# A Relational Theory of Computing by John G. Sanderson (auth.)

By John G. Sanderson (auth.)

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Extra resources for A Relational Theory of Computing

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7). This has the advantage that it is always possible to rerun the algorithm using the same seed for the random number generator, to obtain exactly the same results. Most of the global stochastic search methods share the same core structure. First, one or several initial points x0 are chosen in the solution space. Then a loop is started with an incrementing variable k, and at every step a number of random points yk are drawn from a probability distribution Fk . This distribution can (but doesn’t have to) depend on the previous points xk .

If the frequency is the same, the voltage di¤erence is easy to measure at every point in time. If not, it is not much harder to take into account only simultaneous points or to extrapolate additional points on the voltage trace with the lowest sampling. However, the inherent nonlinearity of the neuron responses makes such a ﬁtness function quite unreliable. 3, where the data are shown in solid lines and the model in dotted lines, most of us would prefer model a to model b and model b to model c because model b has a larger time shift than model a and model c does not spike at all.

Gear methods and their descendants, such as LSODE and CVODE, combine adaptative and implicit methods to solve sti¤ ODEs quickly and accurately. More description of numerical methods for simulating di¤erential equations can be found in Mascagni and Sherman (1998). Boundary-Value Problems Boundary-value problems arise in cases when we are looking for steady states of PDEs or, as earlier, when looking for traveling wave solutions to nerve axon equations. As we have noted, unlike initial-value problems, there is no guarantee of the existence of solutions.