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Bioengineering","2024-08-07T03:43:07.625Z","2024-08-14T02:06:46.293Z","2024-08-07T03:43:09.603Z","21",{"id":526,"name":527,"alternativeText":16,"caption":16,"width":528,"height":529,"formats":530,"hash":549,"ext":20,"mime":21,"size":550,"url":551,"previewUrl":16,"provider":24,"provider_metadata":16,"createdAt":552,"updatedAt":552},47,"NIH-BIB2.png",829,216,{"small":531,"medium":537,"thumbnail":543},{"ext":20,"url":532,"hash":533,"mime":21,"name":534,"path":16,"size":535,"width":492,"height":536},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/neuro24/small_NIH_BIB_2_32144643da.png","small_NIH_BIB_2_32144643da","small_NIH-BIB2.png",32.12,130,{"ext":20,"url":538,"hash":539,"mime":21,"name":540,"path":16,"size":541,"width":499,"height":542},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/neuro24/medium_NIH_BIB_2_32144643da.png","medium_NIH_BIB_2_32144643da","medium_NIH-BIB2.png",64.01,195,{"ext":20,"url":544,"hash":545,"mime":21,"name":546,"path":16,"size":547,"width":506,"height":548},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/neuro24/thumbnail_NIH_BIB_2_32144643da.png","thumbnail_NIH_BIB_2_32144643da","thumbnail_NIH-BIB2.png",10.62,64,"NIH_BIB_2_32144643da",19.59,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/neuro24/NIH_BIB_2_32144643da.png","2024-08-14T02:06:43.448Z",{"id":56,"variation":41,"button":554},[555],{"id":556,"label":515,"size":46,"color":47,"style":16,"icon":516,"iconPosition":49,"url":557,"newWindow":51,"downloadable":16,"shape":16},8,"https://www.nih.gov/about-nih/what-we-do/nih-almanac/national-institute-biomedical-imaging-bioengineering-nibib","-1",{"id":146,"name":560,"description":54,"createdAt":561,"updatedAt":562,"publishedAt":563,"url_path_id":564,"logo":565,"website":598,"url_path":602},"NIH: National Institute of Neurological Disorders and Stroke","2024-08-07T03:44:38.773Z","2024-08-07T20:03:18.510Z","2024-08-07T03:44:40.091Z","22",{"id":94,"name":566,"alternativeText":16,"caption":16,"width":567,"height":568,"formats":569,"hash":594,"ext":20,"mime":21,"size":595,"url":596,"previewUrl":16,"provider":24,"provider_metadata":16,"createdAt":597,"updatedAt":597},"NIH-NINDS.png",1842,422,{"large":570,"small":577,"medium":582,"thumbnail":588},{"ext":20,"url":571,"hash":572,"mime":21,"name":573,"path":16,"size":574,"width":575,"height":576},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/neuro24/large_NIH_NINDS_e15eed526e.png","large_NIH_NINDS_e15eed526e","large_NIH-NINDS.png",65.28,1000,229,{"ext":20,"url":578,"hash":579,"mime":21,"name":580,"path":16,"size":581,"width":492,"height":500},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/neuro24/small_NIH_NINDS_e15eed526e.png","small_NIH_NINDS_e15eed526e","small_NIH-NINDS.png",27.32,{"ext":20,"url":583,"hash":584,"mime":21,"name":585,"path":16,"size":586,"width":499,"height":587},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/neuro24/medium_NIH_NINDS_e15eed526e.png","medium_NIH_NINDS_e15eed526e","medium_NIH-NINDS.png",45.03,172,{"ext":20,"url":589,"hash":590,"mime":21,"name":591,"path":16,"size":592,"width":506,"height":593},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/neuro24/thumbnail_NIH_NINDS_e15eed526e.png","thumbnail_NIH_NINDS_e15eed526e","thumbnail_NIH-NINDS.png",11.7,56,"NIH_NINDS_e15eed526e",17.43,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/neuro24/NIH_NINDS_e15eed526e.png","2024-08-07T20:01:55.325Z",{"id":556,"variation":41,"button":599},[600],{"id":170,"label":515,"size":46,"color":47,"style":16,"icon":516,"iconPosition":49,"url":601,"newWindow":51,"downloadable":16,"shape":16},"https://www.ninds.nih.gov/","-2",{"id":120,"name":604,"description":54,"createdAt":605,"updatedAt":606,"publishedAt":607,"url_path_id":608,"logo":609,"website":643,"url_path":647},"National Science