Highly-integrable analogue reservoir circuits based on a simple cycle architecture.

Physical reservoir computing is a promising solution for accelerating artificial intelligence (AI) computations. Various physical systems that exhibit nonlinear and fading-memory properties have been proposed as physical reservoirs. Highly-integrable physical reservoirs, particularly for edge AI computing, has a strong demand. However, realizing a practical physical reservoir with high performance and integrability remains challenging. Herein, we present an analogue circuit reservoir with a simple cycle architecture suitable for complementary metal-oxide-semiconductor (CMOS) chip integration. In several benchmarks and demonstrations using synthetic and real-world data, our developed hardware prototype and its simulator exhibit a high prediction performance and sufficient memory capacity for practical applications, showing promise for future applications in highly integrated AI accelerators.

Physical Implementation of Reservoir Computing through Electrochemical Reaction.

Nonlinear dynamical systems serving reservoir computing enrich the physical implementation of computing systems. A method for building physical reservoirs from electrochemical reactions is provided, and the potential of chemical dynamics as computing resources is shown. The essence of signal processing in such systems includes various degrees of ionic currents which pass through the solution as well as the electrochemical current detected based on a multiway data acquisition system to achieve switchable and parallel testing. The results show that they have respective advantages in periodic signals and temporal dynamic signals. Polyoxometalate molecule in the solution increases the diversity of the response current and thus improves their abilities to predict periodic signals. Conversely, distilled water exhibits great computing power in solving a second-order nonlinear problem. It is expected that these results will lead to further exploration of ionic conductance as a nonlinear dynamical system and provide more support for novel devices as computing resources.

Long- and Short-Term Conductance Control of Artificial Polymer Wire Synapses.

Networks in the human brain are extremely complex and sophisticated. The abstract model of the human brain has been used in software development, specifically in artificial intelligence. Despite the remarkable outcomes achieved using artificial intelligence, the approach consumes a huge amount of computational resources. A possible solution to this issue is the development of processing circuits that physically resemble an artificial brain, which can offer low-energy loss and high-speed processing. This study demonstrated the synaptic functions of conductive polymer wires linking arbitrary electrodes in solution. By controlling the conductance of the wires, synaptic functions such as long-term potentiation and short-term plasticity were achieved, which are similar to the manner in which a synapse changes the strength of its connections. This novel organic artificial synapse can be used to construct information-processing circuits by wiring from scratch and learning efficiently in response to external stimuli.

A molecular neuromorphic network device consisting of single-walled carbon nanotubes complexed with polyoxometalate.

In contrast to AI hardware, neuromorphic hardware is based on neuroscience, wherein constructing both spiking neurons and their dense and complex networks is essential to obtain intelligent abilities. However, the integration density of present neuromorphic devices is much less than that of human brains. In this report, we present molecular neuromorphic devices, composed of a dynamic and extremely dense network of single-walled carbon nanotubes (SWNTs) complexed with polyoxometalate (POM). We show experimentally that the SWNT/POM network generates spontaneous spikes and noise. We propose electron-cascading models of the network consisting of heterogeneous molecular junctions that yields results in good agreement with the experimental results. Rudimentary learning ability of the network is illustrated by introducing reservoir computing, which utilises spiking dynamics and a certain degree of network complexity. These results indicate the possibility that complex functional networks can be constructed using molecular devices, and contribute to the development of neuromorphic devices.

Dynamical system design for silicon neurons using phase reduction approach.

In the present paper, we apply a computer-aided phase reduction approach to dynamical system design for silicon neurons (SiNs). Firstly, we briefly review the dynamical system design for SiNs. Secondly, we summarize the phase response properties of circuit models of previous SiNs to clarify design criteria in our approach. From a viewpoint of the phase reduction theory, as a case study, we show how to tune circuit parameters of the resonate-and-fire neuron (RFN) circuit as a hybrid type SiN. Finally, we demonstrate delay-induced synchronization in a silicon spiking neural network that consists of the RFN circuits.

Active Brownian motion in threshold distribution of a Coulomb blockade model.

Randomly distributed offset charges affect the nonlinear current-voltage property via the fluctuation of the threshold voltage above which the current flows in an array of a Coulomb blockade (CB). We analytically derive the distribution of the threshold voltage for a model of one-dimensional locally coupled CB arrays and propose a general relationship between conductance and distribution. In addition, we show that the distribution for a long array is equivalent to the distribution of the number of upward steps for aligned objects of different heights. The distribution satisfies a novel Fokker-Planck equation corresponding to active Brownian motion. The feature of the distribution is clarified by comparing it with the Wigner and Ornstein-Uhlenbeck processes. It is not restricted to the CB model but is instructive in statistical physics generally.

Effect of overglazed and polished surface finishes on the compressive fracture strength of machinable ceramic materials.

Controversy prevails over the effect of overglazing on the fracture strength of ceramic materials. Therefore, the effects of different surface finishes on the compressive fracture strength of machinable ceramic materials were investigated in this study. Plates prepared from four commercial brands of ceramic materials were either surface-polished or overglazed (n=10 per ceramic material for each surface finish), and bonded to flat surfaces of human dentin using a resin cement. Loads at failure were determined and statistically analyzed using two-way ANOVA and Bonferroni test. Although no statistical differences in load value were detected between polished and overglazed groups (p>0.05), the fracture load of Vita Mark II was significantly lower than those of ProCAD and IPS Empress CAD, whereas that of IPS e.max CAD was significantly higher than the latter two ceramic materials (p<0.05). It was concluded that overglazed and polished surfaces produced similar compressive fracture strengths irrespective of the machinable ceramic material tested, and that fracture strength was material-dependent.

Analog VLSI implementation of resonate-and-fire neuron.

We propose an analog integrated circuit that implements a resonate-and-fire neuron (RFN) model based on the Lotka-Volterra (LV) system. The RFN model is a spiking neuron model that has second-order membrane dynamics, and thus exhibits fast damped subthreshold oscillation, resulting in the coincidence detection, frequency preference, and post-inhibitory rebound. The RFN circuit has been derived from the LV system to mimic such dynamical behavior of the RFN model. Through circuit simulations, we demonstrate that the RFN circuit can act as a coincidence detector and a band-pass filter at circuit level even in the presence of additive white noise and background random activity. These results show that our circuit is expected to be useful for very large-scale integration (VLSI) implementation of functional spiking neural networks.

A majority-logic nanodevice using a balanced pair of single-electron boxes.

This paper describes a majority-logic gate device that will be useful in developing single-electron integrated circuits. The gate device consists of two identical single-electron boxes combined to form a balanced pair. It accepts three inputs and produces a majority-logic output by using imbalances caused by the input signals; it produces a 1 output if two or three inputs are 1, and a 0 output if two or three inputs are 0. We combine these gate devices into two subsystems, a shift register and an adder, and demonstrate their operation by computer simulation. We also propose a method of fabricating the unit element of the gate device, a minute dot with four coupling arms. We demonstrate by experiments that it is possible to arrange these unit elements on a GaAs substrate, in a self-organizing manner, by means of a process technology that is based on selective-area metalorganic vapor-phase epitaxy.