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Machine Theory

Unconventional Computation and Natural Computation: 13th by Oscar H. Ibarra, Lila Kari, Steffen Kopecki

By Oscar H. Ibarra, Lila Kari, Steffen Kopecki

This booklet constitutes the refereed lawsuits of the thirteenth overseas convention on Unconventional Computation and usual Computation, UCNC 2014, held in London, ON, Canada, in July 2014. The 31 revised complete papers have been conscientiously reviewed and chosen from seventy nine submissions. The papers conceal a variety of themes together with between others molecular, quantum, optical and chaos computing in addition to neural computation, evolutionary computation, swarm intelligence and computational neuroscience.

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Extra resources for Unconventional Computation and Natural Computation: 13th International Conference, UCNC 2014, London, ON, Canada, July 14-18, 2014, Proceedings

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The results show that our perceptron is able to learn linear and nonlinear (quadratic) functions of two inputs. To the best of our knowledge, it is the first simulated chemical system capable of doing so. The small number of species and reactions and their simplicity allows for a mapping to an actual wet implementation using DNA-strand displacement or deoxyribozymes. Our results are an important step toward actual biochemical systems that can learn and adapt. Keywords: Chemical perceptron, analog perceptron, supervised learning, chemical computing, RNMSE, linear function, quadratic function.

Note that each configuration Ci+1 is uniquely determined by the configuration Ci , i ≥ 0. The result of a computation in Γ for an input vector (w1 , . . , for the initial configuration {(x, w0 (x)w1 $ . . $wm ) | x ∈ XH }, is the set of all strings (of the form v1 $ . . $vn , 6 A. Alhazov, R. Freund, and Y. , L(Γ )((w1 , . . , wm )) = {(v1 , . . , vn ) | vj ∈ T, 1 ≤ j ≤ n, v1 $ . . $vn ∈ Cs (i0 ), s ≥ 0)}. Remark 3. Consider any input win = w1 $ . . $wm . We first note that, since different strings do not influence each other, the strings in Cinit do not affect the evolution of the strings in Cinput concatenated with win and vice-versa.

We demonstrate that the AASP can learn various linear and nonlinear functions. For example, it is possible to learn to produce the average of two analog values. In combination with a chemical delay line [11], the AASP could also be used to predict time series. 2 Chemical Reaction Network To model the AASP we employ the chemical reaction network (CRN) formalism. A CRN consists of a finite set of molecular species and reactions paired with rate constants [12]. CRN represents an unstructured macroscopic simulated chemistry, hence, the species labeled with symbols are not assigned a molecular structure yet.

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