{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T06:53:55Z","timestamp":1787900035584,"version":"build-2784847793"},"reference-count":124,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Manipal Academy of Higher Education, Manipal"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats: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\u2019s, Parkinson\u2019s, 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\u2019s complexities and addressing neurological challenges.<\/jats:p>","DOI":"10.1007\/s10462-024-10948-3","type":"journal-article","created":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T16:22:12Z","timestamp":1728577332000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Neuromorphic computing for modeling neurological and psychiatric disorders: implications for drug development"],"prefix":"10.1007","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1328-1589","authenticated-orcid":false,"given":"Amisha S.","family":"Raikar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J","family":"Andrew","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pranjali Prabhu","family":"Dessai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sweta M.","family":"Prabhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shounak","family":"Jathar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aishwarya","family":"Prabhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mayuri B.","family":"Naik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gokuldas Vedant S.","family":"Raikar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,10]]},"reference":[{"issue":"1","key":"10948_CR1","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1186\/s13321-022-00623-6","volume":"14","author":"M Abbasi","year":"2022","unstructured":"Abbasi M, Santos BP, Pereira TC, Sofia R, Monteiro NR, Simoes CJ, Brito RM, Ribeiro B, Oliveira JL, Arrais JP (2022) Designing optimized drug candidates with generative adversarial network. J Cheminform 14(1):40","journal-title":"J Cheminform"},{"issue":"4","key":"10948_CR2","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aceca3","volume":"20","author":"K Aboumerhi","year":"2023","unstructured":"Aboumerhi K, G\u00fcemes A, Liu H, Tenore F, Etienne-Cummings R (2023) Neuromorphic applications in medicine. J Neural Eng 20(4):041004","journal-title":"J Neural Eng"},{"issue":"5","key":"10948_CR3","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1016\/S0731-7085(99)00272-1","volume":"22","author":"S Agatonovic-Kustrin","year":"2000","unstructured":"Agatonovic-Kustrin S, Beresford R (2000) Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal 22(5):717\u2013727","journal-title":"J Pharm Biomed Anal"},{"issue":"12","key":"10948_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsps.2023.101835","volume":"31","author":"FF Alshehri","year":"2023","unstructured":"Alshehri FF (2023) Integrated virtual screening, molecular modeling and machine learning approaches revealed potential natural inhibitors for epilepsy. Saudi Pharmaceut J 31(12):101835","journal-title":"Saudi Pharmaceut J"},{"issue":"1","key":"10948_CR5","doi-asserted-by":"publisher","first-page":"464","DOI":"10.3390\/make6010024","volume":"6","author":"MG Alsubaie","year":"2024","unstructured":"Alsubaie MG, Luo S, Shaukat K (2024) Alzheimer\u2019s disease detection using deep learning on neuroimaging: a systematic review. Mach Learn Knowl Extract 6(1):464\u2013505","journal-title":"Mach Learn Knowl Extract"},{"key":"10948_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.biomaterials.2022.121531","volume":"285","author":"L Amirifar","year":"2022","unstructured":"Amirifar L, Shamloo A, Nasiri R, de Barros NR, Wang ZZ, Unluturk BD, Libanori A, Ievglevskyi O, Diltemiz SE, Sances S, Balasingham I (2022) Brain-on-a-chip: recent advances in design and techniques for microfluidic models of the brain in health and disease. Biomaterials 285:121531","journal-title":"Biomaterials"},{"key":"10948_CR7","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.jdbs.2023.11.001","volume":"3","author":"S Baker","year":"2023","unstructured":"Baker S, Fenstermacher E, Davis RA, Kern DS, Thompson JA, Felsen G, Baumgartner AJ (2023) Ethical considerations in closed loop deep brain stimulation. Deep Brain Stimulation 3:8\u201315","journal-title":"Deep Brain Stimulation"},{"issue":"5","key":"10948_CR8","doi-asserted-by":"publisher","first-page":"874","DOI":"10.1016\/j.clinph.2014.01.006","volume":"125","author":"A Beuter","year":"2014","unstructured":"Beuter A, Lefaucheur JP, Modolo J (2014) Closed-loop cortical neuromodulation in Parkinson\u2019s disease: an alternative to deep brain stimulation? Clin Neurophysiol 125(5):874\u2013885","journal-title":"Clin Neurophysiol"},{"key":"10948_CR9","unstructured":"Bird TD (2018) Alzheimer disease overview. GeneReviews\u00ae[Internet]"},{"issue":"4","key":"10948_CR10","doi-asserted-by":"publisher","first-page":"131","DOI":"10.3390\/biom8040131","volume":"8","author":"EJ Bjerrum","year":"2018","unstructured":"Bjerrum EJ, Sattarov B (2018) Improving chemical autoencoder latent space and molecular de novo generation diversity with heteroencoders. Biomolecules 8(4):131","journal-title":"Biomolecules"},{"issue":"18","key":"10948_CR300","doi-asserted-by":"publisher","first-page":"1611","DOI":"10.1212\/WNL.0b013e3182a9f558","volume":"81","author":"NI Bohnen","year":"2013","unstructured":"Bohnen NI, Frey KA, Studenski S, Kotagal V, Koeppe RA, Scott PJ, Albin RL, M\u00fcller ML (2013) Gait speed in Parkinson disease correlates with cholinergic degeneration. Neurol 81(18):1611\u20131616","journal-title":"Neurol"},{"issue":"2","key":"10948_CR11","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1093\/brain\/awr271","volume":"135","author":"D Borsook","year":"2012","unstructured":"Borsook D (2012) Neurological diseases and pain. Brain 135(2):320\u2013344","journal-title":"Brain"},{"key":"10948_CR12","doi-asserted-by":"publisher","DOI":"10.1111\/epi.17566","author":"E Bou