20 papers · ranked by Valyu relevance
Shiva Subbulakshmi Radhakrishnan, Amritanand Sebastian, Aaryan Oberoi, Sarbashis Das + 1 more
Spiking neural networks (SNNs) promise to bridge the gap between artificial neural networks (ANNs) and biological neural networks (BNNs) by exploiting biologically plausible neurons that offer faster inference, lower energy expenditure, and event-driven information processing capabilities. However, implementation of…
Prasanna Date, Shruti Kulkarni, Aaron Young, Catherine D. Schuman + 2 more
'Thomas E. Potok' 'Jeffrey S. Vetter'] Abstract—Neuromorphic computers perform computations by emulating the human brain, and use extremely low power. They are expected to be indispensable for energy-efficient computing in the future. While they are primarily used in spiking neural network-based machine learning…
Prasanna Date, Shruti Kulkarni, Aaron Young, Catherine Schuman + 2 more
'Thomas Potok' 'Jeffrey Vetter'] Neuromorphic computers emulate the human brain while being extremely power efficient for computing tasks. In fact, they are poised to be critical for energy-efficient computing in the future. Neuromorphic computers are primarily used in spiking neural network-based machine learning…
Aadhitiya VS, Jani Babu Shaik, Sonal Singhal, Siona Menezes Picardo + 1 more
'Nilesh Goel'] Abstract— Neuromorphic computing systems emulate the electrophysiological behavior of the biological nervous system using mixed-mode analog or digital VLSI circuits. These systems show superior accuracy and power efficiency in carrying out cognitive tasks. The neural network architecture used in…
Filippo Costa, Chiara De Luca
The encoder has low model complexity, relying on a shallow network of heterogeneous neurons. It relies on an internal time reference, allowing for continuous processing. Moreover, stimulus parameters can be linearly decoded from the spiking patterns, granting fast information retrieval. Our approach, validated on both…
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…
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…
Pedro Félix, Maria-Teresa Jurado-Parras, Joel Freitas, João Ventura + 2 more
Real-time closed-loop neuromodulation, in which stimulation is precisely timed to ongoing brain dynamics, holds transformative potential for treating neurological disorders and probing neural circuit function. However, it requires low-latency, energy-efficient processing of high-bandwidth neural signals that…
Lopez-Randulfe, Javier, Reeb, Nico + 2 more
Processing sensor data with spiking neural networks on digital neuromorphic chips requires converting continuous analog signals into spike pulses. Two strategies are promising for achieving low energy consumption and fast processing speeds in end-to-end neuromorphic applications. First, to directly encode analog…
Stefano Panzeri, Ella Janotte, Alejandro Pequeño-Zurro, Jacopo Bonato + 1 more
'Jacopo Bonato' 'Chiara Bartolozzi'] In the brain, information is encoded, transmitted and used to inform behaviour at the level of timing of action potentials distributed over population of neurons. To implement neural-like systems in silico, to emulate neural function, and to interface successfully with the brain…
Wiktoria Agata Pawlak, Newton Howard
Neuromorphic computing technologies are about to change modern computing, yet most work thus far has emphasized hardware development. This review focuses on the latest progress in algorithmic advances specifically for potential use in brain implants. We discuss current algorithms and emerging neurocomputational models…
Anna-Maria Jürgensen, Afshin Khalili, Elisabetta Chicca, Giacomo Indiveri + 1 more
Animal nervous systems are highly efficient in processing sensory input. The neuromorphic computing paradigm aims at the hardware implementation of neural network computations to support novel solutions for building brain-inspired computing systems. Here, we take inspiration from sensory processing in the nervous…
Zhengdi Sun, Anle Mu, Fuxiang Hao, Hang Wang + 1 more
Neuromorphic technologies are attracting increasing interest in neuroengineering, as they provide an event-driven, spike-based computational framework that is well suited to temporally structured, sparse, and resource-constrained biological systems. Compared with conventional computing pipelines, neuromorphic…
Jim Bartels, Olympia Gallou, Hiroyuki Ito, Matthew Cook + 3 more
Long-term monitoring of biomedical signals is essential for the modern clinical management of neurological conditions such as epilepsy. However, developing wearable systems that are able to monitor, analyze, and detect epileptic seizures with long-lasting operation times using current technologies is still an open…
Joël Küchler, Katarina Vulić, Haotian Yao, Christian Valmaggia + 3 more
We present an in vitro neuronal network with controlled topology capable of performing basic Boolean computations, such as NAND and OR. Neurons cultured within polydimethylsiloxane (PDMS) microstructures on high-density microelectrode arrays (HD-MEAs) enable precise interaction through extracellular voltage stimulation…
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…
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
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
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…
Cailum Stienstra, Liam Hebert, Patrick Thomas, Alexander Haack + 2 more
Given that Infrared (IR) spectroscopy is a crucial tool in various chemical and forensic domains, improved in silico methods for predicting experimental spectra are needed due to the time and accuracy limitations of ab initio methods. We employ Graphormer, a graph neural network (GNN) transformer, to predict IR spectra…