In this study, a novel wireless central pattern generator (CPG) is presented. Detailed analyses derive parameter values suitable for realizing a typical gait of a multi-legged robot and for assisting a typical motion of a human. The CPG is implemented by field programmable gate arrays coupled via simple on-off keying radio transmitters and receivers. Experiments show the wireless CPG can realize the robot gait control and the human motion assist. It is also shown the presented CPG can be implemented with fewer circuit elements and consumes lower power compared to a commonly used digital-processor-based CPG. Finally, significance and impact of this study are discussed.

This study explores the intersection of neuroscience and computer science, focusing on the use of spiking neural networks (SNNs) to simulate the behavior of biological neurons. A neural network model based on the Izhikevich neuron model is proposed to simulate the local auditory network of crickets. The parameters of the neuron model are optimized based on evaluation functions and identified by Particle Swarm Optimization (PSO), aligning its input-output relationships with the observed cricket neuron responses. The results showed that the network successfully simulated the behavior of individual neurons, promising applications in fields like neural prosthetics.

A novel design method of an integrated cochlear model, designed for hardware efficiency, is presented, where its nonlinear dynamics is governed by ergodic sequential logic. It is shown the presented model can reproduce the nonlinear compression characteristics of a mammalian cochlea, which is one of the most representative mammalian nonlinear sound processing functions. Furthermore, the presented model is implemented by a field-programmable gate array and its functionality is confirmed through experiments. It is shown the presented model consumes significantly lower hardware resources and power in comparison to a standard ordinary differential equation cochlear model.

In this paper, a novel wireless functional electrical stimulation (FES) device based on an ergodic cellular automaton (CA) central pattern generator (CPG) is proposed, where the CPG consists of a network of ergodic CA oscillators coupled via wireless impulse radio (IR) sequences. It is shown that the proposed CPG can realize various synchronization phenomena by adjusting parameters. In addition, an electric stimulator controlled by a novel pain relaxation filter is proposed. Then the proposed wireless FES device is implemented and human subject experiments reveal that the device can assist periodic motions of arms for walking and squatting. It is also shown that the pain relaxation filter can decrease the pain caused by the electric stimulation. Furthermore, it is shown that the proposed CPG can be implemented by fewer circuit elements and consumes lower power compared to a conventional digital-processor-based CPG. Finally, it is discussed that the results of this paper will contribute to the development of a large-scale, small, and low-power wireless FES device to support a variety of body movements.

A novel hardware-efficient ergodic cellular automaton asthma model is proposed. It is shown that the proposed model can reproduce typical nonlinear phenomena and typical differentiation mechanisms of a conventional delayed ordinary differential equation asthma model. In addition, the proposed model is implemented by a field programmable gate array and its operation is verified by experiments. It is then shown that the proposed model can be implemented by much fewer circuit elements and consumes much lower power compared to the conventional asthma model. Furthermore, it is discussed that the results of this paper will contribute to the development of hardware-efficient immune system simulators for personalized drug discovery and medicine.

In this paper, a novel ergodic cellular automaton cochlear partition model is proposed. It is shown that the proposed model can reproduce nonlinear frequency response characteristics of mammalians (guinea pig and cat) to sound inputs. In addition, the proposed model is implemented by a field programmable gate array and its operations are validated by experiments. It is then shown that the proposed model consumes significantly lower power than a standard digital-processor-based cochlear partition model. Furthermore, it is discussed that the results of this paper will contribute to the development of an ultra-low-power cochlear implant that can reproduce nonlinear responses of the human cochlea.

In this study, a novel ergodic sequential logic (SL) central pattern generator (CPG) is presented. First, a novel ergodic SL oscillator is presented and it is shown that the oscillator can realize oscillations with various amplitudes and frequencies by adjusting its parameters. Second, using the oscillator, the ergodic SL CPG is presented. It is shown that the CPG can realize multiple gaits of a six-legged robot by adjusting its parameters. Third, a modulation method of the CPG to realize safe transitions between different gaits is proposed. Experiments validate that the modulation method can realize safe transitions between different gaits of the robot, whereas a straightforward method of changing the gaits leads to unsafe operations of the robot. Furthermore, it is shown that the ergodic SL CPG is much more hardware-efficient compared to a commonly used digital processor CPG. Finally, it is discussed that the presented CPG and its modulation method will contribute to develop a hardware-efficient smart gait controller of legged robots, which can realize safe gait changes.