Assi","year":"2023","unstructured":"Bou Assi E, Schindler K, De B\u00e9zenac C, Denison T, Desai S, Keller SS, Lemoine \u00c9, Rahimi A, Shoaran M, Rummel C (2023) From basic sciences and engineering to epileptology: a translational approach. Epilepsia. https:\/\/doi.org\/10.1111\/epi.17566","journal-title":"Epilepsia"},{"issue":"2","key":"10948_CR13","doi-asserted-by":"publisher","first-page":"95","DOI":"10.3109\/10837459709022615","volume":"2","author":"J Bourquin","year":"1997","unstructured":"Bourquin J, Schmidli H, van Hoogevest P, Leuenberger H (1997) Basic concepts of artificial neural networks (ANN) modeling in the application to pharmaceutical development. Pharm Dev Technol 2(2):95\u2013109","journal-title":"Pharm Dev Technol"},{"issue":"28","key":"10948_CR14","doi-asserted-by":"publisher","first-page":"3347","DOI":"10.2174\/1381612824666180607124038","volume":"24","author":"KA Carpenter","year":"2018","unstructured":"Carpenter KA, Huang X (2018) Machine learning-based virtual screening and its applications to Alzheimer\u2019s drug discovery: a review. Curr Pharm Des 24(28):3347\u20133358","journal-title":"Curr Pharm Des"},{"key":"10948_CR15","doi-asserted-by":"crossref","unstructured":"Chatzipaschalis IK, Tsipas E, Fyrigos IA, Rubio A, Sirakoulis GC (2023) CBRAM-based bio-inspired circuit for the emulation and treatment of the Parkinson\u2019s disease. IEEE transactions on circuits and systems II: express briefs","DOI":"10.1109\/TCSII.2023.3339442"},{"issue":"4","key":"10948_CR16","doi-asserted-by":"publisher","first-page":"427","DOI":"10.3390\/mi11040427","volume":"11","author":"Q Chen","year":"2020","unstructured":"Chen Q, Han T, Tang M, Zhang Z, Zheng X, Liu G (2020) Improving the recognition accuracy of memristive neural networks via homogenized analog type conductance quantization. Micromachines 11(4):427","journal-title":"Micromachines"},{"issue":"9","key":"10948_CR17","doi-asserted-by":"publisher","first-page":"1367","DOI":"10.1109\/JPROC.2014.2313954","volume":"102","author":"E Chicca","year":"2014","unstructured":"Chicca E, Stefanini F, Bartolozzi C, Indiveri G (2014) Neuromorphic electronic circuits for building autonomous cognitive systems. Proc IEEE 102(9):1367\u20131388","journal-title":"Proc IEEE"},{"issue":"1","key":"10948_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12877-020-01926-9","volume":"20","author":"FK Clemmensen","year":"2020","unstructured":"Clemmensen FK, Hoffmann K, Siersma V, Sobol N, Beyer N, Andersen BB, Vogel A, Lolk A, Gottrup H, H\u00f8gh P, Waldemar G (2020) The role of physical and cognitive function in performance of activities of daily living in patients with mild-to-moderate Alzheimer\u2019s disease\u2013a cross-sectional study. BMC Geriatr 20(1):1\u20139","journal-title":"BMC Geriatr"},{"issue":"4","key":"10948_CR19","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1177\/1535759720934787","volume":"20","author":"PL Colmers","year":"2020","unstructured":"Colmers PL, Maguire J (2020) Network dysfunction in comorbid psychiatric illnesses and epilepsy. Epilepsy Currents 20(4):205\u2013210","journal-title":"Epilepsy Currents"},{"key":"10948_CR20","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2021.725797","volume":"15","author":"DD Cummins","year":"2021","unstructured":"Cummins DD, Kochanski RB, Gilron R, Swann NC, Little S, Hammer LH, Starr PA (2021) Chronic sensing of subthalamic local field potentials: comparison of first and second generation implantable bidirectional systems within a single subject. Front Neurosci 15:725797","journal-title":"Front Neurosci"},{"key":"10948_CR21","doi-asserted-by":"crossref","unstructured":"Das MK, Chakraborty T (2016) ANN in pharmaceutical product and process development. In Artificial neural network for drug design, delivery and disposition (pp. 277\u2013293). Academic Press\\","DOI":"10.1016\/B978-0-12-801559-9.00014-4"},{"key":"10948_CR22","doi-asserted-by":"crossref","unstructured":"Date P, Kay B, Schuman C, Patton R, Potok T (2021) Computational complexity of neuromorphic algorithms. In international conference on neuromorphic systems 2021 (pp. 1-7)","DOI":"10.1145\/3477145.3477154"},{"issue":"5","key":"10948_CR23","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1109\/JPROC.2021.3067593","volume":"109","author":"M Davies","year":"2021","unstructured":"Davies M, Wild A, Orchard G, Sandamirskaya Y, Guerra GA, Joshi P, Plank P, Risbud SR (2021) Advancing neuromorphic computing with loihi: a survey of results and outlook. Proc IEEE 109(5):911\u2013934","journal-title":"Proc IEEE"},{"key":"10948_CR24","first-page":"485","volume":"2022","author":"R Davuluri","year":"2021","unstructured":"Davuluri R, Rengaswamy R (2021) Identification of Alzheimer\u2019s disease using various deep learning techniques\u2014a review. Intell Manuf Energy Sustain: Proceed ICIMES 2022:485\u2013498","journal-title":"Intell Manuf Energy Sustain: Proceed ICIMES"},{"issue":"1","key":"10948_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13024-019-0333-5","volume":"14","author":"MA DeTure","year":"2019","unstructured":"DeTure MA, Dickson DW (2019) The neuropathological diagnosis of Alzheimer\u2019s disease. Mol Neurodegener 14(1):1\u20138","journal-title":"Mol Neurodegener"},{"issue":"1","key":"10948_CR26","doi-asserted-by":"publisher","DOI":"10.1088\/2516-1091\/acb51c","volume":"5","author":"E Donati","year":"2023","unstructured":"Donati E, Indiveri G (2023) Neuromorphic bioelectronic medicine for nervous system interfaces: from neural computational primitives to medical applications. Progress Biomed Eng 5(1):013002","journal-title":"Progress Biomed Eng"},{"key":"10948_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/3098673","volume":"2020","author":"MM dos Santos","year":"2020","unstructured":"dos Santos MM, Rodrigues GC, de Sousa NF, Scotti MT, Scotti L, Mendon\u00e7a-Junior FJ (2020) Identification of new targets and the virtual screening of lignans against Alzheimer\u2019s disease. Oxid Med Cell Longev 