Computing in Verotoxin

Adamatzky, Andrew

doi:10.1002/cphc.201700477

Cited by 13 publications

(16 citation statements)

References 36 publications

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“…The distribution demonstrates frequencies of discoveries of the fourinput-one-output logical gates and could be used for characterisation of a computational power of the fungal substrates. This is accompanied by distributions of gates discovered in experimental laboratory reservoir computing with slime mould Physarum polycephalum [18], succulent plant [8] and numerical modelling experiments on computing with protein verotoxin [3], actin bundles network [9], and actin monomer [4]. The distributions of gates discovered in natural systems are alike to each other in the hierarchies of the gates frequencies.…”

Section: Spikes Derived Logical Gatesmentioning

confidence: 90%

“…Figure 6. Comparative ratios of Boolean gates discovered in mycelium network analysed in present paper, black disc and solid line; slime mould Physarum polycephalum[18], black circle and dotted line; succulent plant[8], red snowflake and dashed line; single molecule of protein verotoxin[3], light blue '+' and dash-dot line; actin bundles network[9], green triangle pointing right and dash-dot-dot line; actin monomer[4], magenta triangle pointing left and dashed line. Area of xor gate is magnified in the insert.…”

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confidence: 85%

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Logics in fungal mycelium networks

Adamatzky¹,

Ayres²,

Beasley³

et al. 2021

Preprint

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The living mycelium networks are capable of efficient sensorial fusion over very large areas and distributed decision making. The information processing in the mycelium networks is implemented via propagation of electrical and chemical signals en pair with morphological changes in the mycelium structure. These information processing mechanisms are manifested in experimental laboratory findings that show that the mycelium networks exhibit rich dynamics of neuron-like spiking behaviour and a wide range of non-linear electrical properties. On an example of a single real colony of Aspergillus niger, we demonstrate that the non-linear transformation of electrical signals and trains of extracellular voltage spikes can be used to implement logical gates and circuits. The approaches adopted include numerical modelling of excitation propagation on the mycelium network, representation of the mycelium network as a resistive and capacitive (RC) network and an experimental laboratory study on mining logical circuits in mycelium bound composites.

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Section: Spikes Derived Logical Gatesmentioning

confidence: 90%

mentioning

confidence: 85%

Logics in fungal mycelium networks

Adamatzky¹,

Ayres²,

Beasley³

et al. 2021

Preprint

Self Cite

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show abstract

“…9. This is accompanied by distributions of gates discovered in experimental laboratory reservoir computing with slime mould Physarum polycephalum [34], succulent plant [11] and numerical modelling experiments on computing with protein verotoxin [2], actin bundles network [12], and actin monomer [3]. All the listed distributions have very similar structure with gates selecting one of the inputs in majority, followed by or gate, not-and an and-not gates.…”

Section: Distribution Of Boolean Gatesmentioning

confidence: 99%

On Boolean gates in fungal colony

et al. 2020

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A fungal colony maintains its integrity via flow of cytoplasm along mycelium network. This flow, together with possible coordination of mycelium tips propagation, is controlled by calcium waves and associated waves of electrical potential changes. We propose that these excitation waves can be employed to implement a computation in the mycelium networks. We use FitzHugh-Nagumo model to imitate propagation of excitation in a single colony of Aspergillus niger. Boolean values are encoded by spikes of extracellular potential. We represent binary inputs by electrical impulses on a pair of selected electrodes and we record responses of the colony from sixteen electrodes. We derive sets of two-inputs-on-output logical gates implementable the fungal colony and analyse distributions of the gates.

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“…Most close to biophysical reality models of computing on biological networks have been implemented with molecular structures of verotoxin protein [2] and actin monomer [3], actin bundles networks derived from experimental laboratory data [6], plant leaf vascular system [4] and microscopic images of three-dimensional fungal colonies [7]. These approaches employed the following method.…”

Section: Introductionmentioning

confidence: 99%

Generating Boolean Functions on Totalistic Automata Networks

Goles,

Adamatzky,

Montealegre

et al. 2021

Preprint

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We consider the problem of studying the simulation capabilities of the dynamics of arbitrary networks of finite states machines. In these models, each node of the network takes two states 0 (passive) and 1 (active). The states of the nodes are updated in parallel following a local totalistic rule, i.e., depending only on the sum of active states. Four families of totalistic rules are considered: linear or matrix defined rules (a node takes state 1 if each of its neighbours is in state 1), threshold rules (a node takes state 1 if the sum of its neighbours exceed a threshold), isolated rules (a node takes state 1 if the sum of its neighbours equals to some single number) and interval rule (a node takes state 1 if the sum of its neighbours belong to some discrete interval). We focus in studying the simulation capabilities of the dynamics of each of the latter classes. In particular, we show that totalistic automata networks governed by matrix defined rules can only implement constant functions and other matrix defined functions. In addition, we show that t by threshold rules can generate any monotone Boolean functions. Finally, we show that networks driven by isolated and the interval rules exhibit a very rich spectrum of boolean functions as they can, in fact, implement any arbitrary Boolean functions. We complement this results by studying experimentally the set of different Boolean functions generated by totalistic rules on random graphs.

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Computing in Verotoxin

Cited by 13 publications

References 36 publications

Logics in fungal mycelium networks

Logics in fungal mycelium networks

On Boolean gates in fungal colony

Generating Boolean Functions on Totalistic Automata Networks

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