An ergodic sequential logic (SL) neuron model is designed and it is shown that the model can reproduce various nonlinear responses of neurons. Using the neuron model, a novel ergodic SL spiking neural network is presented. It is shown that the network can reproduce biologically plausible spatio-temporal phenomena, (e.g., fundamental synchronization phenomenon and complicated chimera phenomenon) which are typically observed in the brain. In addition, the network is implemented by a field programmable gate array and it is shown that the presented network is more hardware-efficient compared to a commonly used digital processor spiking neural network. Furthermore, it is discussed that the presented network will contribute to develop a small and low power brain prosthetic device.

In this paper, a novel liquid state machine (LSM) comprising neurons whose nonlinear dynamics are described by a non-simultaneous cellular automaton (CA) is proposed. The proposed LSM is applied to a supervised classification task. It is shown that the proposed LSM can recognize spatio-temporal spike patterns with high accuracy. Furthermore, the non-simultaneous CA-based neuron is implemented on a field programmable gate array (FPGA), and an experiment validates its spiking function. It is then shown that the non-simultaneous CA-based neuron occupies fewer FPGA resources compared with typical conventional neuron models, such as the Izhikevich, leaky integrate-and-fire, quadratic integrate-and-fire, and Morris–Lecar neurons.

In this paper, a novel hardware-efficient integrated cochlear model, whose nonlinear dynamics is described by er-godic sequential logics, is presented. It is shown that the presented cochlear model can reproduce combination tone generation of a mammalian cochlea, which is one of the most typical nonlinear sound processing functions. In addition, the presented model is implemented by a field-programmable gate array (FPGA) and its operations are verified by experiments. It is then shown that the presented model consumes much less hardware resources and much less power compared to a standard ordinary differential equation (ODE) model of cochlea.

In this paper, a novel hardware-efficient electronic circuit cochlear model, the dynamics of which are described by an ergodic cellular automaton, is presented. Based on theoretical and numerical analyses, a parameter setting method so that the presented model properly works as a cochlear model is proposed. It is shown that the presented cochlear model designed by the proposed parameter setting method can reproduce typical nonlinear sound processing functions of mammalian cochleae such as nonlinear compression and two-tone distortion products. Furthermore, the presented model is implemented by a field programmable gate array (FPGA) and its operations are validated by experiments. It is shown that the presented model is much more hardware-efficient (i.e., consumes many fewer circuits elements) compared to some other electronic circuit cochlear models.

In this paper, a novel ergodic cellular automaton neuron model and its network is presented. Additionally, a learning method of the presented network to mimic a network of biologically plausible neuron models is presented. It is shown that the learning method enables the presented network to reproduce input-output relations of the biologically plausible neuron network model. Furthermore, the presented network and the learning method are implemented in an FPGA and it is shown that the presented network is more hardware-efficient compared to the biologically plausible neuron network model.

A novel central pattern generator (CPG) based locomotion controller for a quadruped robot is proposed. The model is composed of a network of neuronal oscillators, where the nonlinear dynamics of each oscillator is modeled by an asynchronous sequential logic. Based on bifurcation analyses of the oscillator, a systematic design method of the oscillator to generate a prescribed target gait for the quadruped robot is proposed. Furthermore, a systematic design method of the network to generate the prescribed target gait is also proposed. Then, a prototype of the proposed model is implemented in an FPGA and experiments show that the model can generate the prescribed target gait of a physically implemented quadruped robot. It is also shown that the proposed model consumes lower power and fewer circuit resources compared to a CPG-based locomotion controller implemented by a digital signal processor.

This paper designs a multi-compartment soma-dendrite-spine model having nonlinear dynamics of an asynchronous cellular automaton. The model can exhibit various propagations of action potentials observed in neurons and these propagations are analyzed in detailed. Then, using the analysis results, a novel systematic design method of the model is proposed. It is shown that the model designed by the proposed method can realize robust conditioning based on spike-timing dependent plasticity (STDP). Also, the designed model is implemented by a field programmable gate array (FPGA) and experiments validate its STDP-based conditioning function. It is then shown that the designed model consumes fewer hardware resources and lower power compared to an ODE-based multi-compartment model.