2020:1\u201319","journal-title":"Oxid Med Cell Longev"},{"key":"10948_CR500","unstructured":"Dwyer L (2019) Modelling Depression Recurrence Through Analysis of Electronic Health Records"},{"issue":"11","key":"10948_CR28","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1038\/nrn3599","volume":"14","author":"GT Einevoll","year":"2013","unstructured":"Einevoll GT, Kayser C, Logothetis NK, Panzeri S (2013) Modelling and analysis of local field potentials for studying the function of cortical circuits. Nat Rev Neurosci 14(11):770\u2013785","journal-title":"Nat Rev Neurosci"},{"issue":"18","key":"10948_CR29","doi-asserted-by":"publisher","first-page":"7065","DOI":"10.1080\/07391102.2020.1805362","volume":"39","author":"ND Elangovan","year":"2021","unstructured":"Elangovan ND, Dhanabalan AK, Gunasekaran K, Kandimalla R, Sankarganesh D (2021) Screening of potential drug for Alzheimer\u2019s disease: a computational study with GSK-3 \u03b2 inhibition through virtual screening, docking, and molecular dynamics simulation. J Biomol Struct Dyn 39(18):7065\u20137079","journal-title":"J Biomol Struct Dyn"},{"key":"10948_CR30","unstructured":"Fares H, Ronchini M, Zamani M, Farkhani H, Moradi F (2022) In the realm of hybrid Brain: Human Brain and AI. Preprint at arXiv:2210.01461"},{"issue":"2","key":"10948_CR31","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1001\/jamaneurol.2020.4152","volume":"78","author":"VL Feigin","year":"2021","unstructured":"Feigin VL, Vos T, Alahdab F, Amit AM, B\u00e4rnighausen TW, Beghi E, Beheshti M, Chavan PP, Criqui MH, Desai R, Dharmaratne SD (2021) Burden of neurological disorders across the US from 1990\u20132017: a global burden of disease study. JAMA Neurol 78(2):165\u2013176","journal-title":"JAMA Neurol"},{"issue":"170","key":"10948_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.sse.2020.107833","volume":"1","author":"C Fernandez","year":"2020","unstructured":"Fernandez C, Gomez J, Ortiz J, Vourkas I (2020) Comprehensive predictive modeling of resistive switching devices using a bias-dependent window function approach. Solid-State Electron 1(170):107833","journal-title":"Solid-State Electron"},{"issue":"16","key":"10948_CR33","doi-asserted-by":"publisher","first-page":"11086","DOI":"10.1021\/acsomega.1c01266","volume":"6","author":"D Fernandez-Llaneza","year":"2021","unstructured":"Fernandez-Llaneza D, Ulander S, Gogishvili D, Nittinger E, Zhao H, Tyrchan C (2021) Siamese Recurrent neural network with a self-attention mechanism for bioactivity prediction. ACS Omega 6(16):11086\u201311094","journal-title":"ACS Omega"},{"key":"10948_CR409","doi-asserted-by":"publisher","first-page":"89","DOI":"10.3389\/fnhum.2013.00089","volume":"7","author":"SE Fox","year":"2013","unstructured":"Fox SE, Wagner JB, Shrock CL, Tager-Flusberg H, Nelson CA (2013) Neural processing of facial identity and emotion in infants at high\u2013risk for autism spectrum disorders. Front hum neurosci 7:89","journal-title":"Front hum neurosci"},{"issue":"5","key":"10948_CR34","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/13\/5\/051001","volume":"13","author":"S Furber","year":"2016","unstructured":"Furber S (2016) Large-scale neuromorphic computing systems. J Neural Eng 13(5):051001","journal-title":"J Neural Eng"},{"key":"10948_CR35","doi-asserted-by":"crossref","unstructured":"Gull S, Akbar S (2021) Artificial intelligence in brain tumor detection through MRI scans: advancements and challenges. Artificial intelligence and internet of things. pp 241\u2013276","DOI":"10.1201\/9781003097204-10"},{"key":"10948_CR36","doi-asserted-by":"crossref","unstructured":"Henry KR, Miulli MM, Elahi B, Rosenow J, Nolt M, Golestanirad L (2022) Analysis of the intended and actual orientations of directional deep brain stimulation leads across deep brain stimulation systems. In 2022 44th annual international conference of the IEEE engineering in medicine & biology society (EMBC) (pp. 1725\u20131728). IEEE","DOI":"10.1109\/EMBC48229.2022.9871608"},{"issue":"18","key":"10948_CR37","doi-asserted-by":"publisher","first-page":"14061","DOI":"10.3390\/ijms241814061","volume":"24","author":"Y Huang","year":"2023","unstructured":"Huang Y, Huang HY, Chen Y, Lin YC, Yao L, Lin T, Leng J, Chang Y, Zhang Y, Zhu Z, Ma K (2023) A robust drug\u2013target interaction prediction framework with capsule network and transfer learning. Int J Mol Sci 24(18):14061","journal-title":"Int J Mol Sci"},{"key":"10948_CR38","doi-asserted-by":"publisher","first-page":"1248","DOI":"10.1023\/A:1015843527138","volume":"8","author":"AS Hussain","year":"1991","unstructured":"Hussain AS, Yu X, Johnson RD (1991) Application of neural computing in pharmaceutical product development. Pharm Res 8:1248\u20131252","journal-title":"Pharm Res"},{"issue":"10","key":"10948_CR39","doi-asserted-by":"publisher","first-page":"1739","DOI":"10.3109\/03639049409038390","volume":"20","author":"AS Hussain","year":"1994","unstructured":"Hussain AS, Shivanand P, Johnson RD (1994) Application of neural computing in pharmaceutical product development: computer aided formulation design. Drug Dev Ind Pharm 20(10):1739\u20131752","journal-title":"Drug Dev Ind Pharm"},{"key":"10948_CR40","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.3389\/fphys.2019.01044","volume":"10","author":"G Idakwo","year":"2019","unstructured":"Idakwo G, Thangapandian S, Luttrell J IV, Zhou Z, Zhang C, Gong P (2019) Deep learning-based structure-activity relationship modeling for multi-category toxicity classification: a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Front Physiol 10:1044","journal-title":"Front Physiol"},{"key":"10948_CR41","doi-asserted-by":"publisher","first-page":"118206","DOI":"10.1016\/j.neuroimage.2021.118206","volume":"237","author":"JE Iglesias","year":"2021","unstructured":"Iglesias JE, Billot B, Balbastre Y, Tabari A, Conklin J, Gonz\u00e1lez RG, Alexander DC, Golland P, Edlow BL, Fischl B, Alzheimer\u2019s Disease Neuroimaging Initiative (2021) Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast. Neuroimage. 