22 papers · ranked by Valyu relevance
Anthony Zador, Sean Escola, Blake Richards, Bence Ölveczky + 23 more
Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at…
Jenelle Feather, Meenakshi Khosla, N. Apurva Ratan Murty, Aran Nayebi
What makes an artificial system a good model of intelligence? The classical test proposed by Alan Turing focuses on behavior, requiring that an artificial agent's behavior be indistinguishable from that of a human. While behavioral similarity provides a strong starting point, two systems with very different internal…
Dhruvik Patel, Md. Sayed Tanveer, Jesus Gonzalez-Ferrer, Alon Loeffler + 3 more
NeuroAI is an emerging field at the intersection of neuroscience and artificial intelligence, where insights from brain function guide the design of intelligent systems. A central area within this field is synthetic biological intelligence (SBI), which combines the adaptive learning properties of biological neural…
Dhruvik Patel, Md Sayed Tanveer, Jesus Gonzalez-Ferrer, Alon Loeffler + 3 more
NeuroAI is an emerging field at the intersection of neuroscience and artificial intelligence, where insights from brain function guide the design of intelligent systems. A central area within this field is synthetic biological intelligence (SBI), which combines the adaptive learning properties of biological neural…
Afifah Kashif, Abdul Muhsin Hameed, Asim Iqbal
Current AI governance frameworks, including regulatory benchmarks for accuracy, latency, and energy efficiency, are built for static, centrally trained artificial neural networks on von Neumann hardware. NeuroAI systems, embodied in neuromorphic hardware and implemented via spiking neural networks, break these…
Ida Momennejad
Researchers across cognitive, neuro-, and computer sciences increasingly reference "human-like" artificial intelligence and "neuroAI". However, the scope and use of the terms are often inconsistent. Contributed research ranges widely from mimicking behaviour, to testing machine learning methods as neurally plausible…
Andrea I. Luppi, Jascha Achterberg, Samuel Schmidgall, Isil Poyraz Bilgin + 12 more
At this critical juncture in the development of NeuroAI, we outline challenges and training needs of junior researchers working across AI and neuroscience. We also provide advice and resources to help trainees plan their NeuroAI careers.
Qiande Zhao, Junhai Xu, Deying Li, Xia Wu + 3 more
NeuroAI develops the interplay of neuroscience and artificial intelligence, especially on visual processing. Human visual system organizes objects based on a representational hierarchy. However, it remains unclear whether this hierarchy arises from visual or semantic information. One hypothesis posits that the visual…
Anthony M. Zador, Blake A. Richards, Bence P. Ölveczky, G. Sean Escola + 23 more
'G. Sean Escola' 'Yoshua Bengio' 'Kwabena Boahen' 'Matthew Botvinick' 'Dmitri B. Chklovskii' 'Anne K. Churchland' 'Claudia Clopath' 'James J. DiCarlo' 'Surya Ganguli' 'Jeff Hawkins' 'Konrad Koerding' 'Alexei A. Koulakov' 'Yann LeCun' 'Timothy Lillicrap' 'Adam Marblestone' 'Bruno A. Olshausen' 'Alexandre Pouget'…
Alexander D. Boyd, Kamal Sen
Modern automatic speech recognition (ASR) systems are capable of impressive performance recognizing clean speech but struggle in noisy, multi-talker environments, commonly referred to as the “cocktail party problem.” In contrast, many human listeners can solve this problem, suggesting the existence of a solution in the…
Mutiyat Usman, Simachew Ashebir, Chioma Okey-Mbata, Yeoheung Yun + 1 more
Neurodegenerative diseases, including Alzheimer’s disease (AD) and Parkinson’s disease (PD), present a growing public health challenge globally. Recent advancements in neurotechnology and neuroengineering have significantly enhanced brain-computer interfaces, artificial intelligence, and organoid technologies, making…
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…
Michael A. Arbib
After noting the cybernetic origins of Kybernetik/ Biological Cybernetics, we respond to the Editorial by Fellous et al. (2025) and then analyze talks from the NIH BRAIN NeuroAI 2024 Workshop to get one “snapshot” of the state of the conversation between Artificial intelligence (AI) and brain theory (BT). Key…
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…
Marcel van Gerven
New developments in AI and neuroscience are revitalizing the quest to understanding natural intelligence, offering insight about how to equip machines with human-like capabilities. This paper reviews some of the computational principles relevant for understanding natural intelligence and, ultimately, achieving strong…
Authors not listed
Artificial intelligence (AI) is reshaping scientific research by accelerating discovery and enabling the analysis of complex data that traditional methods struggle to handle. This review examines over 310,000 journal articles and patents from the CAS Content Collection (2015–2025), with a focus on, biomedical research…
Ittai Shamir, Yaniv Assaf
Advancements in neuroscience and artificial intelligence have been fueling one another for decades. In this study, we integrate a neuroimaging model of laminar-level connectomics into a biologically-inspired deep learning model of recurrent neural networks (RNNs) for working memory tasks. The resulting model offers a…
Authors not listed
The rapid progress in Artificial Intelligence (AI) has led to extraordinary achievements across various domains, significantly impacting every aspect of daily life. This advancement is also revolutionizing research in numerous scientific areas, particularly within bioinformatics, chemistry, pharmaceuticals, and…
Authors not listed
X-ray diffraction (XRD) is an immediate and powerful characterization technique that provides detailed information on the lattice structure and long-range order in crystalline materials. In recent decades, the quality and quantity of available crystal structure data has exploded, in large part due to the advent of…
Jonathan Zaslavsky, Pauric Bannigan, Christine Allen
Introduction Interest in nanomedicines has surged in recent years due to the critical role they have played in the COVID-19 pandemic. Nanoformulations can turn promising therapeutic cargo into viable products through improvements in drug safety and efficacy profiles. However, the developmental pathway for such…
Long Qian, Xin Lu, Parvez Haris, Jianyong Zhu + 2 more
Clinical trials are crucial for drug development, but they require significant time and financial resources. Additionally, uncertainties may arise during these trials concerning their results due to concerns surrounding effectiveness, safety, or the enrollment of participants. If robust AI (artificial intelligence)…
Authors not listed
This article proposes a three-level classification of artificial intelligence (AI) application in chemical sciences, reflecting the increasing degree of technology involvement in scientific and production processes: from automation of routine tasks (the level of "AI Assistant"), to the creation of specialized…