In this paper, a novel hardware-efficient central pattern generator (CPG) model based on asynchronous coupling of cellular automaton (CA) phase oscillators for a hexapod robot is presented. It is shown that the presented model can exhibit various synchronization patterns depending on parameter values. In order to analyze the synchronization patterns, a phase equilibrium and an evaluation function for a target synchronization pattern are introduced. As a result of the analysis, it is shown that an asynchronously coupled CA phase oscillators is suitable for the hexapod robot than a synchronously coupled CA phase oscillators. The presented asynchronous CPG model with parameters tuned appropriately is implemented on a field programmable gate array (FPGA) device and the device is mounted on a hexapod robot. A laboratory experiment verifies that the hexapod robot can reproduce one of typical gaits of six-legged insects. Finally, it is shown that the presented CPG model consumes far fewer circuits elements and much less power compared with one of conventional numerical integration CPG model and our previous CPG model.

This paper presents a novel central pattern generator (CPG) model based on nonlinear dynamics of asynchronous cellular automata. It is shown that the presented CPG model can exhibit various synchronization phenomena depending on parameter values. In order to evaluate usefulness of the presented CPG model, this paper focuses on controlling a hexapod robot shown in Fig. 1. Based on intensive analyses of the synchronization phenomena, a parameter tuning method to realize a tripod gait of the hexapod robot is derived. Then the CPG model with a tuned parameter value is implemented in a field programmable gate array and it is shown that the CPG model can realize a tripod gait of the hexapod robot. Also, it is shown that the presented CPG model uses much fewer circuit elements and consumes much less power compared to a conventional CPG model.

In this paper, a novel hardware-efficient spiking neuron model based on an asynchronous cellular automaton is proposed. It is shown that the proposed model can exhibit typical nonlinear response curves (so-called IF-curves) such as class 1 excitability without hysteresis, class 2 excitability without hysteresis, and class 2 excitability and class 1 spiking with hysteresis. The proposed model is implemented by an FPGA device and it is shown that the FPGA-implemented proposed model can exhibit the typical nonlinear response curves. Furthermore, it is shown that the proposed model can be implemented by much fewer circuit elements compared to some conventional neuron models that exhibit the typical nonlinear response curves.

The gene affects various behaviors of animals such as circadian rhythm, courtship behavior, motor behavior, visual behavior, and learning. The circadian rhythm is a biological rhythm having a period of almost 24 hours, which is sometimes called internal clock or biological clock. In this paper, a gene network model based on the nonlinear dynamics of an asynchronous cellular automaton is proposed. It is shown that the proposed gene network model can reproduce circadian rhythms generated by one of the representative simplified ordinary differential equation gene network models. It is also shown that the proposed gene network model can be implemented in a field programmable gate array (ab. FPGA) with much less hardware resource compared to the ordinary differential equation gene network model. Hence, the results of this paper will contribute to develop a hardware-based large scale gene network simulator the applications of which include a hardware-based genomic drug discovery (e.g., special hardware to execute simulations of large scale gene networks for drug discovery).

In this paper, a multi-compartment soma-dendrite model based on asynchronous cellular automaton dynamics is designed. It is shown that the model can reproduce typical propagation phenomena of membrane potentials between somas and dendrites of neurons. Also, the model is implemented in a field programmable gate array and it is shown that the model can be implemented by using much less hardware resource compared to conventional multi-compartment soma-dendrite models.

Modeling and implementation approaches for the reproduction of input-output relationships in biological nervous tissues contribute to the development of engineering and clinical applications. However, because of high nonlinearity, the traditional modeling and implementation approaches encounter difficulties in terms of generalization ability (i.e., performance when reproducing an unknown data set) and computational resources (i.e., computation time and circuit elements). To overcome these difficulties, asynchronous cellular automaton-based neuron (ACAN) models, which are described as special kinds of cellular automata that can be implemented as small asynchronous sequential logic circuits have been proposed. This paper presents a novel type of such ACAN and a theoretical analysis of its excitability. This paper also presents a novel network of such neurons, which can mimic input-output relationships of biological and nonlinear ordinary differential equation model neural networks. Numerical analyses confirm that the presented network has a higher generalization ability than other major modeling and implementation approaches. In addition, Field-Programmable Gate Array-implementations confirm that the presented network requires lower computational resources.

The mammalian cochlea has complicated nonlinear dynamics and exhibits various nonlinear responses to a sound stimulation. In this paper, a novel cochlea model the nonlinear dynamics of which is described by an asynchronous cellular automaton (triggered by multiple clocks) is presented. It is shown that the model can reproduce typical nonlinear responses observed in physiological measurements of a mammalian cochlea. Also, the model is implemented in a field programmable gate array and its operation is validated by laboratory measurements. It is shown that the model consumes much less hardware resources than a conventional representative simplified cochlea model.

