Bureaucrats, emailconfirmed, Administrators
5,478
edits
GMU:BioArt WS15/Physarum polycephalum and unconventional computing (view source)
Revision as of 11:35, 8 December 2015
, 8 December 2015no edit summary
No edit summary |
No edit summary |
||
| Line 2: | Line 2: | ||
== Physarum Polycephalum and its life cycle== | == Physarum Polycephalum and its life cycle== | ||
Physarum polycephalum, literally the "many-headed slime", is a slime mold that inhabits shady, cool, moist areas, such as decaying leaves and logs. Like slime molds in general, it is sensitive to light; in particular, light can repel the slime mold and be a factor in triggering spore growth.(wikipedia A) It feeds on bacteria, spores and other microbial creatures. | "Physarum polycephalum, literally the "many-headed slime", is a slime mold that inhabits shady, cool, moist areas, such as decaying leaves and logs. Like slime molds in general, it is sensitive to light; in particular, light can repel the slime mold and be a factor in triggering spore growth."(wikipedia A) It feeds on bacteria, spores and other microbial creatures. | ||
* Vegetative phase: plasmodium (consists of networks of protoplasmic veins, and many nuclei) | * Vegetative phase: plasmodium (consists of networks of protoplasmic veins, and many nuclei) | ||
| Line 17: | Line 17: | ||
* They will gather and form a stalk and then a fruiting body | * They will gather and form a stalk and then a fruiting body | ||
* Those self making up the stalk will die. Those at the top will clump into a ball made of life spores (Bonner) | * Those self making up the stalk will die. Those at the top will clump into a ball made of life spores (Bonner) | ||
https://www.youtube.com/watch?v=bkVhLJLG7ug&list=PLb14u5e_rEcSVd0ZjgEFHuggA6y7ozQQI | https://www.youtube.com/watch?v=bkVhLJLG7ug&list=PLb14u5e_rEcSVd0ZjgEFHuggA6y7ozQQI | ||
| Line 36: | Line 35: | ||
* [https://www.youtube.com/watch?v=2UxGrde1NDA Toshiyuki Nakagaki. 3-5 min @ Heather Barnett: What humans can learn from semi-intelligent slime] | * [https://www.youtube.com/watch?v=2UxGrde1NDA Toshiyuki Nakagaki. 3-5 min @ Heather Barnett: What humans can learn from semi-intelligent slime] | ||
Unconventional computing is an interdisciplinary of science where computer scientists, physicists, mathematicians, apply principles of information processing in natural systems to design novel computer devices and architectures (Adamatzky 2007) | "Unconventional computing is an interdisciplinary of science where computer scientists, physicists, mathematicians, apply principles of information processing in natural systems to design novel computer devices and architectures" (Adamatzky 2007) | ||
“The plasmodium functions as a parallel amorphous computer with parallel inputs and parallel outputs. Data are represented by spatial configurations of sources of nutrients. A program of computation is coded via configurations of repellents and attractants. Results of computation are presented by the configuration of the protoplasmic network and the localisation of the plasmodium.”(Adamatzky 2010) | “The plasmodium functions as a parallel amorphous computer with parallel inputs and parallel outputs. Data are represented by spatial configurations of sources of nutrients. A program of computation is coded via configurations of repellents and attractants. Results of computation are presented by the configuration of the protoplasmic network and the localisation of the plasmodium.”(Adamatzky 2010) | ||
| Line 53: | Line 52: | ||
=== Markov chain === | === Markov chain === | ||
A Markov chain (discrete-time Markov chain or DTMC[1]), named after Andrey Markov, is a random process that undergoes transitions from one state to another on a state space. It must possess a property that is usually characterized as "memorylessness": the probability distribution of the next state depends only on the current state and not on the sequence of events that preceded it. This specific kind of "memorylessness" is called the Markov property. Markov chains have many applications as statistical models of real-world processes.(wikipedia (b)) | "A Markov chain (discrete-time Markov chain or DTMC[1]), named after Andrey Markov, is a random process that undergoes transitions from one state to another on a state space. It must possess a property that is usually characterized as "memorylessness": the probability distribution of the next state depends only on the current state and not on the sequence of events that preceded it. This specific kind of "memorylessness" is called the Markov property. Markov chains have many applications as statistical models of real-world processes."(wikipedia (b)) | ||
=== Kolmogorov Machine === | === Kolmogorov Machine === | ||
Random-access machine (RAM) is an abstract machine in the general class of register machines. (wikipedia) | "Random-access machine (RAM) is an abstract machine in the general class of register machines." (wikipedia (c)) | ||
