Oscillatory threshold logic

Neural flip-flops I: Short-term memory

The networks proposed here show how neurons can be connected to form flip-flops, the basic building blocks in sequential logic systems. The novel neural flip-flops (NFFs) are explicit, dynamic, and can generate known phenomena of short-term memory. For each network design, all neurons, connections, and types of synapses are shown explicitly. The neurons' operation depends only on explicitly stated, minimal properties of excitement and inhibition. This operation is dynamic in the sense that the level of neuron activity is the only cellular change, making the NFFs' operation consistent with the speed of most brain functions. Memory tests have shown that certain neurons fire continuously at a high frequency while information is held in short-term memory. These neurons exhibit seven characteristics associated with memory formation, retention, retrieval, termination, and errors. One of the neurons in each of the NFFs produces all of the characteristics. This neuron and a second neighboring neuron together predict eight unknown phenomena. These predictions can be tested by the same methods that led to the discovery of the first seven phenomena. NFFs, together with a decoder from a previous paper, suggest a resolution to the longstanding controversy of whether short-term memory depends on neurons firing persistently or in brief, coordinated bursts. Two novel NFFs are composed of two and four neurons. Their designs follow directly from a standard electronic flip-flop design by moving each negation symbol from one end of the connection to the other. This does not affect the logic of the network, but it changes the logic of each component to a logic function that can be implemented by a single neuron. This transformation is reversible and is apparently new to engineering as well as neuroscience.

Neuron-like spiking and bursting in Josephson junctions: A review

The superconducting Josephson junction shows spiking and bursting behaviors, which have similarities with neuronal spiking and bursting. This phenomenon had been observed long ago by some researchers; however, they overlooked the biological similarity of this particular dynamical feature and never attempted to interpret it from the perspective of neuronal dynamics. In recent times, the origin of such a strange property of the superconducting junction has been explained and such neuronal functional behavior has also been observed in superconducting nanowires. The history of this research is briefly reviewed here with illustrations from studies of two junction models and their dynamical interpretation in the sense of biological bursting.

Thermal switch of oscillation frequency in Belousov-Zhabotinsky liquid marbles

External control of oscillation dynamics in the Belousov-Zhabotinsky (BZ) reaction is important for many applications including encoding computing schemes. When considering the BZ reaction, there are limited studies dealing with thermal cycling, particularly cooling, for external control. Recently, liquid marbles (LMs) have been demonstrated as a means of confining the BZ reaction in a system containing a solid-liquid interface. BZ LMs were prepared by rolling 50 μl droplets in polyethylene (PE) powder. Oscillations of electrical potential differences within the marble were recorded by inserting a pair of electrodes through the LM powder coating into the BZ solution core. Electrical potential differences of up to 100 mV were observed with an average period of oscillation ca 44 s. BZ LMs were subsequently frozen to -1°C to observe changes in the frequency of electrical potential oscillations. The frequency of oscillations reduced upon freezing to 11 mHz cf. 23 mHz at ambient temperature. The oscillation frequency of the frozen BZ LM returned to 23 mHz upon warming to ambient temperature. Several cycles of frequency fluctuations were able to be achieved.

Critical neuromorphic computing based on explosive synchronization

Synchronous oscillations in neuronal ensembles have been proposed to provide a neural basis for the information processes in the brain. In this work, we present a neuromorphic computing algorithm based on oscillator synchronization in a critical regime. The algorithm uses the high-dimensional transient dynamics perturbed by an input and translates it into proper output stream. One of the benefits of adopting coupled phase oscillators as neuromorphic elements is that the synchrony among oscillators can be finely tuned at a critical state. Especially near a critical state, the marginally synchronized oscillators operate with high efficiency and maintain better computing performances. We also show that explosive synchronization that is induced from specific neuronal connectivity produces more improved and stable outputs. This work provides a systematic way to encode computing in a large size coupled oscillator, which may be useful in designing neuromorphic devices.