Foundation","2024-08-07T03:45:25.377Z","2024-08-07T20:02:58.206Z","2024-08-07T03:45:27.588Z","23",{"id":163,"name":610,"alternativeText":16,"caption":16,"width":611,"height":612,"formats":613,"hash":639,"ext":20,"mime":21,"size":640,"url":641,"previewUrl":16,"provider":24,"provider_metadata":16,"createdAt":642,"updatedAt":642},"NSF.png",1019,1025,{"large":614,"small":620,"medium":626,"thumbnail":632},{"ext":20,"url":615,"hash":616,"mime":21,"name":617,"path":16,"size":618,"width":619,"height":575},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/neuro24/large_NSF_545d316cac.png","large_NSF_545d316cac","large_NSF.png",703.32,994,{"ext":20,"url":621,"hash":622,"mime":21,"name":623,"path":16,"size":624,"width":625,"height":492},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/neuro24/small_NSF_545d316cac.png","small_NSF_545d316cac","small_NSF.png",229.17,497,{"ext":20,"url":627,"hash":628,"mime":21,"name":629,"path":16,"size":630,"width":631,"height":499},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/neuro24/medium_NSF_545d316cac.png","medium_NSF_545d316cac","medium_NSF.png",457.72,746,{"ext":20,"url":633,"hash":634,"mime":21,"name":635,"path":16,"size":636,"width":637,"height":638},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/neuro24/thumbnail_NSF_545d316cac.png","thumbnail_NSF_545d316cac","thumbnail_NSF.png",38.64,155,156,"NSF_545d316cac",149.28,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/neuro24/NSF_545d316cac.png","2024-08-07T20:01:56.054Z",{"id":170,"variation":41,"button":644},[645],{"id":40,"label":515,"size":46,"color":47,"style":16,"icon":516,"iconPosition":49,"url":646,"newWindow":51,"downloadable":16,"shape":16},"https://www.nsf.gov/","-3",{"pagination":649},{"page":5,"pageSize":29,"pageCount":5,"total":120},{"id":316,"heading":651,"pageHeader":652,"sections":653},"Panelist Abstracts",{"id":236,"description":16,"showPageHeader":51,"backgroundColor":58,"image":16},[654,707,797,866],{"id":81,"__component":655,"componentVariation":656,"contactsVariation":657,"styles":16,"header":658,"sessionsGroup":662},"content.sessions","Sessions Sidebar Navigation","Card Contact Full",{"id":296,"heading":659,"prose":54,"lead":660,"eyebrow":16,"badge":16,"componentVariation":661,"containerWidth":16,"image":16},"Topic 1: Epilepsy & Cortical Disorders (Neuromodulation)","Session 1 Abstract Submissions/Responses","Heading Center",[663],{"id":102,"groupTitle":16,"sessions":664},[665,676,686,696],{"id":296,"session":666},{"id":276,"title":667,"teaser":668,"body":669,"createdAt":670,"updatedAt":671,"publishedAt":672,"url_path_id":673,"contacts":674,"url_path":675},"Giacomo Valle","\u003Cp>\u003Cstrong>Abstract: \u003C/strong>What are the \"Needs, Challenges, Contributions, Impact and Investment\" opportunities for the field of neuromorphic technologies in medicine?\u003C/p>","\u003Col style=\"list-style-type:decimal;\">\u003Cli>\u003Cu>Needs:\u003C/u> The needs for the neuromorphic technologies in neuroprosthetics relate to the exploitation of biologically inspired process to interface, communicate and interact with the nervous system at multiple levels. Neuromorphic tech could potentially allow for a better information transfer between the artificial and biological systems. Moreover, the high level of portability and embeddability could boost the transability of these technologies into clinical applications.\u003C/li>\u003Cli>\u003Cu>Challenges:\u003C/u> The challenges are related to the exploitation of the sophisticated neuromorphic framework given the limits of the current available interfacing technology. Moreover, the neuromorphic technology is based on the emulation of the biological counterparts. Notably, important natural processes related to the target neural structures have not been defined or not exhaustively investigated yet, making the validation quite challenging.\u003C/li>\u003Cli>\u003Cu>Contribution:\u003C/u> The contribution of neuromorphic tech has shown interesting applications in artificial sensing (vision and touch) and computing. The flexibility of the technology, its embeddability could favor its application in everyday life settings (e.g. prosthetics). The benefit of exploit a computation based on neural emulation would allow for a better human-machine interfacing. Human neural processing has been shaped by evolution over thousands of years and it is flexible and efficient. Its emulation through neuromorphic tech opened important applications in medicine (neuromodulation, neuroprosthetics, etc.)