The mammalian cochlear consists of nonlinear components: lymph (viscous fluid), a basilar membrane (vibrating membrane), outer hair cells (active dumpers), inner hair cells (neural transducers), and spiral ganglion cells (parallel spikes density modulators). In this paper, a novel spiral ganglion cell model based on an asynchronous sequential logic is presented. It is shown that the presented model can reproduce typical nonlinear responses of the spiral ganglion cell, e.g., spontaneous spiking, parallel spike density modulation, and adaptation. Also, FPGA experiments validate the reproductions of the nonlinear responses by the presented model.

This paper presents a nonlinear model of the human brain activity response to visual stimuli according to Blood-Oxygen-Level-Dependent (BOLD) signals scanned by functional Magnetic Resonance Imaging (fMRI). A BOLD signal usually contains a low frequency signal component (trend), which is often ignored by the existing models or removed by approximation methods. However, such detrending could also destroy the dynamics of the BOLD signal and miss an important response. This paper shows a model that, in the absence of detrending, can predict the BOLD signal with smaller errors than existing models. For detrending, the presented model has also a lower Schwarz information criterion than existing models, which implies that the presented model will be less likely to overfit the experimental data.

The neuron is roughly divided into three parts: soma, dendrite, and an axon. In this paper, a multi-compartment neuron model the dynamics of which is described by an asynchronous cellular automaton is presented. It is shown that the model can reproduce typical propagations of action potentials from dendrites to a soma (forward propagation) and from a soma to dendrites (backward propagation).

Modeling and implementation of input-output relationships in biological nervous tissues contribute to the development of engineering and clinical applications. However, because of the high nonlinearity, the traditional modeling and implementation approaches have difficulties in terms of generalization ability (i.e., performance on reproducing an unknown data) and computational resources. To overcome these difficulties, asynchronous cellular automaton based neuron models has been presented, which are neuron models described as special kinds of cellular automata and can be implemented as small asynchronous sequential logic circuits. This paper presents a novel network of such models, which can mimic input-output relationships of biological and nonlinear ODE model neural networks. Computer simulations confirm that the presented network has a higher generalization ability than another modeling and implementation approach. In addition, brief comparisons of the computational resources for execution and learning shows that the presented network requires less computational resources.

Spike-timing-dependent plasticity (STDP) is a form of synaptic modification that depends on the relative timings of presynaptic and postsynaptic spikes. In this letter, we proposed a calcium-based simple STDP model, described by an ordinary differential equation having only three state variables: one represents the density of intracellular calcium, one represents a fraction of open state NMDARs, and one represents the synaptic weight. We shown that in spite of its simplicity, the model can reproduce the properties of the plasticity that have been experimentally measured in various brain areas (e.g., layer 2/3 and 5 visual cortical slices, hippocampal cultures, and layer 2/3 somatosensory cortical slices) with respect to various patterns of presynaptic and postsynaptic spikes. In addition, comparisons with other STDP models are made, and the significance and advantages of the proposed model are discussed.

A generalized asynchronous cellular automaton-based neuron model is a special kind of cellular automaton that is designed to mimic the nonlinear dynamics of neurons. The model can be implemented as an asynchronous sequential logic circuit and its control parameter is the pattern of wires among the circuit elements that is adjustable after implementation in a field-programmable gate array (FPGA) device. In this paper, a novel theoretical analysis method for the model is presented. Using this method, stabilities of neuron-like orbits and occurrence mechanisms of neuron-like bifurcations of the model are clarified theoretically. Also, a novel learning algorithm for the model is presented. An equivalent experiment shows that an FPGA-implemented learning algorithm enables an FPGA-implemented model to automatically reproduce typical nonlinear responses and occurrence mechanisms observed in biological and model neurons.

The most generalized version of asynchronous sequential logic circuit based neuron models is introduced, where the dynamics of the model is modeled by an asynchronous cellular automaton. In this paper, a new theoretical analysis method is presented, and stabilities of neuron-like orbits and occurrence mechanisms of relational neuron-like bifurcations are clarified theoretically. A synapse unit and a simple compartmental model are also presented, and their functions are confirmed numerically.

