Convergence of artificial intelligence and neuroscience towards the diagnosis of neurological disorders—a scoping review
C Surianarayanan, JJ Lawrence, PR Chelliah… - Sensors, 2023 - mdpi.com
Artificial intelligence (AI) is a field of computer science that deals with the simulation of
human intelligence using machines so that such machines gain problem-solving and …
human intelligence using machines so that such machines gain problem-solving and …
Nanoelectronics Using Metal–Insulator Transition
Metal–insulator transition (MIT) coupled with an ultrafast, significant, and reversible resistive
change in Mott insulators has attracted tremendous interest for investigation into next …
change in Mott insulators has attracted tremendous interest for investigation into next …
A discrete memristive neural network and its application for character recognition
S He, J Liu, H Wang, K Sun - Neurocomputing, 2023 - Elsevier
Abstract Design of artificial neural networks based on memristor has attracted increasing
attentions from researchers. However, there are no reports on the discrete memristor based …
attentions from researchers. However, there are no reports on the discrete memristor based …
N‐P Reconfigurable Dual‐Mode Memtransistors for Compact Bio‐Inspired Feature Extractor with Inhibitory‐Excitatory Spiking Capability
Competitive‐learning‐based spiking neural networks are capable of rapid, highly accurate
pattern recognition with minimal data through denoising mechanisms provide by adaptive …
pattern recognition with minimal data through denoising mechanisms provide by adaptive …
Spiking neural networks based on two-dimensional materials
JB Roldan, D Maldonado… - npj 2D Materials and …, 2022 - nature.com
The development of artificial neural networks using memristors is gaining a lot of interest
among technological companies because it can reduce the computing time and energy …
among technological companies because it can reduce the computing time and energy …
Implementation of convolutional neural networks in memristor crossbar arrays with binary activation and weight quantization
We propose a hardware-friendly architecture of a convolutional neural network using a 32×
32 memristor crossbar array having an overshoot suppression layer. The gradual switching …
32 memristor crossbar array having an overshoot suppression layer. The gradual switching …
LTMD: learning improvement of spiking neural networks with learnable thresholding neurons and moderate dropout
Abstract Spiking Neural Networks (SNNs) have shown substantial promise in processing
spatio-temporal data, mimicking biological neuronal mechanisms, and saving computational …
spatio-temporal data, mimicking biological neuronal mechanisms, and saving computational …
Recent advances in synaptic nonvolatile memory devices and compensating architectural and algorithmic methods toward fully integrated neuromorphic chips
Nonvolatile memory (NVM)‐based neuromorphic computing has been attracting
considerable attention from academia and the industry. Although it is not completely …
considerable attention from academia and the industry. Although it is not completely …
Emerging of two-dimensional materials in novel memristor
The rapid development of big-data analytics (BDA), internet of things (IoT) and artificial
intelligent Technology (AI) demand outstanding electronic devices and systems with faster …
intelligent Technology (AI) demand outstanding electronic devices and systems with faster …
Integrated memristor network for physiological signal processing
Humans are complex organisms made by millions of physiological systems. Therefore,
physiological activities can represent physical or mental states of the human body …
physiological activities can represent physical or mental states of the human body …