Milkowski sketches a mechanistic theory of implementation of computation against a background of extant conceptions, describing four dissimilar computational models of cognition.
Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits.
There is an increasing need and implementation of computation in the conduct of modern scientific research and experimentation.
This version of the SNIP is for the first time modeled as a capacitated network reliability problem allowing for the implementation of computation and solution techniques previously unavailable.
His research focusses on the strategic implementation of computation in architectural design.
Based on an efficient addition chain, double multiplication instead of single multiplication is used for implementation of inversion computations.
MapReduce [3, 4] is a programming model for implementation of distributed computations of high volume or big data and an execution framework for large-scale data processing on clusters of servers.
In particular, a straight-forward implementation of the computations of the determinants in steps (3)–(4), via e.g. the LU factorization (with partial pivoting), renders a total cost of 2m p 4 / 3 flops, which results from having to compute mp factorizations of p × p matrices, with a cost of 2p 3 / 3 flops per LU factorization.
Inference on the relative risks of interest is usually obtained through the implementation of Bayesian computations that output the posterior distribution of the relative risks in each area.
In order to clarify the analysis, the approach distinguishes three independent levels: the computational theory itself, the algorithms employed, and the underlying implementation of the computation, in this case through the human neural mechanisms.