\u003C/li>\u003Cli>\u003Cu>Impact:\u003C/u> the design of a future generation of bio-inspired neuroprosthetic devices allowing the artificial conveyance of more complex information and therapies with neurostimulation could drastically improve the field. The neuromorphic tech could potentially increase, not only the efficacy but also the acceptance of more bio-inspired neuroprosthetic devices. The use of neurostimulation is also critical to brain–computer interfacing, as well as bioelectronic medicine, in which the electrical stimulation targets the central, or the autonomic, nervous systems, respectively.\u003C/li>\u003Cli>\u003Cu>Investment: \u003C/u>As the healthcare sector increasingly adopts AI and advanced computational methods, neuromorphic technologies are likely to play a pivotal role, making them a compelling area for investment. Investing in neuromorphic technologies in medicine presents several promising opportunities: neuroprosthetics, diagnostics, mental health applications, wearable devices, rehabilitation technologies, and AI and machine learning in medicine.\u003C/li>\u003C/ol>","2024-10-18T22:20:09.149Z","2024-10-18T22:20:15.435Z","2024-10-18T22:20:15.394Z","107",[],"-71",{"id":236,"session":677},{"id":296,"title":678,"teaser":679,"body":54,"createdAt":680,"updatedAt":681,"publishedAt":682,"url_path_id":683,"contacts":684,"url_path":685},"Kendall Lee","\u003Cp>\u003Cstrong>Abstract: \u003C/strong>Modern neural engineering technologies now allow for control of neurotransmitter levels by deep brain stimulation. Artificial Intelligence will allow for automated neural control algorithms in the future.\u003C/p>\u003Cp>Further complex electro-analytical methodologies such as MCSWV, will also be available for neuropsychiatric therapies.\u003C/p>","2024-10-18T22:21:30.236Z","2024-10-18T22:35:13.969Z","2024-10-18T22:21:31.357Z","108",[],"-72",{"id":316,"session":687},{"id":236,"title":688,"teaser":689,"body":54,"createdAt":690,"updatedAt":691,"publishedAt":692,"url_path_id":693,"contacts":694,"url_path":695},"Chris Rozell","\u003Cp>\u003Cstrong>Abstract:\u003C/strong> Currently, commercial neurotechnologies are designed with very minimal processing capability, geared almost entirely toward very specific notions of what onboard processing is necessary for closed loop stimulation to treat specific disorders such as epilepsy and Parkinson's disease. There are presumably many constraints driving this narrow product design, including power consumption, form factor limitations, and the time necessary for new engineering designs that meet regulatory standards. Future innovations would benefit from an expanded set of capabilities in a package that improves the user experience in ways that will increase accessibility (e.g., smaller form factors, longer batter life, etc.).\u003C/p>","2024-10-18T22:21:53.825Z","2024-10-18T22:21:54.905Z","2024-10-18T22:21:54.900Z","109",[],"-73",{"id":113,"session":697},{"id":316,"title":698,"teaser":699,"body":700,"createdAt":701,"updatedAt":702,"publishedAt":703,"url_path_id":704,"contacts":705,"url_path":706},"Sydney Cash","\u003Cp>\u003Cstrong>Abstract: \u003C/strong>Neural interfaces, neuromodulatory systems and brain-computer interfaces, have made significant strides over the past decade, evidenced by ongoing clinical trials and increasing clinical applications. However, substantial challenges remain in harnessing their full potential for clinical use. On the engineering side, challenges such as energy efficiency, packaging, data management, and security are identified and the focus of research in multiple groups.\u003C/p>","\u003Cp>Conversely, our understanding of human brain physiology and its interface with these technologies often lags behind our engineering capabilities, which translates to a poor grasp of the fundamental constraints underlying neuromorphic approaches. For instance, in well-studied conditions like epilepsy, we still grapple with critical questions: Where do seizures originate?\u003C/p>\u003Cp>How do they start? What is the optimal scale for interacting with and mapping seizure activity? What areas should we target for modulation? Furthermore, we remain uncertain about the short-and long-term cellular responses to various forms of neuromodulation. To fully leverage the potential of brain-computer interfaces and neuromodulatory technologies, continued investment and interdisciplinary dialogue are essential. This cannot be achieved without integrating engineering advancements with biological understanding in a collaborative feedback loop.