24 papers · ranked by Valyu relevance
Giacomo Indiveri, Shih‐Chii Liu
—A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within…
Catherine D. Schuman, Thomas E. Potok, Robert M. Patton, J.D. Birdwell + 3 more
'J.D. Birdwell' 'Mark E. Dean' 'Garrett S. Rose' 'James S. Plank'] Abstract—Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. This biologically inspired approach has created highly connected synthetic…
Michael Pfeiffer, Thomas Pfeil
Spiking neural networks (SNNs) are inspired by information processing in biology, where sparse and asynchronous binary signals are communicated and processed in a massively parallel fashion. SNNs on neuromorphic hardware exhibit favorable properties such as low power consumption, fast inference, and event-driven…
Jennifer Hasler, Bo Marr
Neuromorphic systems are gaining increasing importance in an era where CMOS digital computing techniques are reaching physical limits. These silicon systems mimic extremely energy efficient neural computing structures, potentially both for solving engineering applications as well as understanding neural computation.…
Bipin Rajendran, Abu Sebastian, Michael Schmuker, Narayan Srinivasa + 1 more
'Evangelos Eleftheriou'] Machine learning has emerged as the dominant tool for implementing complex cognitive tasks that require supervised, unsupervised, and reinforcement learning. While the resulting machines have demonstrated in some cases even super-human performance, their energy consumption has often proved to…
Maryada, Chiara De Luca, Arianna Rubino, Chenxi Wen + 4 more
Cortical microcircuits play a fundamental role in natural intelligence. While they inspired a wide range neural computation models and artificial intelligence algorithms, few attempts have been made to directly emulate them with an electronic computational substrate that uses the same physics of computation. Here we…
Zhengguang Zhu, Nicholas Schaffer, Xiao Yang
Neuromorphic devices are bioinspired electronic systems that mimic key structures and functions of the nervous system, enabling integration and communication between living tissues and machines. This review examines how neuromorphic devices and computing are designed to emulate the structure, organization, and function…
Dmitry Ivanov, Aleksandr Chezhegov, Mikhail Kiselev, Andrey Grunin + 1 more
'Denis Larionov'] Modern artificial intelligence (AI) systems, based on von Neumann architecture and classical neural networks, have a number of fundamental limitations in comparison with the mammalian brain. In this article we discuss these limitations and ways to mitigate them. Next, we present an overview of…
Phu Khanh Huynh, M. Lakshmi Varshika, Ankita Paul, Murat Işık + 2 more
'Adarsha Balaji' 'Anup Das'] Abstract. Recently, both industry and academia have proposed several different neuromorphic systems to execute machine learning applications that are designed using Spiking Neural Networks (SNNs). With the growing complexity on design and technology fronts, programming such systems to admit…
Maryada, Saray Soldado-Magraner, Martino Sorbaro, Rodrigo Laje + 2 more
Many neural computations emerge from self-sustained patterns of activity in recurrent neural circuits, which rely on balanced excitation and inhibition. Neuromorphic electronic circuits that use the physics of silicon to emulate neuronal dynamics represent a promising approach for implementing the brain’s computational…
Francesca Borghi, Thierry R. Nieus, Davide E. Galli, Paolo Milani
The brain’s ability to perform efficient and fault-tolerant data processing is strongly related to its peculiar interconnected adaptive architecture, based on redundant neural circuits interacting at different scales. By emulating the brain’s processing and learning mechanisms, computing technologies strive to achieve…
Asim Iqbal, Hassan Mahmood, Greg J. Stuart, Gord Fishell + 1 more
Understanding the computational principles of the brain and replicating them on neuromorphic hardware and modern deep learning architectures is crucial for advancing neuro-inspired AI (NeuroAI). Here, we develop an experimentally-constrained biophysical network model of neocortical circuit motifs, focusing on layers…
Dmitrii Zendrikov, Sergio Solinas, Giacomo Indiveri
Neuromorphic processing systems implementing spiking neural networks with mixed signal analog/digital electronic circuits and/or memristive devices represent a promising technology for edge computing applications that require low power, low latency, and that cannot connect to the cloud for off-line processing, either…
Fatemeh Hadaeghi
This chapter provides a comprehensive survey of the researches and motivations for hardware implementation of reservoir computing (RC) on neuromorphic electronic systems. Due to its computational efficiency and the fact that training amounts to a simple linear regression, both spiking and non-spiking implementations of…
Davide Cipollini, Hugh Greatorex, Michele Mastella, Elisabetta Chicca + 1 more
In an era characterized by the rapid growth of data processing, developing new and efficient data processing technologies has become a priority. We address this by proposing a novel type of neuromorphic technology we call Fused-MemBrain. Our proposal is inspired by Golgi's theory modeling the brain as a syncytial…
Loïc J. Azzalini, Milad Lankarany
Neuromorphic chips are well-suited for the exploration of neuronal dynamics in (near) real-time. In order to port existing research onto these chips, relevant models of neuronal and synaptic dynamics first need to be supported by their respective development environments and validated against existing simulator…
Felix L. Hoch, Qishen Wang, Kian-Guan Lim, Desmond K. Loke
Title: Highlights 1. The review emphasizes the switching mechanisms of organic neuromorphic materials. 2. In addition to these switching mechanisms, the capabilities of organic neuromorphic materials in tunable, conformable, and low-power applications, e.g., neuromorphic computing, neuroscience, and neuromorphic…
Dmitry Ivanov, Aleksandr Chezhegov, Andrey Grunin, М. Г. Киселев + 1 more
'Denis Larionov'] Modern AI systems, based on von Neumann architecture and classical neural networks, have a number of fundamental limitations in comparison with the brain. This article discusses such limitations and the ways they can be mitigated. Next, it presents an overview of currently available neuromorphic AI…
Andrey E. Schegolev, M. V. Bastrakova, Michael A. Sergeev, A. A. Maksimovskaya + 2 more
'A. A. Maksimovskaya' 'N. V. Klenov' 'I. I. Soloviev'] The extensive development of the field of spiking neural networks has led to many areas of research that have a direct impact on people's lives. As the most bio-similar of all neural networks, spiking neural networks not only allow the solution of recognition and…
Hongwei Cai, Zheng Ao, Chunhui Tian, Zhuhao Wu + 5 more
Brain-inspired hardware emulates the structure and working principles of a biological brain and may address the hardware bottleneck for fast-growing artificial intelligence (AI). Current brain-inspired silicon chips are promising but still limit their power to fully mimic brain function for AI computing. Here, we…
Romain Beaubois, Jérémy Cheslet, Tomoya Duenki, Farad Khoyratee + 3 more
Characterization and modeling of biological neural networks has emerged as a field driving significant advancements in our understanding of brain function and related pathologies. As of today, pharmacological treatments for neurological disorders remain limited, pushing the exploration of promising alternative…
Authors not listed
A memristor is a two-terminal electronic component that modify its conductance state depending on how much charge has passed through it previously. Halide perovskites are materials recently employed for neuromorphic computing in this type of resistive switches. Their performance is rapidly improving, yet the activation…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Authors not listed
Two-dimensional Prussian blue and its analogues hold great promise for applications in catalysis, energy conversion, sensing, and memory devices, thanks to their open frameworks, surface activity, and directional ion transport. However, synthesizing high-quality and large-area two dimensional films remains a major…