Emerging Artificial Neuron Devices for Probabilistic Computing

In recent decades, artificial intelligence has been successively employed in the fields of finance, commerce, and other industries. However, imitating high-level brain functions, such as imagination and inference, pose several challenges as they are relevant to a particular type of noise in a biological neuron network. Probabilistic computing algorithms based on restricted Boltzmann machine and Bayesian inference that use silicon electronics have progressed significantly in terms of mimicking probabilistic inference. However, the quasi-random noise generated from additional circuits or algorithms presents a major challenge for silicon electronics to realize the true stochasticity of biological neuron systems. Artificial neurons based on emerging devices, such as memristors and ferroelectric field-effect transistors with inherent stochasticity can produce uncertain non-linear output spikes, which may be the key to make machine learning closer to the human brain. In this article, we present a comprehensive review of the recent advances in the emerging stochastic artificial neurons (SANs) in terms of probabilistic computing. We briefly introduce the biological neurons, neuron models, and silicon neurons before presenting the detailed working mechanisms of various SANs. Finally, the merits and demerits of silicon-based and emerging neurons are discussed, and the outlook for SANs is presented.

A bio-inspired bistable recurrent cell allows for long-lasting memory

Recurrent neural networks (RNNs) provide state-of-the-art performances in a wide variety of tasks that require memory. These performances can often be achieved thanks to gated recurrent cells such as gated recurrent units (GRU) and long short-term memory (LSTM). Standard gated cells share a layer internal state to store information at the network level, and long term memory is shaped by network-wide recurrent connection weights. Biological neurons on the other hand are capable of holding information at the cellular level for an arbitrary long amount of time through a process called bistability. Through bistability, cells can stabilize to different stable states depending on their own past state and inputs, which permits the durable storing of past information in neuron state. In this work, we take inspiration from biological neuron bistability to embed RNNs with long-lasting memory at the cellular level. This leads to the introduction of a new bistable biologically-inspired recurrent cell that is shown to strongly improves RNN performance on time-series which require very long memory, despite using only cellular connections (all recurrent connections are from neurons to themselves, i.e. a neuron state is not influenced by the state of other neurons). Furthermore, equipping this cell with recurrent neuromodulation permits to link them to standard GRU cells, taking a step towards the biological plausibility of GRU. With this link, this work paves the way for studying more complex and biologically plausible neuromodulation schemes as gating mechanisms in RNNs.

CONTROLE DE CAOS NO MODELO NEURONAL DE HINDMARSH-ROSE COM PARÂMETROS INCERTOS

In bioengineering there is a great motivation in studying the Hindmarsh-Rose (HR) neuron model due to the fact that it represents well the biological neuron, making it possible to simulate several behaviors of a real neuron, including periodic, aperiodic and chaotic behaviors, for example. Based on this model, this article proposes applying a linear optimal control design to the uncertain and chaotic behavior established by changes in the parameters of the system. To do so, the mathematical system of the RH model and its chaotic behavior are presented; afterwards, the fixed parametersare replaced by uncertain ones, and the chaotic dynamics of the system is investigated. At last, the linear optimal control is proposed as a method for controlling the chaotic behavior of the model, and numerical simulations are presented to show the efficiency of this proposal.

Comprehensive Introduction to Neural Networks

Unknowingly, artificial intelligence (AI) has become an inevitable part of our lives. In this chapter, the authors discuss how the neural networks, a sub-part of AI, changed the way we analyse things. In this chapter, the advent of neural networks, inspiration from the human brain, simplification models of biological neuron models are discussed. Later, a detailed overview of various neural network models, their strengths, limitations, applications, and challenges are presented in detail.

Effect of Initial Synaptic State on Pattern Classification Accuracy of 3D Vertical Resistive Random Access Memory (VRRAM) Synapses

Amidst the considerable attention artificial intelligence (AI) has attracted in recent years, a neuromorphic chip that mimics the biological neuron has emerged as a promising technology. Memristor or Resistive random-access memory (RRAM) is widely used to implement a synaptic device. Recently, 3D vertical RRAM (VRRAM) has become a promising candidate to reducing resistive memory bit cost. This study investigates the operation principle of synapse in 3D VRRAM architecture. In these devices, the classification response current through a vertical pillar is set by applying a training algorithm to the memristors. The accuracy of neural networks with 3D VRRAM synapses was verified by using the HSPICE simulator to classify the alphabet in 7×7 character images. This simulation demonstrated that 3D VRRAMs are usable as synapses in a neural network system and that a 3D VRRAM synapse should be designed to consider the initial value of the memristor to prepare the training conditions for high classification accuracy. These results mean that a synaptic circuit using 3D VRRAM will become a key technology for implementing neural computing hardware.

Design of Artificial Neuron Network with Synapse Utilizing Hybrid CMOS Transistors with Memristor for Low Power Applications

Neural networks are mimetic with biological neuron which are employed on digital computers. These networks are designed with CMOS technology using 0.45[Formula: see text][Formula: see text]m in cadence virtuoso. The scaling of CMOS limits parameters like power consumption, area and parallelism. To overcome the limitations, a nanoscale, nonvolatile Memristor device is used to design the synapses. The proposed network is designed for neuron synapse networks implemented with a memristor device. This network is compared with neuron linked with CMOS synapse. The proposed network has low power consumption, high spike frequency, and low delay value. The spike frequency of Memristor synapse increases by 65.51% when compared with the existing CMOS synapse and power consumption is reduced to 52.79%. The delay is reduced to 0.294[Formula: see text][Formula: see text]s. The simulation results are carried using Specter.

ESTUDO DO COMPORTAMENTO DINÂMICO DO MODELO NEURONAL DE HINDMARSH-ROSE

Based on the Hindmarsh-Rose (RH) neuronal model for nerve impulse transmission, this paper aims to study the properties and dynamic behavior of the non-linear chaotic system that describes neuronal bursting in a single neuron. On the part of bioengineering, there is great motivation in the study of the HR model because it is well representative of the biological neuron, being able to simulate several behaviors of a real neuron, among them periodic, aperiodic and chaotic behavior. The literature suggests that the chaotic behaviorrepresents in the human being the epileptic or convulsive state. Through computer simulations, considering the system parameters, it was analyzed that the stability is highly sensitive to the initial conditions and producing oscillations, more so, when the oscillation increases the random behavior tends to increase making the system unpredictable.