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Onciul R, Tataru CI, Dumitru AV, Crivoi C, Serban M, Covache-Busuioc RA, Radoi MP, Toader C. Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications. J Clin Med 2025; 14:550. [PMID: 39860555 PMCID: PMC11766073 DOI: 10.3390/jcm14020550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2024] [Revised: 01/10/2025] [Accepted: 01/14/2025] [Indexed: 01/27/2025] Open
Abstract
The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding of the brain, unlocking new possibilities in research, diagnosis, and therapy. This review explores how AI's cutting-edge algorithms-ranging from deep learning to neuromorphic computing-are revolutionizing neuroscience by enabling the analysis of complex neural datasets, from neuroimaging and electrophysiology to genomic profiling. These advancements are transforming the early detection of neurological disorders, enhancing brain-computer interfaces, and driving personalized medicine, paving the way for more precise and adaptive treatments. Beyond applications, neuroscience itself has inspired AI innovations, with neural architectures and brain-like processes shaping advances in learning algorithms and explainable models. This bidirectional exchange has fueled breakthroughs such as dynamic connectivity mapping, real-time neural decoding, and closed-loop brain-computer systems that adaptively respond to neural states. However, challenges persist, including issues of data integration, ethical considerations, and the "black-box" nature of many AI systems, underscoring the need for transparent, equitable, and interdisciplinary approaches. By synthesizing the latest breakthroughs and identifying future opportunities, this review charts a path forward for the integration of AI and neuroscience. From harnessing multimodal data to enabling cognitive augmentation, the fusion of these fields is not just transforming brain science, it is reimagining human potential. This partnership promises a future where the mysteries of the brain are unlocked, offering unprecedented advancements in healthcare, technology, and beyond.
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Affiliation(s)
- Razvan Onciul
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (R.O.); (M.S.); (R.-A.C.-B.); (M.P.R.); (C.T.)
- Neurosurgery Department, Emergency University Hospital, 050098 Bucharest, Romania
| | - Catalina-Ioana Tataru
- Clinical Department of Ophthalmology, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania
- Department of Ophthalmology, Clinical Hospital for Ophthalmological Emergencies, 010464 Bucharest, Romania
| | - Adrian Vasile Dumitru
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (R.O.); (M.S.); (R.-A.C.-B.); (M.P.R.); (C.T.)
- Department of Morphopathology, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania
- Emergency University Hospital, 050098 Bucharest, Romania
| | - Carla Crivoi
- Department of Computer Science, Faculty of Mathematics and Computer Science, University of Bucharest, 010014 Bucharest, Romania;
| | - Matei Serban
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (R.O.); (M.S.); (R.-A.C.-B.); (M.P.R.); (C.T.)
- Department of Vascular Neurosurgery, National Institute of Neurovascular Disease, 077160 Bucharest, Romania
- Puls Med Association, 051885 Bucharest, Romania
| | - Razvan-Adrian Covache-Busuioc
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (R.O.); (M.S.); (R.-A.C.-B.); (M.P.R.); (C.T.)
- Department of Vascular Neurosurgery, National Institute of Neurovascular Disease, 077160 Bucharest, Romania
- Puls Med Association, 051885 Bucharest, Romania
| | - Mugurel Petrinel Radoi
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (R.O.); (M.S.); (R.-A.C.-B.); (M.P.R.); (C.T.)
- Department of Vascular Neurosurgery, National Institute of Neurovascular Disease, 077160 Bucharest, Romania
| | - Corneliu Toader
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (R.O.); (M.S.); (R.-A.C.-B.); (M.P.R.); (C.T.)
- Department of Vascular Neurosurgery, National Institute of Neurovascular Disease, 077160 Bucharest, Romania
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Chen S, Yang Q, Lim S. Efficient inference of synaptic plasticity rule with Gaussian process regression. iScience 2023; 26:106182. [PMID: 36879810 PMCID: PMC9985048 DOI: 10.1016/j.isci.2023.106182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 01/24/2023] [Accepted: 02/07/2023] [Indexed: 02/16/2023] Open
Abstract
Finding the form of synaptic plasticity is critical to understanding its functions underlying learning and memory. We investigated an efficient method to infer synaptic plasticity rules in various experimental settings. We considered biologically plausible models fitting a wide range of in-vitro studies and examined the recovery of their firing-rate dependence from sparse and noisy data. Among the methods assuming low-rankness or smoothness of plasticity rules, Gaussian process regression (GPR), a nonparametric Bayesian approach, performs the best. Under the conditions measuring changes in synaptic weights directly or measuring changes in neural activities as indirect observables of synaptic plasticity, which leads to different inference problems, GPR performs well. Also, GPR could simultaneously recover multiple plasticity rules and robustly perform under various plasticity rules and noise levels. Such flexibility and efficiency, particularly at the low sampling regime, make GPR suitable for recent experimental developments and inferring a broader class of plasticity models.
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Affiliation(s)
- Shirui Chen
- Department of Applied Mathematics, University of Washington, Lewis Hall 201, Box 353925, Seattle, WA 98195-3925, USA
- Neural Science, New York University Shanghai, 1555 Century Avenue, Shanghai, 200122, China
| | - Qixin Yang
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University, The Suzanne and Charles Goodman Brain Sciences Building, Edmond J. Safra Campus, Jerusalem, 9190401, Israel
- Neural Science, New York University Shanghai, 1555 Century Avenue, Shanghai, 200122, China
| | - Sukbin Lim
- Neural Science, New York University Shanghai, 1555 Century Avenue, Shanghai, 200122, China
- NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai, 3663 Zhongshan Road North, Shanghai, 200062, China
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Gu J, Lim S. Unsupervised learning for robust working memory. PLoS Comput Biol 2022; 18:e1009083. [PMID: 35500033 PMCID: PMC9098088 DOI: 10.1371/journal.pcbi.1009083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 05/12/2022] [Accepted: 03/16/2022] [Indexed: 11/18/2022] Open
Abstract
Working memory is a core component of critical cognitive functions such as planning and decision-making. Persistent activity that lasts long after the stimulus offset has been considered a neural substrate for working memory. Attractor dynamics based on network interactions can successfully reproduce such persistent activity. However, it requires a fine-tuning of network connectivity, in particular, to form continuous attractors which were suggested for encoding continuous signals in working memory. Here, we investigate whether a specific form of synaptic plasticity rules can mitigate such tuning problems in two representative working memory models, namely, rate-coded and location-coded persistent activity. We consider two prominent types of plasticity rules, differential plasticity correcting the rapid activity changes and homeostatic plasticity regularizing the long-term average of activity, both of which have been proposed to fine-tune the weights in an unsupervised manner. Consistent with the findings of previous works, differential plasticity alone was enough to recover a graded-level persistent activity after perturbations in the connectivity. For the location-coded memory, differential plasticity could also recover persistent activity. However, its pattern can be irregular for different stimulus locations under slow learning speed or large perturbation in the connectivity. On the other hand, homeostatic plasticity shows a robust recovery of smooth spatial patterns under particular types of synaptic perturbations, such as perturbations in incoming synapses onto the entire or local populations. However, homeostatic plasticity was not effective against perturbations in outgoing synapses from local populations. Instead, combining it with differential plasticity recovers location-coded persistent activity for a broader range of perturbations, suggesting compensation between two plasticity rules.
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Affiliation(s)
- Jintao Gu
- Neural Science, New York University Shanghai, Shanghai, China
| | - Sukbin Lim
- Neural Science, New York University Shanghai, Shanghai, China
- NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai, Shanghai, China
- * E-mail:
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