Toward Reflective Spiking Neural Networks Exploiting Memristive Devices

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Makarov, Valeri A. and Lobov, Sergey A. and Shchanikov, Sergey and Mikhaylov, Alexey and Kazantsev, Viktor B. (2022) Toward Reflective Spiking Neural Networks Exploiting Memristive Devices. Frontiers in Computational Neuroscience, 16 . ISSN 1662-5188

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Official URL: https://doi.org/10.3389/fncom.2022.859874




Abstract

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.


Item Type:Article
Uncontrolled Keywords:Spiking neural networks (SNNs): Memristors and memristive systems; High-dimensional brain; Plasticity; Reflective systems
Subjects:Sciences > Mathematics > Operations research
Medical sciences > Medicine > Neurosciences
Medical sciences > Biology > Biomathematics
ID Code:74480
Deposited On:08 Sep 2022 16:10
Last Modified:09 Sep 2022 08:07

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