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Biomimetics
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Neuromorphic Engineering: Biomimicry from the Brain
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Neuromorphic Engineering: Biomimicry from the Brain

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Bioinspired Sensorics, Information Processing and Control".

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 8996

Special Issue Editor

Dr. Ella M. Gale

E-Mail Website
Guest Editor
School of Chemistry, University of Bristol, Bristol, UK
Interests: neural networks; memristors; materials science; neuromorphic engineering; unconventional/natural computing; machine learning/AI

Special Issue Information

Dear Colleagues,

Historically, the computing industry has relied on Moore’s law’s promises of computation speeds doubling every few years to keep up with the demands of humanity. As we approach the limits of simply shrinking silicon chips further, this law no longer holds true. Furthermore, the ‘just throw more transistors at the problem’ approach is increasingly energy-inefficient in a climate which demands ever-greener solutions. These problems combine to illustrate that there is a clear need for computer components which can do ‘more than Moore’, and for less, through the application of fundamentally different architectural approaches.

The brain exemplifies an adaptive, robust, resilient, highly featured, general-purpose computational platform that competes with, and in many tasks outstrips, the finest supercomputers of our day, and does so on about 12 W of power (about two modern smartphones). We have long known that biomimetic approaches proffer great advantages, and recent advances in bioinspired/biomimetic disciplines, such as neural networks, have shown us new ways to leverage these.

This Special Issue calls for papers in all areas of neuromorphic engineering, including novel materials, innovative hardware and software approaches and neurological discoveries that suggest new engineering patterns. Areas like spiking neural networks, convolutional neural networks, recurrent neural networks, memristors, ReRAM, robotics, learning systems, bioinspired and biomimetic engineering, artificial intelligence, unconventional/natural computing, symbolic logic processing, ionic materials and related disciplines are of interest. Hardware, software and hybrid approaches are welcomed. Special consideration will be given to submissions demonstrating efficacy on real-world problems, environments or data. 

Dr. Ella M. Gale
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomimetics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • materials science
  • neuromorphic engineering
  • biomimetics
  • unconventional computing
  • neural networks
  • memristors
  • ReRAM
  • robotics
  • learning systems

Published Papers (6 papers)

