Masashi Aono

Amoeba-based neurocomputing with chaotic dynamics

Implementing a deadlock-breaking neural computing scheme that can flexibly search for reasonable solutions without any resource allocation program.

Two-Dimensional Self-Assembled Structures of Melamine and Melem at the Aqueous Solution−Au(111) Interface†

Self-assembled structures of melamine and the condensed melamine derivative melem were investigated at aqueous solution-Au(111) interfaces by cyclic voltammetry and in situ scanning tunneling microscopy (STM) observation. The adsorption/desorption behaviors of both molecules on Au(111) surfaces could be controlled by varying the electrochemical potential and solution concentration. In the negative potential region, self-assembled structures of melem and melamine were constructed by double hydrogen bonding systems between nitrogen atoms of triazine rings and amine groups. In addition, melem formed a closely packed structure at potentials of between -0.3 and -0.15 V or in solutions at higher concentrations.

Amoeba-inspired nanoarchitectonic computing: solving intractable computational problems using nanoscale photoexcitation transfer dynamics.

Biologically inspired computing devices and architectures are expected to overcome the limitations of conventional technologies in terms of solving computationally demanding problems, adapting to complex environments, reducing energy consumption, and so on. We previously demonstrated that a primitive single-celled amoeba (a plasmodial slime mold), which exhibits complex spatiotemporal oscillatory dynamics and sophisticated computing capabilities, can be used to search for a solution to a very hard combinatorial optimization problem. We successfully extracted the essential spatiotemporal dynamics by which the amoeba solves the problem. This amoeba-inspired computing paradigm can be implemented by various physical systems that exhibit suitable spatiotemporal dynamics resembling the amoebas problem-solving process. In this Article, we demonstrate that photoexcitation transfer phenomena in certain quantum nanostructures mediated by optical near-field interactions generate the amoebalike spatiotemporal dynamics and can be used to solve the satisfiability problem (SAT), which is the problem of judging whether a given logical proposition (a Boolean formula) is self-consistent. SAT is related to diverse application problems in artificial intelligence, information security, and bioinformatics and is a crucially important nondeterministic polynomial time (NP)-complete problem, which is believed to become intractable for conventional digital computers when the problem size increases. We show that our amoeba-inspired computing paradigm dramatically outperforms a conventional stochastic search method. These results indicate the potential for developing highly versatile nanoarchitectonic computers that realize powerful solution searching with low energy consumption.

Decision maker based on nanoscale photo-excitation transfer.

Decision-making is one of the most important intellectual abilities of the human brain. Here we propose an efficient decision-making system which uses optical energy transfer between quantum dots (QDs) mediated by optical near-field interactions occurring at scales far below the wavelength of light. The simulation results indicate that our system outperforms the softmax rule, which is known as the best-fitting algorithm for human decision-making behaviour. This suggests that we can produce a nano-system which makes decisions efficiently and adaptively by exploiting the intrinsic spatiotemporal dynamics involving QDs mediated by optical near-field interactions.

Amoeba-based Chaotic Neurocomputing: Combinatorial Optimization by Coupled Biological Oscillators

We demonstrate a neurocomputing system incorporating an amoeboid unicellular organism, the true slime mold Physarum, known to exhibit rich spatiotemporal oscillatory behavior and sophisticated computational capabilities. Introducing optical feedback applied according to a recurrent neural network model, we induce that the amoeba’s photosensitive branches grow or degenerate in a network-patterned chamber in search of an optimal solution to the traveling salesman problem (TSP), where the solution corresponds to the amoeba’s stably relaxed configuration (shape), in which its body area is maximized while the risk of being illuminated is minimized.Our system is capable of reaching the optimal solution of the four-city TSP with a high probability. Moreover, our system can find more than one solution, because the amoeba can coordinate its branches’ oscillatory movements to perform transitional behavior among multiple stable configurations by spontaneously switching between the stabilizing and destabilizing modes. We show that the optimization capability is attributable to the amoeba’s fluctuating oscillatory movements. Applying several surrogate data analyses, we present results suggesting that the amoeba can be characterized as a set of coupled chaotic oscillators.

