Bioinspired mechano-photonic artificial synapse based on graphene/MoS2 heterostructure

Developing multifunctional and diversified artificial neural systems to integrate multimodal plasticity, memory, and supervised learning functions is an important task toward the emulation of neuromorphic computation. Here, we present a bioinspired mechano-photonic artificial synapse with synergistic mechanical and optical plasticity. The artificial synapse is composed of an optoelectronic transistor based on graphene/MoS2 heterostructure and an integrated triboelectric nanogenerator. By controlling the charge transfer/exchange in the heterostructure with triboelectric potential, the optoelectronic synaptic behaviors can be readily modulated, including postsynaptic photocurrents, persistent photoconductivity, and photosensitivity. The photonic synaptic plasticity is elaborately investigated under the synergistic effect of mechanical displacement and the light pulses embodying different spatiotemporal information. Furthermore, artificial neural networks are simulated to demonstrate the improved image recognition accuracy up to 92% assisted with mechanical plasticization. The mechano-photonic artificial synapse is highly promising for implementing mixed-modal interaction, emulating complex biological nervous system, and promoting the development of interactive artificial intelligence.

Some approaches to creating hybrid knowledge-oriented systems by embedding artificial neural networks

The article presents an overview of information systems that combine the advantages of artificial neural systems and systems with knowledge bases, focused on working with knowledge. Ways to improve their efficiency by using ANN to form hypotheses in the process of finding a solution are considered.

Computations by different types of brain, and by artificial neural systems

In this Chapter a comparison is made between computations in the brain and computations performed in computers. This is intended to be helpful to those engineers, computer scientists, AI specialists et al interested in designing new computers that emulate aspects of brain function. In fact, the whole of this book is intended to be useful for this aim, by setting out what is computed by different brain systems, and what we know about how it is computed. It is essential to know this if an emulation of brain function is to be performed, and this is important to enable this group of scientists to bring their expertise to help understand brain function more. The Chapter also considers the levels of investigation, which include the computational, necessary to understand brain function; and some applications of this understanding, to for example how our developing understanding is relevant to understanding disorders, including for example of food intake control leading to obesity. Finally, Section 19.10 makes it clear why the focus of this book is on computations in primate (and that very much includes human) brains, rather than on rodent (rat and mice) brains. It is because the systems-level organization of primate including human brains is quite different from that in rodents, in many fundamental ways that are described.

Biologically-Inspired Learning

A general overview of biologically-inspired learning in the paradigm of artificial neural systems is described. In order to have the reader become familiar with fundamentals underlying this paradigm, a substantial and concise hierarchical background from neurophysiology to neuro-computational models is provided with as much clarity as possible. As an application of a well-known artificial neural network algorithm called feed-forward multi-layer perceptron with back-propagation training algorithm is utilized in implementation of an artificial olfactory system also called electronic nose. For improved classification performance, an algorithm as a preprocessing called linear-discriminant analysis is adapted to chosen neural architecture. The main purpose of the preprocessing stage is to lend better scattered input patterns for classes in the feature space compared to that without preprocessing. The performance improvement is also investigated in terms convergence rate, i.e. the number of iteration, given a number of layers, and recalling or generalization capability of the classifier.

Artificial Neural Systems for Robots

This chapter reviews the use of neural systems in robotics, with particular emphasis on strongly biologically inspired neural networks and methods. As well as describing work at the research frontiers, the paper provides some historical background in order to clarify the motivations and scope of work in this field. There are two major sections that make up the bulk of the chapter: one surveying the application of artificial neural systems to robot control, and one describing the use of robots as tools in neuroscience. The former concentrates on biologically derived neural architectures and methods used to drive robot behaviours, and the latter introduces a closely related area of research where robotic models are used as tools to study neural mechanisms underlying the generation of adaptive behaviour in animals and humans.