237:118206","journal-title":"Neuroimage."},{"key":"10948_CR42","doi-asserted-by":"publisher","first-page":"220","DOI":"10.3389\/fnagi.2019.00220","volume":"11","author":"T Jo","year":"2019","unstructured":"Jo T, Nho K, Saykin AJ (2019) Deep learning in Alzheimer\u2019s disease: diagnostic classification and prognostic prediction using neuroimaging data. Front Aging Neurosci 11:220","journal-title":"Front Aging Neurosci"},{"issue":"12","key":"10948_CR43","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.1080\/17460441.2018.1542428","volume":"13","author":"PM Khan","year":"2018","unstructured":"Khan PM, Roy K (2018) Current approaches for choosing feature selection and learning algorithms in quantitative structure\u2013activity relationships (QSAR). Expert Opin Drug Discov 13(12):1075\u20131089","journal-title":"Expert Opin Drug Discov"},{"issue":"1","key":"10948_CR44","doi-asserted-by":"publisher","first-page":"2100935","DOI":"10.1002\/aelm.202100935","volume":"8","author":"KN Kim","year":"2022","unstructured":"Kim KN, Sung MJ, Park HL, Lee TW (2022) Organic synaptic transistors for bio-hybrid neuromorphic electronics. Adv Electron Mater 8(1):2100935","journal-title":"Adv Electron Mater"},{"issue":"5","key":"10948_CR400","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/S1474-4422(04)00740-9","volume":"3","author":"AE Lang","year":"2004","unstructured":"Lang AE, Obeso JA (2004) Challenges in Parkinson\u2019s disease: restoration of the nigrostriatal dopamine system is not enough. Lancet Neurol 3(5):309\u201316","journal-title":"Lancet Neurol"},{"issue":"1","key":"10948_CR45","doi-asserted-by":"publisher","first-page":"8562","DOI":"10.1038\/s41598-019-44680-8","volume":"9","author":"S Lang","year":"2019","unstructured":"Lang S, Gan LS, Alrazi T, Monchi O (2019) Theta band high definition transcranial alternating current stimulation, but not transcranial direct current stimulation, improves associative memory performance. Sci Rep 9(1):8562","journal-title":"Sci Rep"},{"key":"10948_CR46","doi-asserted-by":"publisher","first-page":"99","DOI":"10.2478\/s13380-014-0212-z","volume":"5","author":"M Lebedev","year":"2014","unstructured":"Lebedev M (2014) Brain-machine interfaces: an overview. Translat Neurosci 5:99\u2013110","journal-title":"Translat Neurosci"},{"issue":"3","key":"10948_CR501","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/S1474-4422(09)70042-0","volume":"8","author":"AJ Lees","year":"2009","unstructured":"Lees AJ, Williams DR  (2009) Progressive supranuclear palsy: clinicopathological concepts and diagnostic challenges. Lancet Neurol 8(3):270\u2013279","journal-title":"Lancet Neurol"},{"issue":"3","key":"10948_CR47","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1016\/j.wneu.2015.04.050","volume":"84","author":"B Lee","year":"2015","unstructured":"Lee B, Zubair MN, Marquez YD, Lee DM, Kalayjian LA, Heck CN, Liu CY (2015) A single-center experience with the NeuroPace RNS system: a review of techniques and potential problems. World Neurosurgery 84(3):719\u2013726","journal-title":"World Neurosurgery"},{"key":"10948_CR48","doi-asserted-by":"crossref","unstructured":"Li B, Yan B, Liu C, Li H (2019) Build reliable and efficient neuromorphic design with memristor technology. In Proceedings of the 24th Asia and South Pacific Design Automation Conference (pp. 224\u2013229)","DOI":"10.1145\/3287624.3288744"},{"issue":"4","key":"10948_CR49","doi-asserted-by":"publisher","first-page":"bbad235","DOI":"10.1093\/bib\/bbad235","volume":"24","author":"X Lin","year":"2023","unstructured":"Lin X, Dai L, Zhou Y, Yu ZG, Zhang W, Shi JY, Cao DS, Zeng L, Chen H, Song B, Yu PS (2023) Comprehensive evaluation of deep and graph learning on drug\u2013drug interactions prediction. Brief Bioinform 24(4):bbad235","journal-title":"Brief Bioinform"},{"key":"10948_CR50","doi-asserted-by":"crossref","unstructured":"Liou GH, Wang SH, Su YY, Lin MP (2018) Classifying analog and digital circuits with machine learning techniques toward mixed-signal design automation. In 2018 15th international conference on synthesis, modeling, analysis and simulation methods and applications to circuit design (SMACD) (pp. 173\u2013176). IEEE","DOI":"10.1109\/SMACD.2018.8434884"},{"key":"10948_CR51","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/978-94-011-1350-2_8","volume-title":"Molecular similarity in drug design","author":"DJ Livingstone","year":"1995","unstructured":"Livingstone DJ, Salt DW (1995) Neural networks in the search for similarity and structure\u2014activity. Molecular similarity in drug design. Springer, Dordrecht, pp 187\u2013214"},{"key":"10948_CR52","doi-asserted-by":"publisher","first-page":"97","DOI":"10.3389\/fnins.2019.00097","volume":"13","author":"M Livne","year":"2019","unstructured":"Livne M, Rieger J, Aydin OU, Taha AA, Akay EM, Kossen T, Sobesky J, Kelleher JD, Hildebrand K, Frey D, Madai VI (2019) A U-Net deep learning framework for high performance vessel segmentation in patients with cerebrovascular disease. Front Neurosci 13:97","journal-title":"Front Neurosci"},{"key":"10948_CR53","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/B978-0-12-804766-8.00013-3","volume":"167","author":"JA Lopez","year":"2019","unstructured":"Lopez JA, Gonz\u00e1lez HM, L\u00e9ger GC (2019) Alzheimer\u2019s disease. Handbook Clin Neurol 167:231\u2013255","journal-title":"Handbook Clin Neurol"},{"issue":"1","key":"10948_CR54","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1021\/ci700124r","volume":"48","author":"J Marialke","year":"2008","unstructured":"Marialke J, Tietze S, Apostolakis J (2008) Similarity based docking. J Chem Inf Model 48(1):186\u2013196","journal-title":"J Chem Inf Model"},{"issue":"9","key":"10948_CR55","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1038\/s42254-020-0208-2","volume":"2","author":"D Markovi\u0107","year":"2020","unstructured":"Markovi\u0107 D, Mizrahi A, Querlioz D, Grollier J (2020) Physics for neuromorphic