[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81931-en":3,"doc-seo-81931-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},81931,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Neuromorphic Silicon Neuron Controller for Adaptive Deep Brain Stimulation in Parkinson’s Disease","Parkinson’s disease causes severe motor symptoms driven by abnormal beta-band activity in the cortico-basal ganglia-thalamic loop. Adaptive deep brain stimulation (aDBS) can track these fluctuations using real-time neural biomarkers, but existing methods are often algorithmic and not focused on low-power, implantable circuit realization. The work introduces a neuromorphic silicon leaky integrate-and-fire (SiLIF-DBS) controller in CMOS, validated through a computational surrogate embedded in a Parkinsonian closed-loop framework using STN-LFP Beta ARV.","Neuromorphic Silicon Neuron Controller for Adaptive Deep Brain Stimulation in Parkinson’s  \nDisease  \nMd Abu Bakr Siddique 1 , Jakub Orłowski2 , Yan Zhang3 , and Hongyu An 1  \n1Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, Michigan, USA  \n2 School of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland  \n3Department of Biological Sciences, Michigan Technological University, Houghton, Michigan, USA  \n[1](1 msiddiq5@mtu.edu)[ msiddiq5@mtu.edu](1 msiddiq5@mtu.edu), [2](2jakub.orlowski@ucd.ie)[jakub.orlowski@ucd.ie](2jakub.orlowski@ucd.ie), [3](3 yzhang49@mtu.edu)[ yzhang49@mtu.edu](3 yzhang49@mtu.edu), [1](1 hongyua@mtu.edu)[ hongyua@mtu.edu](1 hongyua@mtu.edu)  \narXiv :2607 .05453v 1 [ cs .AR] 5 Jul 2026  \nAbstract—Parkinson’s disease (PD) affects millions worldwide and causes severe motor symptoms. Adaptive deep brain stimulation (aDBS) delivers physiologically informed stimulation that can track fluctuations in PD motor symptoms, enabling more intelligent DBS control. However, most existing aDBS approaches are primarily algorithm- and software-driven, with limited efforts toward circuit realization, particularly low-power and implantable integrated circuits. This paper presents the Silicon Leaky Integrate-and-Fire Deep Brain Stimulation (SiLIFDBS) controller, a neuromorphic silicon neuron stimulator implemented with metal-oxide-semiconductor (CMOS) technology. For system-level evaluation, a simplified computational model of the SiLIF-DBS controller is derived and embedded within a Parkinsonian cortico-basal ganglia framework for closed-loop validation. The system is driven by beta-band subthalamic nucleus local field potentials (STN-LFPs), with their average rectified value (Beta ARV) used as the control biomarker. Our SiLIF-DBS controller for aDBS suppresses pathological beta activity while consuming only 25% of the power required by open-loop stimulation and achieving a suppression efficiency of 5.85%/µW. Overall, our SiLIF-DBS controller achieves strong beta suppression at substantially reduced power, delivering high suppression efficiency that demonstrates it is a viable foundation for low-power implantable aDBS.  \nIndex Terms—Adaptive Deep Brain Stimulation; Parkinson’s Disease; Neuromorphic Computing; Leaky Integrate-and-fire Neuron.  \nI. INTRODUCTION  \nParkinson’s disease (PD) is the second most common neurodegenerative disorder and affects more than 1% of the population above the age of 60 years old around the world [1] . PD is a progressive neurodegenerative disorder whose motor symptoms are linked to abnormal rhythmic activity in the cortico-basal-ganglia-thalamic loop, particularly elevated beta-band (13-30 Hz) oscillations in the subthalamic nucleus (STN) [2]–[4] . Conventional deep brain stimulation (DBS) remains clinically effective, but it delivers stimulation continuously despite fluctuations in the pathological state, increasing battery drain, pulse-generator duty cycle, power consumption, and stimulation-related side effects [3] . These  \nlimitations have motivated the development of adaptive DBS (aDBS), in which neural biomarkers are monitored in real time and the stimulation parameters are optimized based on the symptom-related neural activity [2], [5]–[7] . The pathological beta activity (13-30 Hz) is not merely elevated on average but is often organized into bursts whose duration and amplitude correlate with clinical impairment [8] . Thus, the controllers of aDBS system can be designed using STN local field potentials (STN-LFP), smoothed beta envelopes, burst metrics, and beta average-rectified value (Beta ARV) features [6], [9]–[11] . The state-of-the-art (SOTA) methods and algorithms include threshold-based, proportional and proportional-integral, fuzzy, reinforcement learning, and machine learning [12]–[17] . These studies demonstrate that closed-loop DBS can reduce stimulation demand while preserving therapeutic efficacy. H","cbCaijpssLKXOZQi","https://ap.wps.com/l/cbCaijpssLKXOZQi","pdf",2456163,1,9,"English","en",105,"# Introduction\n## Adaptive DBS and beta biomarkers\n## Limitations of algorithmic approaches\n## Motivation for neuromorphic circuits\n## Contributions of the SiLIF-DBS controller","[{\"question\":\"What is the main goal of the SiLIF-DBS controller in this work?\",\"answer\":\"To realize adaptive deep brain stimulation using a neuromorphic silicon neuron controller that suppresses pathological beta activity in Parkinson’s disease while meeting ultra-low-power requirements for implantable devices.\"},{\"question\":\"Which neural signal and biomarker drive the closed-loop control?\",\"answer\":\"The system uses beta-band subthalamic nucleus local field potentials (STN-LFPs), and the average rectified value (Beta ARV) serves as the control biomarker.\"},{\"question\":\"How is the controller implemented and validated?\",\"answer\":\"The controller is implemented as a refractory-enabled silicon leaky integrate-and-fire neuron in CMOS. A simplified computational surrogate is derived and embedded in a Parkinsonian cortico-basal ganglia framework for closed-loop validation.\"}]",1784177119,23,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"neuromorphic-silicon-neuron-controller-for-adaptive-deep-brain-stimulation-in-parkinsons-disease","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/neuromorphic-silicon-neuron-controller-for-adaptive-deep-brain-stimulation-in-parkinsons-disease/81931/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the main goal of the SiLIF-DBS controller in this work?","Question",{"text":74,"@type":75},"To realize adaptive deep brain stimulation using a neuromorphic silicon neuron controller that suppresses pathological beta activity in Parkinson’s disease while meeting ultra-low-power requirements for implantable devices.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which neural signal and biomarker drive the closed-loop control?",{"text":79,"@type":75},"The system uses beta-band subthalamic nucleus local field potentials (STN-LFPs), and the average rectified value (Beta ARV) serves as the control biomarker.",{"name":81,"@type":72,"acceptedAnswer":82},"How is the controller implemented and validated?",{"text":83,"@type":75},"The controller is implemented as a refractory-enabled silicon leaky integrate-and-fire neuron in CMOS. A simplified computational surrogate is derived and embedded in a Parkinsonian cortico-basal ganglia framework for closed-loop validation.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":124,"slug":125},"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]