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Research

16 pages, 6506 KiB  
Article
Artificial Neural Network Model with Astrocyte-Driven Short-Term Memory
by Ilya A. Zimin, Victor B. Kazantsev and Sergey V. Stasenko
Biomimetics 2023, 8(5), 422; https://doi.org/10.3390/biomimetics8050422 - 12 Sep 2023
Cited by 1 | Viewed by 1389
Abstract
In this study, we introduce an innovative hybrid artificial neural network model incorporating astrocyte-driven short-term memory. The model combines a convolutional neural network with dynamic models of short-term synaptic plasticity and astrocytic modulation of synaptic transmission. The model’s performance was evaluated using simulated [...] Read more.
In this study, we introduce an innovative hybrid artificial neural network model incorporating astrocyte-driven short-term memory. The model combines a convolutional neural network with dynamic models of short-term synaptic plasticity and astrocytic modulation of synaptic transmission. The model’s performance was evaluated using simulated data from visual change detection experiments conducted on mice. Comparisons were made between the proposed model, a recurrent neural network simulating short-term memory based on sustained neural activity, and a feedforward neural network with short-term synaptic depression (STPNet) trained to achieve the same performance level as the mice. The results revealed that incorporating astrocytic modulation of synaptic transmission enhanced the model’s performance. Full article
(This article belongs to the Special Issue Neuromorphic Engineering: Biomimicry from the Brain)
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14 pages, 3342 KiB  
Article
Triphenylamine-Based Helical Polymer for Flexible Memristors
by Jinyong Li, Minglei Gong, Xiaoyang Wang, Fei Fan and Bin Zhang
Biomimetics 2023, 8(5), 391; https://doi.org/10.3390/biomimetics8050391 - 26 Aug 2023
Viewed by 950
Abstract
Flexible nonvolatile memristors have potential applications in wearable devices. In this work, a helical polymer, poly (N, N-diphenylanline isocyanide) (PPIC), was synthesized as the active layer, and flexible electronic devices with an Al/PPIC/ITO architecture were prepared on a polyethylene terephthalate (PET) substrate. The [...] Read more.
Flexible nonvolatile memristors have potential applications in wearable devices. In this work, a helical polymer, poly (N, N-diphenylanline isocyanide) (PPIC), was synthesized as the active layer, and flexible electronic devices with an Al/PPIC/ITO architecture were prepared on a polyethylene terephthalate (PET) substrate. The device showed typical nonvolatile rewritable memristor characteristics. The high-molecular-weight helical structure stabilized the active layer under different bending degrees, bending times, and number of bending cycles. The memristor was further employed to simulate the information transmission capability of neural fibers, providing new perspectives for the development of flexible wearable memristors and biomimetic neural synapses. This demonstration highlights the promising possibilities for the advancement of artificial intelligence skin and intelligent flexible robots in the future. Full article
(This article belongs to the Special Issue Neuromorphic Engineering: Biomimicry from the Brain)
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15 pages, 5480 KiB  
Article
Polysilicon-Channel Synaptic Transistors for Implementation of Short- and Long-Term Memory Characteristics
by Myung-Hyun Baek and Hyungjin Kim
Biomimetics 2023, 8(4), 368; https://doi.org/10.3390/biomimetics8040368 - 15 Aug 2023
Cited by 2 | Viewed by 1145
Abstract
The rapid progress of artificial neural networks (ANN) is largely attributed to the development of the rectified linear unit (ReLU) activation function. However, the implementation of software-based ANNs, such as convolutional neural networks (CNN), within the von Neumann architecture faces limitations due to [...] Read more.
The rapid progress of artificial neural networks (ANN) is largely attributed to the development of the rectified linear unit (ReLU) activation function. However, the implementation of software-based ANNs, such as convolutional neural networks (CNN), within the von Neumann architecture faces limitations due to its sequential processing mechanism. To overcome this challenge, research on hardware neuromorphic systems based on spiking neural networks (SNN) has gained significant interest. Artificial synapse, a crucial building block in these systems, has predominantly utilized resistive memory-based memristors. However, the two-terminal structure of memristors presents difficulties in processing feedback signals from the post-synaptic neuron, and without an additional rectifying device it is challenging to prevent sneak current paths. In this paper, we propose a four-terminal synaptic transistor with an asymmetric dual-gate structure as a solution to the limitations of two-terminal memristors. Similar to biological synapses, the proposed device multiplies the presynaptic input signal with stored synaptic weight information and transmits the result to the postsynaptic neuron. Weight modulation is explored through both hot carrier injection (HCI) and Fowler–Nordheim (FN) tunneling. Moreover, we investigate the incorporation of short-term memory properties by adopting polysilicon grain boundaries as temporary storage. It is anticipated that the devised synaptic devices, possessing both short-term and long-term memory characteristics, will enable the implementation of various novel ANN algorithms. Full article
(This article belongs to the Special Issue Neuromorphic Engineering: Biomimicry from the Brain)
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15 pages, 3724 KiB  
Article
Detection and Dispersion Analysis of Water Globules in Oil Samples Using Artificial Intelligence Algorithms
by Alexey N. Beskopylny, Anton Chepurnenko, Besarion Meskhi, Sergey A. Stel’makh, Evgenii M. Shcherban’, Irina Razveeva, Alexey Kozhakin, Kirill Zavolokin and Andrei A. Krasnov
Biomimetics 2023, 8(3), 309; https://doi.org/10.3390/biomimetics8030309 - 13 Jul 2023