computing. Nat Rev Phys 2(9):499\u2013510","journal-title":"Nat Rev Phys"},{"key":"10948_CR56","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.procs.2011.12.015","volume":"7","author":"H Markram","year":"2011","unstructured":"Markram H, Meier K, Lippert T, Grillner S, Frackowiak R, Dehaene S, Knoll A, Sompolinsky H, Verstreken K, DeFelipe J, Grant S (2011) Introducing the human brain project. Procedia Comput Sci 7:39\u201342","journal-title":"Procedia Comput Sci"},{"issue":"6","key":"10948_CR57","doi-asserted-by":"publisher","first-page":"a009282","DOI":"10.1101\/cshperspect.a009282","volume":"2","author":"P Mazzoni","year":"2012","unstructured":"Mazzoni P, Shabbott B, Cort\u00e9s JC (2012) Motor control abnormalities in Parkinson\u2019s disease. Cold Spring Harbor Perspect Med 2(6):a009282","journal-title":"Cold Spring Harbor Perspect Med"},{"issue":"Supplement_2","key":"10948_CR58","doi-asserted-by":"publisher","first-page":"S3","DOI":"10.1017\/S1461145700001942","volume":"3","author":"MS Mega","year":"2000","unstructured":"Mega MS (2000) The cholinergic deficit in Alzheimer\u2019s disease: impact on cognition, behaviour and function. Int J Neuropsychopharmacol 3(Supplement_2):S3\u2013S12","journal-title":"Int J Neuropsychopharmacol"},{"key":"10948_CR59","doi-asserted-by":"publisher","first-page":"61","DOI":"10.3389\/fninf.2017.00061","volume":"11","author":"RJ Meszl\u00e9nyi","year":"2017","unstructured":"Meszl\u00e9nyi RJ, Buza K, Vidny\u00e1nszky Z (2017) Resting state fMRI functional connectivity-based classification using a convolutional neural network architecture. Front Neuroinform 11:61","journal-title":"Front Neuroinform"},{"key":"10948_CR60","doi-asserted-by":"publisher","first-page":"358","DOI":"10.3389\/fnins.2020.00358","volume":"14","author":"A Mikhaylov","year":"2020","unstructured":"Mikhaylov A, Pimashkin A, Pigareva Y, Gerasimova S, Gryaznov E, Shchanikov S, Zuev A, Talanov M, Lavrov I, Demin V, Erokhin V (2020) Neurohybrid memristive CMOS-integrated systems for biosensors and neuroprosthetics. Front Neurosci 14:358","journal-title":"Front Neurosci"},{"key":"10948_CR61","doi-asserted-by":"crossref","unstructured":"Mishra S, Bhargavi K (2021) An AI model for neurodegenerative diseases. In 2021 international conference on computer communication and informatics (ICCCI) (pp. 1\u20136). IEEE.","DOI":"10.1109\/ICCCI50826.2021.9402493"},{"issue":"4","key":"10948_CR62","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1109\/TNNLS.2017.2657601","volume":"29","author":"A Morro","year":"2017","unstructured":"Morro A, Canals V, Oliver A, Alomar ML, Gal\u00e1n-Prado F, Ballester PJ, Rossell\u00f3 JL (2017) A stochastic spiking neural network for virtual screening. IEEE Transact Neural Networks Learn Syst 29(4):1371\u20131375","journal-title":"IEEE Transact Neural Networks Learn Syst"},{"key":"10948_CR63","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1016\/j.neubiorev.2016.07.010","volume":"68","author":"AA Moustafa","year":"2016","unstructured":"Moustafa AA, Chakravarthy S, Phillips JR, Gupta A, Keri S, Polner B, Frank MJ, Jahanshahi M (2016) Motor symptoms in Parkinson\u2019s disease: a unified framework. Neurosci Biobehav Rev 68:727\u2013740","journal-title":"Neurosci Biobehav Rev"},{"key":"10948_CR64","doi-asserted-by":"publisher","first-page":"93","DOI":"10.3389\/fnsys.2017.00093","volume":"11","author":"O M\u00fcller","year":"2017","unstructured":"M\u00fcller O, Rotter S (2017) Neurotechnology: current developments and ethical issues. Front Syst Neurosci 11:93","journal-title":"Front Syst Neurosci"},{"issue":"3","key":"10948_CR65","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.3390\/molecules28031342","volume":"28","author":"M Nascimben","year":"2023","unstructured":"Nascimben M, Rimondini L (2023) Molecular toxicity virtual screening applying a quantized computational SNN-based framework. Molecules 28(3):1342","journal-title":"Molecules"},{"issue":"10","key":"10948_CR66","doi-asserted-by":"publisher","first-page":"3819","DOI":"10.1109\/TED.2016.2598413","volume":"63","author":"RA Nawrocki","year":"2016","unstructured":"Nawrocki RA, Voyles RM, Shaheen SE (2016) A mini review of neuromorphic architectures and implementations. IEEE Trans Electron Devices 63(10):3819\u20133829","journal-title":"IEEE Trans Electron Devices"},{"key":"10948_CR67","unstructured":"Nerella S, Bandyopadhyay S, Zhang J, Contreras M, Siegel S, Bumin A, Silva B, Sena J, Shickel B, Bihorac A, Khezeli K (2023) Transformers in healthcare: a survey. Preprint at arXiv:2307.00067"},{"key":"10948_CR68","unstructured":"Huy-Dung Nguyen (2023) Deep learning for the detection of neurological diseases. Image Processing [eess.IV]. Universit\u00e9 de Bordeaux. English.\u00a0<NNT : 2023BORD0288>.\u00a0<tel-04311995>"},{"key":"10948_CR69","doi-asserted-by":"crossref","unstructured":"O\u2019Leary G, Pazhouhandeh MR, Chang M, Groppe D, Valiante TA, Verma N, Genov R (2018) A recursive-memory brain-state classifier with 32-channel track-and-zoom \u0394 2 \u03a3 ADCs and charge-balanced programmable waveform neurostimulators. In 2018 IEEE international solid-state circuits conference-(ISSCC) (pp. 296\u2013298). IEEE","DOI":"10.1109\/ISSCC.2018.8310301"},{"issue":"7","key":"10948_CR70","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1001\/jamaneurol.2019.0849","volume":"76","author":"MS Okun","year":"2019","unstructured":"Okun MS (2019) Tips for choosing a deep brain stimulation device. JAMA Neurol 76(7):749\u2013750","journal-title":"JAMA Neurol"},{"issue":"11","key":"10948_CR71","doi-asserted-by":"publisher","first-page":"5313","DOI":"10.1007\/s00415-023-11873-1","volume":"270","author":"AM Oliveira","year":"2023","unstructured":"Oliveira AM, Coelho L, Carvalho E, Ferreira-Pinto MJ, Vaz R, Aguiar P (2023) Machine learning for adaptive deep brain stimulation in Parkinson\u2019s disease: closing the loop. J Neurol 270(11):5313\u20135326","journal-title":"J