, p < 0.01, r > 0.70).
As a result, there is no shortage of marketers trying to simulate the stimulation of a stimulus package.
I will have a closer look at this. simulating the stimulation with extracellular electrodes, response in the presence of an extracellular potential boundary condition computed by some external program, leaky patch clamps, incomplete seals in the myelin sheath along with current flow in the space between the myelin and the axon.
The conventional and modified cable equations are used to simulate magnetic stimulation of long peripheral nerves by circular and figure-8 coils.
Then, we simulate persistent stimulation by turning on the reaction, IKKn → IKKa with a rate TR*K1 and a non-zero constant value of TR.
