Memristive Concept of a High-Dimensional Neuron

Shchanikov, S.A.; Bordanov, Ilya; Belov, A. I.; Королев, Д. С.; Shamshin, Maxim; Gryaznov, E.G.; Kazantsev, Victor B.; Mikhaylov, A. N.; Makarov, Valeri A.

doi:10.1109/cnn53494.2021.9580310

Cited by 2 publications

(1 citation statement)

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“…Recent advances use simplified architectures of neurons and include the concept of the high-dimensional brain (Section “Novel Mathematical Principles for Spiking Neural Networks: Concept Cells and High-Dimensional Brain”), which explains the unreasonable efficiency of single cognitively specialized neurons. The system consists of software and hardware parts and is controlled by a microcontroller ( Shchanikov et al, 2021 ). Memristive devices based on a metal-oxide-metal thin-film structure, where yttrium-stabilized zirconium dioxide acts as a switching medium, can implement adaptable synaptic weights of a high-dimensional neuron ( Mikhaylov et al, 2020 ).…”

Section: The Memristive Architecture Enables the Implementation Of Re...mentioning

confidence: 99%

Toward Reflective Spiking Neural Networks Exploiting Memristive Devices

Makarov

Lobov

Shchanikov

et al. 2022

Front. Comput. Neurosci.

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The design of modern convolutional artificial neural networks (ANNs) composed of formal neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy, where receptive fields become increasingly more complex and coding sparse. Nowadays, ANNs outperform humans in controlled pattern recognition tasks yet remain far behind in cognition. In part, it happens due to limited knowledge about the higher echelons of the brain hierarchy, where neurons actively generate predictions about what will happen next, i.e., the information processing jumps from reflex to reflection. In this study, we forecast that spiking neural networks (SNNs) can achieve the next qualitative leap. Reflective SNNs may take advantage of their intrinsic dynamics and mimic complex, not reflex-based, brain actions. They also enable a significant reduction in energy consumption. However, the training of SNNs is a challenging problem, strongly limiting their deployment. We then briefly overview new insights provided by the concept of a high-dimensional brain, which has been put forward to explain the potential power of single neurons in higher brain stations and deep SNN layers. Finally, we discuss the prospect of implementing neural networks in memristive systems. Such systems can densely pack on a chip 2D or 3D arrays of plastic synaptic contacts directly processing analog information. Thus, memristive devices are a good candidate for implementing in-memory and in-sensor computing. Then, memristive SNNs can diverge from the development of ANNs and build their niche, cognitive, or reflective computations.

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Section: The Memristive Architecture Enables the Implementation Of Re...mentioning

confidence: 99%