Neurol"},{"issue":"2","key":"10948_CR72","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1089\/brain.2011.0014","volume":"1","author":"WJ Pan","year":"2011","unstructured":"Pan WJ, Thompson G, Magnuson M, Majeed W, Jaeger D, Keilholz S (2011) Broadband local field potentials correlate with spontaneous fluctuations in functional magnetic resonance imaging signals in the rat somatosensory cortex under isoflurane anesthesia. Brain Connect 1(2):119\u2013131","journal-title":"Brain Connect"},{"issue":"3","key":"10948_CR73","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1109\/TED.2019.2963323","volume":"67","author":"WQ Pan","year":"2020","unstructured":"Pan WQ, Chen J, Kuang R, Li Y, He YH, Feng GR, Duan N, Chang TC, Miao XS (2020) Strategies to improve the accuracy of memristor-based convolutional neural networks. IEEE Trans Electron Devices 67(3):895\u2013901","journal-title":"IEEE Trans Electron Devices"},{"issue":"3","key":"10948_CR74","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1016\/j.neuron.2014.10.007","volume":"84","author":"DE Pankevich","year":"2014","unstructured":"Pankevich DE, Altevogt BM, Dunlop J, Gage FH, Hyman SE (2014) Improving and accelerating drug development for nervous system disorders. Neuron 84(3):546\u2013553","journal-title":"Neuron"},{"key":"10948_CR75","doi-asserted-by":"crossref","unstructured":"Parsa M (2020) Bayesian-based multi-objective hyperparameter optimization for accurate, fast, and efficient neuromorphic system designs. Doctoral dissertation, Purdue University Graduate School","DOI":"10.3389\/fnins.2020.00667"},{"issue":"3","key":"10948_CR76","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1038\/s41587-021-00856-0","volume":"39","author":"K Patch","year":"2021","unstructured":"Patch K (2021) Neural dust swept up in latest leap for bioelectronic medicine. Nat Biotechnol 39(3):255\u2013257","journal-title":"Nat Biotechnol"},{"key":"10948_CR77","doi-asserted-by":"crossref","unstructured":"Pawar K, Attar VZ (2020) Assessment of autoencoder architectures for data representation. Deep learning: concepts and architectures. pp 101\u201332","DOI":"10.1007\/978-3-030-31756-0_4"},{"key":"10948_CR78","doi-asserted-by":"publisher","first-page":"774","DOI":"10.3389\/fnins.2018.00774","volume":"12","author":"M Pfeiffer","year":"2018","unstructured":"Pfeiffer M, Pfeil T (2018) Deep learning with spiking neurons: opportunities and challenges. Front Neurosci 12:774","journal-title":"Front Neurosci"},{"issue":"9","key":"10948_CR79","doi-asserted-by":"publisher","first-page":"1316","DOI":"10.3390\/brainsci13091316","volume":"13","author":"MD Pham","year":"2023","unstructured":"Pham MD, D\u2019Angiulli A, Dehnavi MM, Chhabra R (2023) From brain models to robotic embodied cognition: how does biological plausibility inform neuromorphic systems? Brain Sci 13(9):1316","journal-title":"Brain Sci"},{"issue":"23","key":"10948_CR80","doi-asserted-by":"publisher","first-page":"5882","DOI":"10.1039\/D3MA00449J","volume":"4","author":"C Prakash","year":"2023","unstructured":"Prakash C, Gupta LR, Mehta A, Vasudev H, Tominov R, Korman E, Fedotov A, Smirnov V, Kesari KK (2023) Computing of neuromorphic materials: an emerging approach for bioengineering solutions. Mater Adv 4(23):5882\u20135919","journal-title":"Mater Adv"},{"issue":"4","key":"10948_CR81","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1016\/0022-2836(88)90564-5","volume":"202","author":"N Qian","year":"1988","unstructured":"Qian N, Sejnowski TJ (1988) Predicting the secondary structure of globular proteins using neural network models. J Mol Biol 202(4):865\u2013884","journal-title":"J Mol Biol"},{"key":"10948_CR82","doi-asserted-by":"publisher","first-page":"71730","DOI":"10.1109\/ACCESS.2019.2919163","volume":"7","author":"GC Qiao","year":"2019","unstructured":"Qiao GC, Hu SG, Wang JJ, Zhang CM, Chen TP, Ning N, Yu Q, Liu Y (2019) A neuromorphic-hardware oriented bio-plausible online-learning spiking neural network model. IEEE Access 7:71730\u201371740","journal-title":"IEEE Access"},{"issue":"1","key":"10948_CR83","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6463\/ac28bb","volume":"55","author":"M Ronchini","year":"2021","unstructured":"Ronchini M, Zamani M, Huynh HA, Rezaeiyan Y, Panuccio G, Farkhani H, Moradi F (2021) A CMOS-based neuromorphic device for seizure detection from LFP signals. J Phys D Appl Phys 55(1):014001","journal-title":"J Phys D Appl Phys"},{"key":"10948_CR84","unstructured":"Safa A, Ocket I, Bourdoux A, Sahli H, Catthoor F, Gielen G (2021) A new look at spike-timing-dependent plasticity networks for spatio-temporal feature learning. Preprint at arXiv:2111.00791"},{"key":"10948_CR85","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s10571-019-00733-0","volume":"40","author":"S Sahab-Negah","year":"2020","unstructured":"Sahab-Negah S, Hajali V, Moradi HR, Gorji A (2020) The impact of estradiol on neurogenesis and cognitive functions in Alzheimer\u2019s disease. Cell Mol Neurobiol 40:283\u2013299","journal-title":"Cell Mol Neurobiol"},{"key":"10948_CR86","doi-asserted-by":"crossref","unstructured":"Schemmel J, Br\u00fcderle D, Gr\u00fcbl A, Hock M, Meier K, Millner S (2010) A wafer-scale neuromorphic hardware system for large-scale neural modeling. In 2010 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1947\u20131950). IEEE.","DOI":"10.1109\/ISCAS.2010.5536970"},{"issue":"1","key":"10948_CR87","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1038\/s43588-021-00184-y","volume":"2","author":"CD Schuman","year":"2022","unstructured":"Schuman CD, Kulkarni SR, Parsa M, Mitchell JP, Kay B (2022) Opportunities for neuromorphic computing algorithms and applications. Nature Comput Sci 2(1):10\u201319","journal-title":"Nature Comput Sci"},{"key":"10948_CR88","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/S0072-9752(07)83013-2","volume":"83","author":"J Shahed","year":"2007","unstructured":"Shahed J, Jankovic J (2007) Motor symptoms in Parkinson\u2019s disease. Handb Clin Neurol 83:329\u2013342","journal-title":"Handb Clin