A piece-wise constant (ab. PWC) spiking neuron model that can reproduce various neuron-like phenomena including spikings and burstings is presented. Using an analytical iterative map, it is shown that the model can exhibit spikings, burstings, and related bifurcation scenarios, which are qualitatively similar to that of standard neuron models. Also, the bifurcation scenario can be observed in an actual hardware. In addition, a synapse model for the presented neuron model is presented. It is shown that a synapse-coupled system can exhibit various synchronizations that are often observed in synapse-coupled systems of standard neuron models.

A novel spiking neuron model whose nonlinear dynamics is described by an asynchronous cellular automaton is presented. The model can be implemented by a simple digital sequential logic circuit but can exhibit various neuron-like bifurcations and responses. Using the Poincaré mapping technique, it is clarified that the model can reproduce major bifurcation mechanisms of excitabilities and spikings of biological and model neurons. It is also clarified that the model can reproduce major excitatory responses of the neurons.

A novel analog spiking neuron model which has a piece-wise constant (ab. PWC) vector field and can be implemented by a simple electronic circuit is proposed. Using theories on discontinuous ODEs, the dynamics of the proposed model can be reduced into a one-dimensional return map analytically. Using the return map, it is shown that the proposed model can exhibit various neuron-like behaviors and bifurcations. It is also shown that the model can reproduce not only the individual neuron-like behaviors and bifurcations but also relations among them that are typically observed in biological and model neurons.

A novel rotate-and-fire digital spiking neuron is presented. The digital neuron is a wired system of shift registers and thus it is suited to on-chip learning unlike many other analog spiking neuron models. By adjusting the wiring pattern among the registers, the digital neuron can generate spike trains with various spike patterns and can exhibit related bifurcations. A discrete-continuous hybrid map, which describes the neuron dynamics without any approximation, is derived analytically. Using the hybrid map, it is shown that the digital spiking neuron can mimic typical bifurcation phenomena and various nonlinear responses of biological neurons.

A rotate-and-fire digital spiking neuron is a wired system of shift registers that is suitable for electronic circuit implementation and on-chip learning. By adjusting the wiring pattern among the registers, the neuron can generate spike-trains with various spike patterns. An iterative map which can analytically describe the neuron dynamics is derived. Using the map, it is show that the neuron can exhibit a typical neuron-like bifurcation phenomenon and various neuron-like responses such as class I excitability, class II excitability, integrator-type response, and resonator-type response.

A digital spiking neuron is a wired system of shift registers that can generate spike-trains with various spike patterns by adjusting its wiring pattern. In this paper, a novel theoretical synthesis method of the digital neuron that can be applied to spike-pattern division multiplex communications in an artificial pulse-coupled neural network is presented. A novel learning algorithm of the digital neuron for realization of better communication performances is also presented. It is shown that our digital neuron can have some higher flexibilities than a code theoretic spike pattern design method.

A hybrid spiking neuron is a wired system of shift registers and behaves like a neuron model. The neuron exhibits various bifurcation phenomena and response characteristics for a stimulation spike-train input. In this paper we formulate some typical bifurcation mechanisms and clarify these mechanisms by using discrete/continuous hybrid maps. Based on the analysis results, we can clarify mechanisms of various responses of the neuron. In addition, we propose a novel online learning algorithm of the neuron and show that the neuron can reconstruct or approximate response characteristics of another neuron with unknown parameter values.

Inspired by sound encoding mechanisms of the spiral ganglion cell in the mammalian inner ear, in this paper we present a novel chaotic spiking neuron and analyze its encoding function. A set of N neurons accepts a common analog input and outputs a set of N chaotic spike-trains. We give some theorems which guarantee that the set of neurons can encode various inputs (which can be constant, periodic, non-periodic or random) into a summation of their spike-trains in such a way that a spike density of the summed spike-train mimics a waveform of the input.We also confirm the encoding function of an electrical circuit model of the neuron by a SPICE simulation.

The digital spiking neuron is a wired system of shift registers and behaves like a simplified neuron model. By adjusting the wirings among the registers, the neuron can generate various spike-trains. In this paper some basic relations between the wiring pattern and spike-train characteristics are analyzed. Based on the analysis results, a hardware-oriented learning algorithm is proposed. The learning algorithm and the digital neuron are implemented by a hardware description language (HDL). It is shown that the learning algorithm enables the digital neuron to approximate various spike-trains generated by an analog spiking neuron model. In addition, some basic experimental measurements are provided by using a field programmable gate array (FPGA).

