New computing technologies inspired by the brain promise fundamentally different ways to process information with extreme energy efficiency and the ability to handle the avalanche of unstructured and noisy data that we are generating at an ever-increasing rate. To realize this promise requires a brave and coordinated plan to bring together disparate research communities and to provide them with the funding, focus and support needed. We have done this in the past with digital technologies; we are in the process of doing it with quantum technologies; can we now do it for brain-inspired computing?
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http://dx.doi.org/10.1038/s41586-021-04362-w | DOI Listing |
Nano Converg
January 2025
Bendable Electronics and Sustainable Technologies (BEST) Group, Electrical and Computer Engineering Department, Northeastern University, Boston, MA, 02115, USA.
The intriguing way the receptors in biological skin encode the tactile data has inspired the development of electronic skins (e-skin) with brain-inspired or neuromorphic computing. Starting with local (near sensor) data processing, there is an inherent mechanism in play that helps to scale down the data. This is particularly attractive when one considers the huge data produced by large number of sensors expected in a large area e-skin such as the whole-body skin of a robot.
View Article and Find Full Text PDFPNAS Nexus
January 2025
School of Physical Science and Engineering, Tongji University, Shanghai 200092, P. R. China.
Wiring patterns of brain networks embody a trade-off between information transmission, geometric constraints, and metabolic cost, all of which must be balanced to meet functional needs. Geometry and wiring economy are crucial in the development of brains, but their impact on artificial neural networks (ANNs) remains little understood. Here, we adopt a wiring cost-controlled training framework that simultaneously optimizes wiring efficiency and task performance during structural evolution of sparse ANNs whose nodes are located at arbitrary but fixed positions.
View Article and Find Full Text PDFACS Appl Mater Interfaces
January 2025
Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
Implantable memristors are considered an emerging electronic technology that can simulate brain memory function and demonstrate some promising applications in the biomedical field. However, it remains a critical challenge to enhance their long-term stability and biocompatibility in implantation environments. In this work, an implantable memristor has been successfully fabricated based on TiO using magnetron sputtering.
View Article and Find Full Text PDFNeural Netw
January 2025
Tsinghua University, Beijing, China. Electronic address:
Artificial neural networks (ANNs) can help camera-based remote photoplethysmography (rPPG) in measuring cardiac activity and physiological signals from facial videos, such as pulse wave, heart rate and respiration rate with better accuracy. However, most existing ANN-based methods require substantial computing resources, which poses challenges for effective deployment on mobile devices. Spiking neural networks (SNNs), on the other hand, hold immense potential for energy-efficient deep learning owing to their binary and event-driven architecture.
View Article and Find Full Text PDFMol Psychiatry
January 2025
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
To understand the neural mechanism of autism spectrum disorder (ASD) and developmental delay/intellectual disability (DD/ID) that can be associated with ASD, it is important to investigate individuals at an early stage with brain, behavioural and also genetic measures, but such research is still lacking. Here, using the cross-sectional sMRI data of 1030 children under 8 years old, we employed developmental normative models to investigate the atypical development of gray matter volume (GMV) asymmetry in individuals with ASD without DD/ID, ASD with DD/ID and individuals with only DD/ID, and their associations with behavioral and clinical measures and transcription profiles. By extracting the individual deviations of patients from the typical controls with normative models, we found a commonly abnormal pattern of GMV asymmetry across all ASD children: more rightward laterality in the inferior parietal lobe and precentral gyrus, and higher individual variability in the temporal pole.
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