Amoeba-based nonequilibrium neurocomputer utilizing fluctuations and instability

We employ a photosensitive amoeboid cell known as a model organism for studying cellular information processing, and construct an experimental system for exploring the amoebas processing ability of information on environmental light stimuli. The system enables to examine the amoebas solvability of various problems imposed by an optical feedback, as the feedback is implemented with a neural network algorithm. We discovered that the amoeba solves the problems by positively exploiting fluctuations and instability of its components. Thus, our system works as a neurocomputer having flexible properties. The elucidation of the amoebas dynamics may lead to the development of unconventional computing devices based on nonequilibrium media to utilize fluctuations and instability.

Information physics fundamentals of nanophotonics

Nanophotonics has been extensively studied with the aim of unveiling and exploiting light-matter interactions that occur at a scale below the diffraction limit of light, and recent progress made in experimental technologies--both in nanomaterial fabrication and characterization--is driving further advancements in the field. From the viewpoint of information, on the other hand, novel architectures, design and analysis principles, and even novel computing paradigms should be considered so that we can fully benefit from the potential of nanophotonics. This paper examines the information physics aspects of nanophotonics. More specifically, we present some fundamental and emergent information properties that stem from optical excitation transfer mediated by optical near-field interactions and the hierarchical properties inherent in optical near-fields. We theoretically and experimentally investigate aspects such as unidirectional signal transfer, energy efficiency and networking effects, among others, and we present their basic theoretical formalisms and describe demonstrations of practical applications. A stochastic analysis of light-assisted material formation is also presented, where an information-based approach provides a deeper understanding of the phenomena involved, such as self-organization. Furthermore, the spatio-temporal dynamics of optical excitation transfer and its inherent stochastic attributes are utilized for solution searching, paving the way to a novel computing paradigm that exploits coherent and dissipative processes in nanophotonics.

Single-photon decision maker

Decision making is critical in our daily lives and for society in general and is finding evermore practical applications in information and communication technologies. Herein, we demonstrate experimentally that single photons can be used to make decisions in uncertain, dynamically changing environments. Using a nitrogen-vacancy in a nanodiamond as a single-photon source, we demonstrate the decision-making capability by solving the multi-armed bandit problem. This capability is directly and immediately associated with single-photon detection in the proposed architecture, leading to adequate and adaptive autonomous decision making. This study makes it possible to create systems that benefit from the quantum nature of light to perform practical and vital intelligent functions.

Beyond input-output computings: Error-driven emergence with parallel non-distributed slime mold computer

The emergence derived from errors is the key importance for both novel computing and novel usage of the computer. In this paper, we propose an implementable experimental plan for the biological computing so as to elicit the emergent property of complex systems. An individual plasmodium of the true slime mold Physarum polycephalum acts in the slime mold computer. Modifying the Elementary Cellular Automaton as it entails the global synchronization problem upon the parallel computing provides the NP-complete problem solved by the slime mold computer. The possibility to solve the problem by giving neither all possible results nor explicit prescription of solution-seeking is discussed. In slime mold computing, the distributivity in the local computing logic can change dynamically, and its parallel non-distributed computing cannot be reduced into the spatial addition of multiple serial computings. The computing system based on exhaustive absence of the super-system may produce, something more than filling the vacancy.

Decision making based on optical excitation transfer via near-field interactions between quantum dots

Optical near-field interactions between nanostructured matter, such as quantum dots, result in unidirectional optical excitation transfer when energy dissipation is induced. This results in versatile spatiotemporal dynamics of the optical excitation, which can be controlled by engineering the dissipation processes and exploited to realize intelligent capabilities such as solution searching and decision making. Here we experimentally demonstrate the ability to solve a decision making problem on the basis of optical excitation transfer via near-field interactions by using colloidal quantum dots of different sizes, formed on a geometry-controlled substrate. We characterize the energy transfer behavior due to multiple control light patterns and experimentally demonstrate the ability to solve the multi-armed bandit problem. Our work makes a decisive step towards the practical design of nanophotonic systems capable of efficient decision making, one of the most important intellectual attributes of the human brain.