Cited by 1 | Viewed by 1247
Abstract
Fluid particle detection technology is of great importance in the oil and gas industry for improving oil-refining techniques and in evaluating the quality of refining equipment. The article discusses the process of creating a computer vision algorithm that allows the user to detect [...] Read more.
Fluid particle detection technology is of great importance in the oil and gas industry for improving oil-refining techniques and in evaluating the quality of refining equipment. The article discusses the process of creating a computer vision algorithm that allows the user to detect water globules in oil samples and analyze their sizes. The process of developing an algorithm based on the convolutional neural network (CNN) YOLOv4 is presented. For this study, our own empirical base was proposed, which comprised microphotographs of samples of raw materials and water–oil emulsions taken at various points and in different operating modes of an oil refinery. The number of images for training the neural network algorithm was increased by applying the authors’ augmentation algorithm. The developed program makes it possible to detect particles in a fluid medium with the level of accuracy required by a researcher, which can be controlled at the stage of training the CNN. Based on the results of processing the output data from the algorithm, a dispersion analysis of localized water globules was carried out, supplemented with a frequency diagram describing the ratio of the size and number of particles found. The evaluation of the quality of the results of the work of the intelligent algorithm in comparison with the manual method on the verification microphotographs and the comparison of two empirical distributions allow us to conclude that the model based on the CNN can be verified and accepted for use in the search for particles in a fluid medium. The accuracy of the model was AP@50 = 89% and AP@75 = 78%. Full article
(This article belongs to the Special Issue Neuromorphic Engineering: Biomimicry from the Brain)
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14 pages, 922 KiB  
Article
Model of Neuromorphic Odorant-Recognition Network
by Sergey V. Stasenko, Alexey N. Mikhaylov and Victor B. Kazantsev
Biomimetics 2023, 8(3), 277; https://doi.org/10.3390/biomimetics8030277 - 28 Jun 2023
Cited by 3 | Viewed by 1036
Abstract
We propose a new model for a neuromorphic olfactory analyzer based on memristive synapses. The model comprises a layer of receptive neurons that perceive various odors and a layer of “decoder” neurons that recognize these odors. It is demonstrated that connecting these layers [...] Read more.
We propose a new model for a neuromorphic olfactory analyzer based on memristive synapses. The model comprises a layer of receptive neurons that perceive various odors and a layer of “decoder” neurons that recognize these odors. It is demonstrated that connecting these layers with memristive synapses enables the training of the “decoder” layer to recognize two types of odorants of varying concentrations. In the absence of such synapses, the layer of “decoder” neurons does not exhibit specificity in recognizing odorants. The recognition of the ’odorant’ occurs through the neural activity of a group of decoder neurons that have acquired specificity for the odorant in the learning process. The proposed phenomenological model showcases the potential use of a memristive synapse in practical odorant recognition applications. Full article
(This article belongs to the Special Issue Neuromorphic Engineering: Biomimicry from the Brain)
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23 pages, 5226 KiB  
Article
A Conductance-Based Silicon Synapse Circuit
by Ashish Gautam and Takashi Kohno
Biomimetics 2022, 7(4), 246; https://doi.org/10.3390/biomimetics7040246 - 16 Dec 2022
Cited by 1 | Viewed by 2184
Abstract
Neuron, synapse, and learning circuits inspired by the brain comprise the key components of a neuromorphic chip. In this study, we present a conductance-based analog silicon synapse circuit suitable for the implementation of reduced or multi-compartment neuron models. Compartmental models are more bio-realistic. [...] Read more.
Neuron, synapse, and learning circuits inspired by the brain comprise the key components of a neuromorphic chip. In this study, we present a conductance-based analog silicon synapse circuit suitable for the implementation of reduced or multi-compartment neuron models. Compartmental models are more bio-realistic. They are implemented in neuromorphic chips aiming to mimic the electrical activities of the neuronal networks in the brain and incorporate biomimetic soma and synapse circuits. Most contemporary low-power analog synapse circuits implement bioinspired “current-based” synaptic models suited for the implementation of single-compartment point neuron models. They emulate the exponential decay profile of the synaptic current, but ignore the effect of the postsynaptic membrane potential on the synaptic current. This dependence is necessary to emulate shunting inhibition, which is thought to play important roles in information processing in the brain. The proposed circuit uses an oscillator-based resistor-type element at its output stage to incorporate this effect. This circuit is used to demonstrate the shunting inhibition phenomenon. Next, to demonstrate that the oscillatory nature of the induced synaptic current has no unforeseen effects, the synapse circuit is employed in a spatiotemporal spike pattern detection task. The task employs the adaptive spike-timing-dependent plasticity (STDP) learning rule, a bio-inspired learning rule introduced in a previous study. The mixed-signal chip is designed in a Taiwan Manufacturing Semiconductor Company 250 nm complementary metal oxide semiconductor technology node. It comprises a biomimetic soma circuit and 256 synapse circuits, along with their learning circuitries. Full article
(This article belongs to the Special Issue Neuromorphic Engineering: Biomimicry from the Brain)
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