Towards fungal computer

We propose that fungi Basidiomycetes can be used as computing devices: information is represented by spikes of electrical activity, a computation is implemented in a mycelium network and an interface is realized via fruit bodies. In a series of scoping experiments, we demonstrate that electrical activity recorded on fruits might act as a reliable indicator of the fungi's response to thermal and chemical stimulation. A stimulation of a fruit is reflected in changes of electrical activity of other fruits of a cluster, i.e. there is distant information transfer between fungal fruit bodies. In an automaton model of a fungal computer, we show how to implement computation with fungi and demonstrate that a structure of logical functions computed is determined by mycelium geometry.

Strange nonchaotic attractors for computation

We investigate the response of quasiperiodically driven nonlinear systems exhibiting strange nonchaotic attractors (SNAs) to deterministic input signals. We show that if one uses two square waves in an aperiodic manner as input to a quasiperiodically driven double-well Duffing oscillator system, the response of the system can produce logical output controlled by such a forcing. Changing the threshold or biasing of the system changes the output to another logic operation and memory latch. The interplay of nonlinearity and quasiperiodic forcing yields logical behavior, and the emergent outcome of such a system is a logic gate. It is further shown that the logical behaviors persist even for an experimental noise floor. Thus the SNA turns out to be an efficient tool for computation.

Double-Strand Breaks in Genome-Sized DNA Caused by Ultrasound

DNA double-strand breaks (DSBs) caused by ultrasound were evaluated in a quantitative manner by single-molecule fluorescence microscopy. We compared the effect of time-interval (or pulse) sonication to that of continuous wave (CW) sonication at a fixed frequency of 30 kHz. Pulses caused fewer DSBs than CW sonication under the same total input ultrasound energy when the pulse repetition period was above the order of a second. In contrast, pulses caused more DSBs than CW sonication for pulse widths shorter than a second. These effect of ultrasound on DNA were interpreted in terms of the time-dependent decay in the probability of breakage during the duration of a pulse. We propose a simple phenomenological model by considering a characteristic decay in the probability of DSBs during single-pulse sonication, which reproduces the essence of the experimental trend. In addition, a data analysis revealed a characteristic scaling behavior between the number of pulses and the number of DSBs.

Multicluster oscillation death and chimeralike states in globally coupled Josephson Junctions

We observe the multiclustered oscillation death and chimeralike states in an array of Josephson junctions under a combination of self-repulsive and cross-attractive mean-field interaction when each isolated junction is in a bistable state, a coexisting fixed point and an oscillatory state. We locate the parameter landscape of the multiclustered oscillation death and chimeralike states. Alternatively, a purely repulsive mean-field interaction in an array of all oscillatory junctions produces chimeralike states with signatures of metastability in the incoherent subpopulation of junctions.

Coherent libration to coherent rotational dynamics via chimeralike states and clustering in a Josephson junction array

An array of excitable Josephson junctions under a global mean-field interaction and a common periodic forcing shows the emergence of two important classes of coherent dynamics, librational and rotational motion, in the weaker and stronger coupling limits, respectively, with transitions to chimeralike states and clustered states in the intermediate coupling range. In this numerical study, we use the Kuramoto complex order parameter and introduce two measures, a libration index and a clustering index, to characterize the dynamical regimes and their transitions and locate them in a parameter plane.

Discrimination of time-dependent inflow properties with a cooperative dynamical system

Many physical, chemical, and biological systems exhibit a cooperative or sigmoidal response with respect to the input. In biochemistry, such behavior is called an allosteric effect. Here, we demonstrate that a system with such properties can be used to discriminate the amplitude or frequency of an external periodic perturbation. Numerical simulations performed for a model sigmoidal kinetics illustrate that there exists a narrow range of frequencies and amplitudes within which the system evolves toward significantly different states. Therefore, observation of system evolution should provide information about the characteristics of the perturbation. The discrimination properties for periodic perturbation are generic. They can be observed in various dynamical systems and for different types of periodic perturbation.

