Short-term memory of TiO2-based electrochemical capacitors: empirical analysis with adoption of a sliding threshold.

Chemical synapses are important components of the large-scaled neural network in the hippocampus of the mammalian brain, and a change in their weight is thought to be in charge of learning and memory. Thus, the realization of artificial chemical synapses is of crucial importance in achieving artificial neural networks emulating the brain's functionalities to some extent. This kind of research is often referred to as neuromorphic engineering. In this study, we report short-term memory behaviours of electrochemical capacitors (ECs) utilizing TiO2 mixed ionic-electronic conductor and various reactive electrode materials e.g. Ti, Ni, and Cr. By experiments, it turned out that the potentiation behaviours did not represent unlimited growth of synaptic weight. Instead, the behaviours exhibited limited synaptic weight growth that can be understood by means of an empirical equation similar to the Bienenstock-Cooper-Munro rule, employing a sliding threshold. The observed potentiation behaviours were analysed using the empirical equation and the differences between the different ECs were parameterized.

Scalable excitatory synaptic circuit design using floating gate based leaky integrators.

We propose a scalable synaptic circuit realizing spike timing dependent plasticity (STDP)-compatible with randomly spiking neurons. The feasible working of the circuit was examined by circuit simulation using the BSIM 4.6.0 model. A distinguishable feature of the circuit is the use of floating-gate integrators that provide the compact implementation of biologically plausible relaxation time scale. This relaxation occurs on the basis of charge tunneling that mainly relies upon area-independent tunnel barrier properties (e.g. barrier width and height) rather than capacitance. The circuit simulations feature (i) weight-dependent STDP that spontaneously limits the synaptic weight growth, (ii) competitive synaptic adaptation within both unsupervised and supervised frameworks with randomly spiking neurons. The estimated power consumption is merely 34 pW, perhaps meeting one of the most crucial principles (power-efficiency) of neuromorphic engineering. Finally, a means of fine-tuning the STDP behavior is provided.

Leaky Integrate-and-Fire Neuron Circuit Based on Floating-Gate Integrator.

The artificial spiking neural network (SNN) is promising and has been brought to the notice of the theoretical neuroscience and neuromorphic engineering research communities. In this light, we propose a new type of artificial spiking neuron based on leaky integrate-and-fire (LIF) behavior. A distinctive feature of the proposed FG-LIF neuron is the use of a floating-gate (FG) integrator rather than a capacitor-based one. The relaxation time of the charge on the FG relies mainly on the tunnel barrier profile, e.g., barrier height and thickness (rather than the area). This opens up the possibility of large-scale integration of neurons. The circuit simulation results offered biologically plausible spiking activity (<100 Hz) with a capacitor of merely 6 fF, which is hosted in an FG metal-oxide-semiconductor field-effect transistor. The FG-LIF neuron also has the advantage of low operation power (<30 pW/spike). Finally, the proposed circuit was subject to possible types of noise, e.g., thermal noise and burst noise. The simulation results indicated remarkable distributional features of interspike intervals that are fitted to Gamma distribution functions, similar to biological neurons in the neocortex.

Relaxation oscillator-realized artificial electronic neurons, their responses, and noise.

A proof-of-concept relaxation oscillator-based leaky integrate-and-fire (ROLIF) neuron circuit is realized by using an amorphous chalcogenide-based threshold switch and non-ideal operational amplifier (op-amp). The proposed ROLIF neuron offers biologically plausible features such as analog-type encoding, signal amplification, unidirectional synaptic transmission, and Poisson noise. The synaptic transmission between pre- and postsynaptic neurons is achieved through a passive synapse (simple resistor). The synaptic resistor coupled to the non-ideal op-amp realizes excitatory postsynaptic potential (EPSP) evolution that evokes postsynaptic neuron spiking. In an attempt to generalize our proposed model, we theoretically examine ROLIF neuron circuits adopting different non-ideal op-amps having different gains and slew rates. The simulation results indicate the importance of gain in postsynaptic neuron spiking, irrespective of the slew rate (as long as the rate exceeds a particular value), providing the basis for the ROLIF neuron circuit design. Eventually, the behavior of a postsynaptic neuron in connection to multiple presynaptic neurons via synapses is highlighted in terms of EPSP evolution amid simultaneously incident asynchronous presynaptic spikes, which in fact reveals an important role of the random noise in spatial integration.

Chameleonic electrochemical metallization cells: dual-layer solid electrolyte-inducing various switching behaviours.