This paper studies response of a chaotic spiking circuit to spike-train inputs. Applying input, chaotic behavior is changed into a variety of phenomena and we introduce two interesting phenomena. The first one is "chaos + chaos = order": mixing two periodic inputs each of which causes chaos, the circuit exhibits periodic phenomena. The second one is "consistency": applying some kind of random input, the circuit exhibits identical nonperiodic steady state response for various initial states. We derive return map and analyze these phenomena precisely.

A digital spiking neuron (DSN) consists of shift registers and can generate spike-trains with various patterns of inter-spike intervals. In this paper we present a learning algorithm for the DSN in order to approximate given spike-trains. We study a case where a student DSN accepts a spike-train from a teacher DSN. It is shown that the student can reproduce a spike-train of the teacher based on the learning algorithm. We also study a case where a chaotic analog spiking neuron is used as a teacher. It is shown that the DSN can approximate a sampled chaotic spike-train with a small error.

This paper presents digital three-layer perceptrons (ab. DLPs) having binary connection parameters and consider their application to analysis and synthesis of binary cellular automata (ab. BCAs). Dynamics of BCS is governed by a rule table that is a Boolean function and can cause rich spatiotemporal patterns. Regarding a rule table of a BCA as a teacher signals we apply a GA-based learning algorithm to synthesize a DLP. Performing basic numerical experiments, we suggest the following: 1) The GA-based learning algorithm runs successfully and can realize rule table of BCA. 2) The number of hidden neurons may be an important factor to evaluate complexity of spatiotemporal pattern of BCA. 3) The DLP function can be preserved even if we reduce the teacher signals based on core teacher signals.

A digital spiking neuron can generate spike-trains having various inter-spike intervals (ISIs). Using an ISI-based modulation method, the spike-train can be symbolized by a digital code. We propose fast and rigorous analysis methods for the dynamics of the ISI and characteristics of the modulation. Then we can show that the DSN can encode a given input information into the output spike-train efficiently in a parameter case. Using an FPGA board, generation of a typical spike-train and its modulation can be confirmed in the laboratory.

We propose an extremely simple stabilization method for nonlinear circuits having inductor(s). The method is based on instantaneous and periodic opening of a switch, that causes reset of an inductor current to zero (or constant). As a typical stabilization object, we consider a current-mode H-bridge inverter. It is shown that our stabilization method can stabilize unstable periodic operation of the inverter with extremely rapid transient to a stabilized state. Using a simple test circuit, typical phenomena are confirmed experimentally.

This paper studies superstable phenomena in a simple circuit consisting of two capacitors, two signum voltage-controlled current sources and one time-controlled impulsive switch. The circuit equation defines piecewise constant vector field, the trajectory is piecewise linear, and the embedded return map is piecewise linear: it is well suited for precise analysis. In some parameter range the map has infinite extrema and one flat segment. In this case the circuit exhibits extremely complicated periodic orbits characterized by superstability for initial value, very fast transient and sensitivity for parameters

This paper studies A/D and D/A converters based on spiking neurons. The neurons repeat integrate-and-fire behavior between threshold and base signals and can output a variety of spike-trains. For the A/D conversion, an analog dc input is applied as an initial value and the spike-train is transformed into a digital output via spike-interval modulation. For the D/A conversion, a digital input is applied to switch base level and an analog output is given as phase of final spike within a given time limit. We then present a reconfigurable circuit that can realize both A/D and D/A converters by changing shape of base and threshold waveforms. The circuit operation is confirmed by PSPICE simulation

This paper studies a spiking neuron circuit with a periodic triangular base signal. The circuit can output rich pulse-trains and the dynamics can be analyzed exploiting a piecewise linear one-dimensional pulse position map. Using two neurons we construct a pulse-coupled system whose dynamics can be integrated into the composite map of the pulse position maps of two neurons. The composite map is piecewise linear and we can analyze rich phenomena precisely. For example, periodic behavior of each neuron is changed into chaotic behavior and chaotic behavior of each neuron is changed into periodic behavior. These results provide basic information to construct flexible pulse-coupled neural networks.

We present a novel digital spiking neuron that can generate various spike-trains. Based on a spike position modulation, the neuron can code multiple digital information into a single spike-train. We also present a pulse-coupled network of the neurons to which the coded spike-train is input. The network can retrieve the digital information from the input spike-train, based on a synchronization phenomenon. Applications of the network to multiplex communications are discussed, and typical phenomena are confirmed by an HDL simulation.