Bursting dynamics in a population of oscillatory and excitable Josephson junctions

We report an emergent bursting dynamics in a globally coupled network of mixed population of oscillatory and excitable Josephson junctions. The resistive-capacitive shunted junction (RCSJ) model of the superconducting device is considered for this study. We focus on the parameter regime of the junction where its dynamics is governed by the saddle-node on invariant circle (SNIC) bifurcation. For a coupling value above a threshold, the network splits into two clusters when a reductionism approach is applied to reproduce the bursting behavior of the large network. The excitable junctions effectively induce a slow dynamics on the oscillatory units to generate parabolic bursting in a broad parameter space. We reproduce the bursting dynamics in a mixed population of dynamical nodes of the Morris-Lecar model.

Processing of sub- and supra-second intervals in the primate brain results from the calibration of neuronal oscillators via sensory, motor, and feedback processes

The processing of time intervals in the sub- to supra-second range by the brain is critical for the interaction of primates with their surroundings in activities, such as foraging and hunting. For an accurate processing of time intervals by the brain, representation of physical time within neuronal circuits is necessary. I propose that time dimension of the physical surrounding is represented in the brain by different types of neuronal oscillators, generating spikes or spike bursts at regular intervals. The proposed oscillators include the pacemaker neurons, tonic inputs, and synchronized excitation and inhibition of inter-connected neurons. Oscillators, which are built inside various circuits of brain, help to form modular clocks, processing time intervals or other temporal characteristics specific to functions of a circuit. Relative or absolute duration is represented within neuronal oscillators by "neural temporal unit," defined as the interval between regularly occurring spikes or spike bursts. Oscillator output is processed to produce changes in activities of neurons, named frequency modulator neuron, wired within a separate module, represented by the rate of change in frequency, and frequency of activities, proposed to encode time intervals. Inbuilt oscillators are calibrated by (a) feedback processes, (b) input of time intervals resulting from rhythmic external sensory stimulation, and (c) synchronous effects of feedback processes and evoked sensory activity. A single active clock is proposed per circuit, which is calibrated by one or more mechanisms. Multiple calibration mechanisms, inbuilt oscillators, and the presence of modular connections prevent a complete loss of interval timing functions of the brain.

Unipolar memristive switching in bulk negative temperature coefficient thermosensitive ceramics

A memristive phenomenon was observed in macroscopic bulk negative temperature coefficient nickel monoxide (NiO) ceramic material. Current-voltage characteristics of memristors, pinched hysteretic loops were systematically observed in the Ag/NiO/Ag cell. A thermistor-based model for materials with negative temperature coefficient was proposed to explain the mechanism of the experimental phenomena. Most importantly, the results demonstrate the potential for a realization of memristive systems based on macroscopic bulk materials.

A DNA-based semantic fusion model for remote sensing data

Semantic technology plays a key role in various domains, from conversation understanding to algorithm analysis. As the most efficient semantic tool, ontology can represent, process and manage the widespread knowledge. Nowadays, many researchers use ontology to collect and organize data's semantic information in order to maximize research productivity. In this paper, we firstly describe our work on the development of a remote sensing data ontology, with a primary focus on semantic fusion-driven research for big data. Our ontology is made up of 1,264 concepts and 2,030 semantic relationships. However, the growth of big data is straining the capacities of current semantic fusion and reasoning practices. Considering the massive parallelism of DNA strands, we propose a novel DNA-based semantic fusion model. In this model, a parallel strategy is developed to encode the semantic information in DNA for a large volume of remote sensing data. The semantic information is read in a parallel and bit-wise manner and an individual bit is converted to a base. By doing so, a considerable amount of conversion time can be saved, i.e., the cluster-based multi-processes program can reduce the conversion time from 81,536 seconds to 4,937 seconds for 4.34 GB source data files. Moreover, the size of result file recording DNA sequences is 54.51 GB for parallel C program compared with 57.89 GB for sequential Perl. This shows that our parallel method can also reduce the DNA synthesis cost. In addition, data types are encoded in our model, which is a basis for building type system in our future DNA computer. Finally, we describe theoretically an algorithm for DNA-based semantic fusion. This algorithm enables the process of integration of the knowledge from disparate remote sensing data sources into a consistent, accurate, and complete representation. This process depends solely on ligation reaction and screening operations instead of the ontology.