Neurol"},{"issue":"1","key":"10948_CR89","doi-asserted-by":"publisher","first-page":"3095","DOI":"10.1038\/s41467-021-23342-2","volume":"12","author":"M Sharifshazileh","year":"2021","unstructured":"Sharifshazileh M, Burelo K, Sarnthein J, Indiveri G (2021) An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG. Nat Commun 12(1):3095","journal-title":"Nat Commun"},{"issue":"2","key":"10948_CR90","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1109\/MCAS.2022.3166331","volume":"22","author":"A Shrestha","year":"2022","unstructured":"Shrestha A, Fang H, Mei Z, Rider DP, Wu Q, Qiu Q (2022) A survey on neuromorphic computing: models and hardware. IEEE Circuits Syst Mag 22(2):6\u201335","journal-title":"IEEE Circuits Syst Mag"},{"key":"10948_CR91","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2023.1274575","author":"MA Siddique","year":"2023","unstructured":"Siddique MA, Zhang Y, An H (2023) Monitoring time domain characteristics of Parkinson\u2019s disease using 3D memristive neuromorphic system. Front Comput Neurosci. https:\/\/doi.org\/10.3389\/fncom.2023.1274575","journal-title":"Front Comput Neurosci"},{"key":"10948_CR92","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12929-019-0524-y","volume":"26","author":"MV Silva","year":"2019","unstructured":"Silva MV, Loures CD, Alves LC, de Souza LC, Borges KB, Carvalho MD (2019) Alzheimer\u2019s disease: risk factors and potentially protective measures. J Biomed Sci 26:1\u20131","journal-title":"J Biomed Sci"},{"key":"10948_CR93","doi-asserted-by":"publisher","DOI":"10.3389\/fnetp.2022.868092","volume":"2","author":"N Sinha","year":"2022","unstructured":"Sinha N, Joshi RB, Sandhu MR, Netoff TI, Zaveri HP, Lehnertz K (2022) Perspectives on understanding aberrant brain networks in epilepsy. Front Network Physiol 2:868092","journal-title":"Front Network Physiol"},{"issue":"9","key":"10948_CR94","doi-asserted-by":"publisher","first-page":"978","DOI":"10.1038\/s41587-019-0231-y","volume":"37","author":"E Smalley","year":"2019","unstructured":"Smalley E (2019) The business of brain-computer interfaces. Nat Biotechnol 37(9):978","journal-title":"Nat Biotechnol"},{"key":"10948_CR95","doi-asserted-by":"crossref","unstructured":"Song S, Das A (2020) A case for lifetime reliability-aware neuromorphic computing. Preprint at arXiv:2007.02210","DOI":"10.1109\/MWSCAS48704.2020.9184557"},{"issue":"4","key":"10948_CR96","doi-asserted-by":"publisher","first-page":"882","DOI":"10.17344\/acsi.2021.6875","volume":"68","author":"F Soualmia","year":"2021","unstructured":"Soualmia F, Belaidi S, Tchouar N, Lanez T, Boudergua S (2021) QSAR studies and structure property\/activity relationships applied in pyrazine derivatives as antiproliferative agents against the BGC823. Acta Chim Slov 68(4):882\u2013895","journal-title":"Acta Chim Slov"},{"key":"10948_CR97","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmech.2021.113320","volume":"216","author":"S Srivastava","year":"2021","unstructured":"Srivastava S, Ahmad R, Khare SK (2021) Alzheimer\u2019s disease and its treatment by different approaches: a review. Eur J Med Chem 216:113320","journal-title":"Eur J Med Chem"},{"issue":"7","key":"10948_CR98","doi-asserted-by":"publisher","first-page":"3166","DOI":"10.1021\/acs.jcim.9b00325","volume":"59","author":"N St\u00e5hl","year":"2019","unstructured":"St\u00e5hl N, Falkman G, Karlsson A, Mathiason G, Bostrom J (2019) Deep reinforcement learning for multiparameter optimization in de novo drug design. J Chem Inf Model 59(7):3166\u20133176","journal-title":"J Chem Inf Model"},{"key":"10948_CR99","unstructured":"Stroud C, DeFeo C, Strauss E, Norris SM (2015) Financial incentives to encourage development of therapies that address unmet medical needs for nervous system disorders: Workshop summary. National Academies Press"},{"key":"10948_CR100","doi-asserted-by":"publisher","first-page":"103705","DOI":"10.1016\/j.ajp.2023.103705","volume":"87","author":"J Sun","year":"2023","unstructured":"Sun J, Dong QX, Wang SW, Zheng YB, Liu XX, Lu TS, Yuan K, Shi J, Hu B, Lu L, Han Y (2023) Artificial intelligence in psychiatry research, diagnosis, and therapy. Asian J Psychiatry 87:103705","journal-title":"Asian J Psychiatry"},{"issue":"6","key":"10948_CR101","doi-asserted-by":"publisher","first-page":"1474","DOI":"10.1021\/acs.jcim.7b00188","volume":"57","author":"S Tian","year":"2017","unstructured":"Tian S, Wang X, Li L, Zhang X, Li Y, Zhu F, Hou T, Zhen X (2017) Discovery of novel and selective adenosine A2A receptor antagonists for treating Parkinson\u2019s disease through comparative structure-based virtual screening. J Chem Inf Model 57(6):1474\u20131487","journal-title":"J Chem Inf Model"},{"issue":"3","key":"10948_CR102","doi-asserted-by":"publisher","DOI":"10.1088\/2634-4386\/ace737","volume":"3","author":"J Timcheck","year":"2023","unstructured":"Timcheck J, Shrestha SB, Rubin DB, Kupryjanow A, Orchard G, Pindor L, Shea T, Davies M (2023) The intel neuromorphic DNS challenge. Neuromorphic Comput Eng 3(3):034005","journal-title":"Neuromorphic Comput Eng"},{"key":"10948_CR103","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120005","volume":"225","author":"LH Torres","year":"2023","unstructured":"Torres LH, Ribeiro B, Arrais JP (2023) Few-shot learning with transformers via graph embeddings for molecular property prediction. Expert Syst Appl 225:120005","journal-title":"Expert Syst Appl"},{"key":"10948_CR104","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.compbiomed.2018.05.019","volume":"99","author":"\u039a\u039c Tsiouris","year":"2018","unstructured":"Tsiouris \u039a\u039c, Pezoulas VC, Zervakis M, Konitsiotis S, Koutsouris DD, Fotiadis DI (2018) A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals. Comput Biol Med 99:24\u201337","journal-title":"Comput Biol Med"},{"issue":"4","key":"10948_CR105","doi-asserted-by":"publisher","first-page":"1800589","DOI":"10.1002\/admt.201800589","volume":"4","author":"NK Upadhyay","year":"2019","unstructured":"Upadhyay NK, Jiang