用于神经和精神疾病建模的神经形态计算:对药物开发的影响

IF 10.7 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence Review Pub Date : 2024-10-10 DOI:10.1007/s10462-024-10948-3
Amisha S. Raikar, J Andrew, Pranjali Prabhu Dessai, Sweta M. Prabhu, Shounak Jathar, Aishwarya Prabhu, Mayuri B. Naik, Gokuldas Vedant S. Raikar
{"title":"用于神经和精神疾病建模的神经形态计算:对药物开发的影响","authors":"Amisha S. Raikar,&nbsp;J Andrew,&nbsp;Pranjali Prabhu Dessai,&nbsp;Sweta M. Prabhu,&nbsp;Shounak Jathar,&nbsp;Aishwarya Prabhu,&nbsp;Mayuri B. Naik,&nbsp;Gokuldas Vedant S. Raikar","doi":"10.1007/s10462-024-10948-3","DOIUrl":null,"url":null,"abstract":"<div><p>The emergence of neuromorphic computing, inspired by the structure and function of the human brain, presents a transformative framework for modelling neurological disorders in drug development. This article investigates the implications of applying neuromorphic computing to simulate and comprehend complex neural systems affected by conditions like Alzheimer’s, Parkinson’s, and epilepsy, drawing from extensive literature. It explores the intersection of neuromorphic computing with neurology and pharmaceutical development, emphasizing the significance of understanding neural processes and integrating deep learning techniques. Technical considerations, such as integrating neural circuits into CMOS technology and employing memristive devices for synaptic emulation, are discussed. The review evaluates how neuromorphic computing optimizes drug discovery and improves clinical trials by precisely simulating biological systems. It also examines the role of neuromorphic models in comprehending and simulating neurological disorders, facilitating targeted treatment development. Recent progress in neuromorphic drug discovery is highlighted, indicating the potential for transformative therapeutic interventions. As technology advances, the synergy between neuromorphic computing and neuroscience holds promise for revolutionizing the study of the human brain’s complexities and addressing neurological challenges.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":null,"pages":null},"PeriodicalIF":10.7000,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10948-3.pdf","citationCount":"0","resultStr":"{\"title\":\"Neuromorphic computing for modeling neurological and psychiatric disorders: implications for drug development\",\"authors\":\"Amisha S. Raikar,&nbsp;J Andrew,&nbsp;Pranjali Prabhu Dessai,&nbsp;Sweta M. Prabhu,&nbsp;Shounak Jathar,&nbsp;Aishwarya Prabhu,&nbsp;Mayuri B. Naik,&nbsp;Gokuldas Vedant S. Raikar\",\"doi\":\"10.1007/s10462-024-10948-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The emergence of neuromorphic computing, inspired by the structure and function of the human brain, presents a transformative framework for modelling neurological disorders in drug development. This article investigates the implications of applying neuromorphic computing to simulate and comprehend complex neural systems affected by conditions like Alzheimer’s, Parkinson’s, and epilepsy, drawing from extensive literature. It explores the intersection of neuromorphic computing with neurology and pharmaceutical development, emphasizing the significance of understanding neural processes and integrating deep learning techniques. Technical considerations, such as integrating neural circuits into CMOS technology and employing memristive devices for synaptic emulation, are discussed. The review evaluates how neuromorphic computing optimizes drug discovery and improves clinical trials by precisely simulating biological systems. It also examines the role of neuromorphic models in comprehending and simulating neurological disorders, facilitating targeted treatment development. Recent progress in neuromorphic drug discovery is highlighted, indicating the potential for transformative therapeutic interventions. As technology advances, the synergy between neuromorphic computing and neuroscience holds promise for revolutionizing the study of the human brain’s complexities and addressing neurological challenges.</p></div>\",\"PeriodicalId\":8449,\"journal\":{\"name\":\"Artificial Intelligence Review\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":10.7000,\"publicationDate\":\"2024-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s10462-024-10948-3.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Artificial Intelligence Review\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10462-024-10948-3\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence Review","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10462-024-10948-3","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

受人脑结构和功能的启发,神经形态计算的出现为药物开发中的神经系统疾病建模提供了一个变革性框架。本文通过大量文献,探讨了应用神经形态计算模拟和理解受阿尔茨海默氏症、帕金森氏症和癫痫等疾病影响的复杂神经系统的意义。文章探讨了神经形态计算与神经学和药物开发的交叉点,强调了理解神经过程和整合深度学习技术的重要性。文中还讨论了一些技术考虑因素,如将神经电路集成到 CMOS 技术中,以及采用记忆器件进行突触仿真。综述评估了神经形态计算如何通过精确模拟生物系统来优化药物发现和改进临床试验。它还探讨了神经形态模型在理解和模拟神经系统疾病方面的作用,从而促进有针对性的治疗开发。报告重点介绍了神经形态药物发现的最新进展,指出了变革性治疗干预的潜力。随着技术的进步,神经形态计算与神经科学之间的协同作用有望彻底改变对人类大脑复杂性的研究,并解决神经学方面的难题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Neuromorphic computing for modeling neurological and psychiatric disorders: implications for drug development

The emergence of neuromorphic computing, inspired by the structure and function of the human brain, presents a transformative framework for modelling neurological disorders in drug development. This article investigates the implications of applying neuromorphic computing to simulate and comprehend complex neural systems affected by conditions like Alzheimer’s, Parkinson’s, and epilepsy, drawing from extensive literature. It explores the intersection of neuromorphic computing with neurology and pharmaceutical development, emphasizing the significance of understanding neural processes and integrating deep learning techniques. Technical considerations, such as integrating neural circuits into CMOS technology and employing memristive devices for synaptic emulation, are discussed. The review evaluates how neuromorphic computing optimizes drug discovery and improves clinical trials by precisely simulating biological systems. It also examines the role of neuromorphic models in comprehending and simulating neurological disorders, facilitating targeted treatment development. Recent progress in neuromorphic drug discovery is highlighted, indicating the potential for transformative therapeutic interventions. As technology advances, the synergy between neuromorphic computing and neuroscience holds promise for revolutionizing the study of the human brain’s complexities and addressing neurological challenges.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Artificial Intelligence Review
Artificial Intelligence Review 工程技术-计算机:人工智能
CiteScore
22.00
自引率
3.30%
发文量
194
审稿时长
5.3 months
期刊介绍: Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.
期刊最新文献
Counterfactuals in fuzzy relational models Chronobridge: a novel framework for enhanced temporal and relational reasoning in temporal knowledge graphs A review of Artificial Intelligence methods in bladder cancer: segmentation, classification, and detection Artificial intelligence techniques for dynamic security assessments - a survey A survey of recent approaches to form understanding in scanned documents
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1