The RAM's equivalent of the universal Turing machine – with its program in the registers as well as its data – is called the random-access stored-program machine or RASP. It is an example of the so-called von Neumann architecture and is closest to the common notion of computer.(wikipedia (c)) | "The RAM's equivalent of the universal Turing machine – with its program in the registers as well as its data – is called the random-access stored-program machine or RASP. It is an example of the so-called von Neumann architecture and is closest to the common notion of computer."(wikipedia (c)) | ||
Together with the Turing machine and counter-machine models, the RAM and RASP models are used for computational complexity analysis. Van Emde Boas (1990) calls these three plus the pointer machine "sequential machine" models, to distinguish them from "parallel random-access machine" models.(wikipedia (c)) | "Together with the Turing machine and counter-machine models, the RAM and RASP models are used for computational complexity analysis. Van Emde Boas (1990) calls these three plus the pointer machine "sequential machine" models, to distinguish them from "parallel random-access machine" models."(wikipedia (c)) | ||
Kolmogorov, or Kolmogorov-Uspensky, machines [Ko1, KU, US] are similar to Turing machines except that the tape can change its topology.(Gurevich) | "Kolmogorov, or Kolmogorov-Uspensky, machines [Ko1, KU, US] are similar to Turing machines except that the tape can change its topology."(Gurevich) | ||
Мы остановимся на следующих вариантах математического опреде ления вычислимой функции или алгоритма: | "Мы остановимся на следующих вариантах математического опреде ления вычислимой функции или алгоритма: | ||
A) Определение вычислимой функции как функции, значения которой выводимы в некотором логическом исчислении (Гёдель [4], Чёрч [5]1)). Б) Определение вычислимой функции как функции, значения кото | A) Определение вычислимой функции как функции, значения которой выводимы в некотором логическом исчислении (Гёдель [4], Чёрч [5]1)). Б) Определение вычислимой функции как функции, значения кото | ||
рой получаются при помощи исчисления Х-коиверсии Чёрча [5], [7]. | рой получаются при помощи исчисления Х-коиверсии Чёрча [5], [7]. | ||
| Line 70: | Line 69: | ||
Г) Вычислительная машина Тьюринга [ И ] 3 ) . | Г) Вычислительная машина Тьюринга [ И ] 3 ) . | ||
Д) Финитный комбинаторный процесс Поста [13]. | Д) Финитный комбинаторный процесс Поста [13]. | ||
Е) Нормальный алгорифм А. А. Маркова [1], [2]. | Е) Нормальный алгорифм А. А. Маркова [1], [2]." | ||
Kolmogorov machines tape similarly to Schönhage’s tape is a finite connected graph with a distinguished (active) node. They work upon partly recursive function, changing instructions in real time. | "Kolmogorov machines tape similarly to Schönhage’s tape is a finite connected graph with a distinguished (active) node. They work upon partly recursive function, changing instructions in real time." (Gurevich) | ||
Instructions: | "Instructions: | ||
1. add a new node together with a pair of edges of some colors between the active node and the new one, | 1. add a new node together with a pair of edges of some colors between the active node and the new one, | ||
2. remove a node and the edges incident to it, | 2. remove a node and the edges incident to it, | ||
3. add a pair of edges of some colors between two existing nodes, | 3. add a pair of edges of some colors between two existing nodes, | ||
4. remove the two edges between two existing nodes, | 4. remove the two edges between two existing nodes, | ||
5. halt. (Gurevich) | 5. halt. "(Gurevich) | ||
Grigoriev [Gr] exhibited a function real-time computable by some KU machine but not real-time computable by any Turing machine.(Gurevich) | "Grigoriev [Gr] exhibited a function real-time computable by some KU machine but not real-time computable by any Turing machine."(Gurevich) | ||
== Projects == | == Projects == | ||
| Line 91: | Line 90: | ||
2. Leslie Garcia and Paloma López. Machine shop. | 2. Leslie Garcia and Paloma López. Machine shop. | ||
Video made during the workshop Bio Machines cultural center in the 77 organized by the Laboratory of Digital Citizenship. Participants cultured Physarum polycephalum samples to understand their life cycle. They also modified web cameras to turn them into microscopes inexpensive (28 pesos each) and to closely observe their growth and oscillations using software written with processing. | "Video made during the workshop Bio Machines cultural center in the 77 organized by the Laboratory of Digital Citizenship. Participants cultured Physarum polycephalum samples to understand their life cycle. They also modified web cameras to turn them into microscopes inexpensive (28 pesos each) and to closely observe their growth and oscillations using software written with processing." | ||
https://www.youtube.com/watch?v=4sp9Efokv4o | https://www.youtube.com/watch?v=4sp9Efokv4o | ||