H, Wang Z, Asapu S, Xia Q, Joshua YJ (2019) Emerging memory devices for neuromorphic computing. Adv Mater Technol 4(4):1800589","journal-title":"Adv Mater Technol"},{"key":"10948_CR106","first-page":"234","volume":"7","author":"TR Victor","year":"2020","unstructured":"Victor TR, Tsirka SE (2020) Microglial contributions to aberrant neurogenesis and pathophysiology of epilepsy. Neuroimmunol Neuroinflamm 7:234","journal-title":"Neuroimmunol Neuroinflamm"},{"issue":"7","key":"10948_CR107","doi-asserted-by":"publisher","first-page":"1916","DOI":"10.3390\/pharmaceutics15071916","volume":"15","author":"LK Vora","year":"2023","unstructured":"Vora LK, Gholap AD, Jetha K, Thakur RR, Solanki HK, Chavda VP (2023) Artificial intelligence in pharmaceutical technology and drug delivery design. Pharmaceutics 15(7):1916","journal-title":"Pharmaceutics"},{"issue":"7","key":"10948_CR108","doi-asserted-by":"publisher","first-page":"1600510","DOI":"10.1002\/aelm.201600510","volume":"3","author":"Z Wang","year":"2017","unstructured":"Wang Z, Wang L, Nagai M, Xie L, Yi M, Huang W (2017) Nanoionics-enabled memristive devices: strategies and materials for neuromorphic applications. Adv Electron Mater 3(7):1600510","journal-title":"Adv Electron Mater"},{"key":"10948_CR109","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhang L, Zhang W (2022) The development and application of artificial intelligence chips. In 2022 IEEE international conference on advances in electrical engineering and computer applications (AEECA) (pp. 689\u2013696). IEEE","DOI":"10.1109\/AEECA55500.2022.9918899"},{"key":"10948_CR110","unstructured":"World Health Organization (2006) Neurological disorders: public health challenges. World Health Organization"},{"issue":"140","key":"10948_CR111","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.neucom.2014.03.037","volume":"22","author":"X Yang","year":"2014","unstructured":"Yang X, Tan L, He L (2014) A robust least squares support vector machine for regression and classification with noise. Neurocomputing 22(140):41\u201352","journal-title":"Neurocomputing"},{"key":"10948_CR112","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.copbio.2021.10.012","volume":"72","author":"J Yoo","year":"2021","unstructured":"Yoo J, Shoaran M (2021) Neural interface systems with on-device computing: machine learning and neuromorphic architectures. Curr Opin Biotechnol 72:95\u2013101","journal-title":"Curr Opin Biotechnol"},{"key":"10948_CR113","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.preteyeres.2016.05.002","volume":"53","author":"L Yue","year":"2016","unstructured":"Yue L, Weiland JD, Roska B, Humayun MS (2016) Retinal stimulation strategies to restore vision: fundamentals and systems. Prog Retin Eye Res 53:21\u201347","journal-title":"Prog Retin Eye Res"},{"issue":"3","key":"10948_CR114","doi-asserted-by":"publisher","DOI":"10.1088\/2634-4386\/ace64c","volume":"3","author":"D Zendrikov","year":"2023","unstructured":"Zendrikov D, Solinas S, Indiveri G (2023) Brain-inspired methods for achieving robust computation in heterogeneous mixed-signal neuromorphic processing systems. Neuromorphic Comput Eng 3(3):034002","journal-title":"Neuromorphic Comput Eng"},{"issue":"20","key":"10948_CR115","doi-asserted-by":"publisher","first-page":"33864","DOI":"10.18632\/oncotarget.13060","volume":"8","author":"J Zhao","year":"2017","unstructured":"Zhao J, Li Z, Cong Y, Zhang J, Tan M, Zhang H, Geng N, Li M, Yu W, Shan P (2017) Repetitive transcranial magnetic stimulation improves cognitive function of Alzheimer\u2019s disease patients. Oncotarget 8(20):33864","journal-title":"Oncotarget"},{"issue":"1","key":"10948_CR116","doi-asserted-by":"publisher","DOI":"10.1063\/1.5124915","volume":"7","author":"M Zhao","year":"2020","unstructured":"Zhao M, Gao B, Tang J, Qian H, Wu H (2020) Reliability of analog resistive switching memory for neuromorphic computing. Appl Phys Rev 7(1):011301","journal-title":"Appl Phys Rev"},{"issue":"3","key":"10948_CR117","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1093\/bioinformatics\/btab715","volume":"38","author":"Q Zhao","year":"2022","unstructured":"Zhao Q, Zhao H, Zheng K, Wang J (2022) HyperAttentionDTI: improving drug\u2013protein interaction prediction by sequence-based deep learning with attention mechanism. Bioinformatics 38(3):655\u2013662","journal-title":"Bioinformatics"},{"issue":"9","key":"10948_CR118","doi-asserted-by":"publisher","first-page":"4287","DOI":"10.1109\/TNNLS.2017.2761335","volume":"29","author":"N Zheng","year":"2017","unstructured":"Zheng N, Mazumder P (2017) Online supervised learning for hardware-based multilayer spiking neural networks through the modulation of weight-dependent spike-timing-dependent plasticity. IEEE Transact Neural Networks Learn Syst 29(9):4287\u20134302","journal-title":"IEEE Transact Neural Networks Learn Syst"},{"issue":"5","key":"10948_CR119","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1109\/TBCAS.2021.3112756","volume":"15","author":"B Zhu","year":"2021","unstructured":"Zhu B, Shin U, Shoaran M (2021) Closed-loop neural prostheses with on-chip intelligence: a review and a low-latency machine learning model for brain state detection. IEEE Trans Biomed Circuits Syst 15(5):877\u2013897","journal-title":"IEEE Trans Biomed Circuits Syst"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-10948-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-024-10948-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-10948-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T10:08:30Z","timestamp":1731492510000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-024-10948-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,10]]},"references-count":124,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["10948"],"URL":"https:\/\/doi.org\/10.1007\/s10462-024-10948-3","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,10]]},"assertion":[{"value":"10 September 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"318"}}