This paper studies a simple synthesis algorithm of desired cellular automata (ab. CAs) based on a binary neural network (ab. BNN). The CAs have binary state variable and the BNN has bipolar connection parameters. In order to realize dynamics of the CA, the bipolar parameters of BNN are determined using the genetic algorithm. Performing basic numerical experiments we show that the BNN can realize desired dynamics with small number of hidden layers for a class of CAs. We also consider the case of noisy teacher signals. These results provide basic information for application to signal processing, analysis of digital nonlinear phenomena, and so on.

We present a pulse-coupled network (PCN) of spiking oscillators (SOCs) which can be implemented as a simple electrical circuit. The SOC has a periodic reset level that can realize rich dynamics represented by chaotic spike-trains. Applying a spike-train input, the PCN can exhibit the following interesting phenomena. 1) Each SOC synchronizes with a part of the input without overlapping, i.e., the input is decomposed. 2) Some SOCs synchronize with a part of the input with overlapping, i.e., the input is decomposed and the SOCs are clustered. The PCN has multiple synchronization phenomena and exhibits one of them depending on the initial state. We clarify the numbers of the synchronization phenomena and the parameter regions in which these phenomena can be observed. Also stability of the synchronization phenomena is clarified. Presenting a simple test circuit, typical phenomena are confirmed experimentally.

We present a pulse-coupled network of integrate-and-fire circuits that have refractoriness. The network can be implemented by a simple electrical circuit, and various periodic synchronization phenomena are observed in the laboratory. The phenomena are characterized by a ratio of phase locking. Using a return map having a trapping window, the ratio can be analyzed in a parameter subspace rigorously. We clarify effects of the refractoriness on pulse coding ability of the network.

A quantized spiking neuron has various co-existing periodic spike-trains and generates one of them depending on the initial state. An information coding method for the spike-trains is introduced based on pulse interval modulation. We clarify correspondence between the set of code sequences in a parameter subspace. Also, a pulse-coupled network is introduced and its application to multiplex communications is considered.

Presents a pulse-coupled network (PCN) and considers its basic functions in terms of electronic circuits. The PCN consists of integrate-and-fire oscillators (IFO) coupled inhibitory to each other, and can accept an external input via excitatory coupling. When the input is given by mixing some independent pulse-trains, the PCN exhibits an interesting winner-take-all function and the original pulse-trains can be recovered. An efficient test circuit is implemented and the classification function can be verified in the laboratory.

This paper presents a novel analog-to-digital converter with a window (ab. WADC) that can approximate a DC input by Farey series: a set of fractions with variable denominators. The WADC can be implemented by a simple continuous time circuit including a dependent switched capacitor. The error characteristics can be given analytically and the efficient performance of the WADC can be guaranteed theoretically.

This paper presents N binary hysteresis relaxation oscillators connected by the intermittently coupled capacitors. The network exhibits various interesting synchronous phenomena. As a powerful analysis tool, we derive a hybrid return map which has one real and N binary states. Using this map, the stability of the periodic synchronization can be analyzed simply by the binary states: it is not necessary to check the real state. Then we classify basic phenomena in a bifurcation diagram. Typical phenomena are verified in the laboratory.

This paper presents a buck-boost converter controlled by periodic inputs and provides the basic theoretical results, As an efficient analysis tool, we derive a slope chart that displays the relationship between slopes of the return map and those of the inputs. Applying the tool for the case of triangular inputs, we can give a sufficient condition for chaos generation. We then give a method to realize uniform distribution of the chaotic switching time intervals for any DC output: it might be a criterion to improve EMC. A method to realize super stable periodic behavior is also given.

Introduces a resonance phenomenon from an integrate-and-fire model (IFM) with plural periodic inputs. When two inputs are applied, the IFM usually outputs aperiodic pulse-trains. However when adjusting the parameters of an input, the IFM outputs a periodic pulse-train with a constant pulse interval, while the state is quasi-periodic. This resonance phenomenon was confirmed in the laboratory, and analyzed using the mapping procedure.

This paper discusses occasional linear connection (ab. OLC) that realizes in-phase, lagging phase and leading phase master-slave synchronization of chaos. Also, occasional proportional feedback (ab. OPF) is a basic version of OLC and can stabilize desired UPO in chaos. The OLC and OPF can realize various spatiotemporal dynamics. An implementation example of the OLC is also shown.