Figure 5 shows this spline function that fits the EEJ magnetic data.
The effect of some synapses can be described with a function that fits the shape of the postsynaptic response due to the arrival of an action potential at the presynaptic release site.
To include inhibition effects, we apply an inhibition function that fits the generalized mass action rate law [ 5, 73]: (2) v j (S, p ) = F j (S, p ) (k + j ∏ i S i n i j − − k − j ∏ i S i n i j + ).
One thing we could change in the future is to make functions that fit use cases, and then allow users to select the data source as a parameter in the function.
We derive now a computationally efficient characterization of polynomial functions that fit a time series of a given length.
We find such function that fits the data in the least-squares sense, for each of the network properties.
The sigmoid regression function that fits these curves is given by the equation: = a 1 + e x – x o b, where ' a' is the slope of the curve.
Since there is an infinite set of non-linear prediction functions, f(x), that fit the output data, we need to constrain the solution.
We propose a method to smooth out the discontinuities that are detected along a trajectory, based on the definition of a continuous function that fits locally the computed potential, and is used to integrate the trajectory across the discontinuity.