We present 'unusual' resistive switching behaviours in electrochemical metallization (ECM) cells utilizing a dual-layer (SiOx/GeSex: SiOx on GeSex) solid electrolyte (SE). The observed switching behaviour markedly varies with the thickness of the upper SiOx layer and compliance current: (i) monostable switching, (ii) counter-eightwise bipolar switching, and (iii) combination of monostable and eightwise bipolar switching behaviours. Focusing on cases (i) and (iii), electrical and chemical analyses on these chameleonic cells were performed in an attempt to gain clues to the understanding of the observed complexity. The chemical analysis indicated the upper SiOx layer as a chemical potential well for Cu ions-Cu ions were largely confined in the well. This non-uniform distribution of Cu across the SE perhaps hints at the mechanism for the complex behaviour; it may be a 'zero-sum game' between SiOx and GeSex layers, in which the two layers fight over the limited number of Cu atoms/ions.

Electric-field-induced shift in the threshold voltage in LaAlO3/SrTiO3 heterostructures.

The two-dimensional electron gas (2DEG) at the interface between insulating LaAlO3 and SrTiO3 is intriguing both as a fundamental science topic and for possible applications in electronics or sensors. For example, because the electrical conductance of the 2DEG at the LaAlO3/SrTiO3 interface can be tuned by applying an electric field, new electronic devices utilizing the 2DEG at the LaAlO3/SrTiO3 interface could be possible. For the implementation of field-effect devices utilizing the 2DEG, determining the on/off switching voltage for the devices and ensuring their stability are essential. However, the factors influencing the threshold voltage have not been extensively investigated. Here, we report the voltage-induced shift of the threshold voltage of Pt/LaAlO3/SrTiO3 heterostructures. A large negative voltage induces an irreversible positive shift in the threshold voltage. In fact, after the application of such a large negative voltage, the original threshold voltage cannot be recovered even by application of a large positive electric field. This irreversibility is attributed to the generation of deep traps near the LaAlO3/SrTiO3 interface under the negative voltage. This finding could contribute to the implementation of nanoelectronic devices using the 2DEG at the LaAlO3/SrTiO3 interface.

Reliability of neuronal information conveyed by unreliable neuristor-based leaky integrate-and-fire neurons: a model study.

We conducted simulations on the neuronal behavior of neuristor-based leaky integrate-and-fire (NLIF) neurons. The phase-plane analysis on the NLIF neuron highlights its spiking dynamics--determined by two nullclines conditional on the variables on the plane. Particular emphasis was placed on the operational noise arising from the variability of the threshold switching behavior in the neuron on each switching event. As a consequence, we found that the NLIF neuron exhibits a Poisson-like noise in spiking, delimiting the reliability of the information conveyed by individual NLIF neurons. To highlight neuronal information coding at a higher level, a population of noisy NLIF neurons was analyzed in regard to probability of successful information decoding given the Poisson-like noise of each neuron. The result demonstrates highly probable success in decoding in spite of large variability--due to the variability of the threshold switching behavior--of individual neurons.

Multiprotocol-induced plasticity in artificial synapses.

We suggest a 'universal' electrical circuit for the realization of an artificial synapse that exhibits long-term plasticity induced by different protocols. The long-term plasticity of the artificial synapse is basically attributed to the nonvolatile resistance change of the bipolar resistive switch in the circuit. The synaptic behaviour realized by the circuit is termed 'universal' inasmuch as (i) the shape of the action potential is not required to vary so as to implement different plasticity-induction behaviours, activity-dependent plasticity (ADP) and spike-timing-dependent plasticity (STDP), (ii) the behaviours satisfy several essential features of a biological chemical synapse including firing-rate and spike-timing encoding and unidirectional synaptic transmission, and (iii) both excitatory and inhibitory synapses can be realized using the same circuit but different diode polarity in the circuit. The feasibility of the suggested circuit as an artificial synapse is demonstrated by conducting circuit calculations and the calculation results are introduced in comparison with biological chemical synapses.

Elastic resistance change and action potential generation of non-faradaic Pt/TiO2/Pt capacitors.

Electric current in the mixed ionic-electronic conductor TiO2 is hysteretic, i.e. history-dependent, and its use is versatile in electronic devices. Nowadays, biologically inspired, analogue-type computing systems, known as neuromorphic systems, are being actively investigated owing to their new and intriguing physical concepts. The realization of artificial synapses is important for constructing neuromorphic systems. In mammalians' brains, the plasticity of synapses between neighbouring nerve cells arises from action potential firing. Emulating action potential firing via inorganic systems has therefore become important in neuromorphic engineering. In this work, the current-voltage hysteresis of TiO2-based non-faradaic capacitors is investigated to primarily focus on the correlation between the blocking contact and the elasticity, i.e. non-plasticity, of the capacitors' resistance change, in experimental and theoretical methods. The similarity between the action potential firing behaviour in nerve cells and the elasticity of the non-faradaic capacitors is addressed.