\u003C/p>","2024-10-18T22:22:59.909Z","2024-10-18T22:35:27.750Z","2024-10-18T22:23:02.069Z","110",[],"-74",{"id":146,"__component":655,"componentVariation":656,"contactsVariation":657,"styles":708,"header":710,"sessionsGroup":713},{"id":425,"edgeTop":57,"edgeBottom":57,"background":709,"containerWidth":16},"base-50",{"id":236,"heading":711,"prose":54,"lead":712,"eyebrow":16,"badge":16,"componentVariation":661,"containerWidth":16,"image":16},"Topic 2: Human-machine interfaces (Prosthetics)","Session 2 Abstract Submissions/Responses",[714],{"id":56,"groupTitle":16,"sessions":715},[716,726,736,747,757,767,777,787],{"id":29,"session":717},{"id":157,"title":718,"teaser":719,"body":54,"createdAt":720,"updatedAt":721,"publishedAt":722,"url_path_id":723,"contacts":724,"url_path":725},"R. James Cotton","\u003Cp>\u003Cstrong>Title:\u003C/strong> Clinical Gaps and Understanding Real World Outcomes with AI Technologies\u003C/p>\u003Cp>\u003Cstrong>Abstract:\u003C/strong> I will first discuss important clinical gaps for both upper and lower prosthesis users given current the existing technologies, touching on performance, safety, and usability. I will then emphasize the importance of understanding and optimizing real world function and the risks of an over-emphasize on laboratory measures and clinical outcomes assessments as surrogates for real world function. Finally, I will discuss how advances in computer vision and wearable sensors can better link these constructs and inform research.\u003C/p>","2024-10-18T22:01:42.472Z","2024-10-18T22:01:50.786Z","2024-10-18T22:01:50.779Z","99",[],"-63",{"id":212,"session":727},{"id":138,"title":728,"teaser":729,"body":54,"createdAt":730,"updatedAt":731,"publishedAt":732,"url_path_id":733,"contacts":734,"url_path":735},"Ranu Jung","\u003Cp>\u003Cstrong>Abstract: \u003C/strong>Neurotechnology holds the promise of enhancing our understanding and offering innovative treatment of both physical and mental health conditions. However, transitioning these advancements from the laboratory to clinical and home settings to transform healthcare remains a significant challenge. This talk discusses the use of a convergence approach for pioneering advancements in neurotechnology to revolutionize healthcare. The journey from concept to development and deployment of a Neural-Enabled Prosthetic Hand System in an FDA approved first-in-human trial will be used to illustrate the extensive collaboration across government, industry, academia, and the volunteer participants necessary to make major advances in human-machine interfaces.\u003C/p>","2024-10-18T22:03:24.757Z","2024-10-18T22:03:25.947Z","2024-10-18T22:03:25.940Z","100",[],"-64",{"id":193,"session":737},{"id":322,"title":738,"teaser":739,"body":740,"createdAt":741,"updatedAt":742,"publishedAt":743,"url_path_id":744,"contacts":745,"url_path":746},"Elisa Donati","\u003Cp>\u003Cstrong>Title:\u003C/strong> Neuromorphic Hardware for closed-loop in Healthcare\u003C/p>\u003Cp>\u003Cstrong>Abstract: \u003C/strong>Neuromorphic technology is at the forefront of revolutionizing healthcare by enabling the development of efficient, bio-inspired closed-loop systems that directly interact with biological tissues in real-time. In this talk, I will delve into how neuromorphic devices, which mimic the neural architecture of the brain, are transforming medical technologies, particularly in the areas of neuroprosthetics, wearable monitors, and chronic disease management.\u003C/p>\u003Cp>A key focus will be on how neuromorphic chips can replicate the behavior of biological neurons with high precision, facilitating seamless bioelectric signal processing for real-time, adaptive feedback. This is critical for closed-loop systems, where continuous monitoring and immediate adjustments to external stimuli are necessary to provide tailored therapeutic interventions. By leveraging these real-time capabilities, neuromorphic technologies allow for more autonomous, responsive medical devices that can improve patient outcomes, reduce intervention needs, and enhance the overall quality of life.\u003C/p>","\u003Cp>I will explore the unique advantages neuromorphic systems bring to neuroprosthetics, where real-time sensory feedback and motor control are essential. Neuromorphic devices enable the development of prosthetics that adapt dynamically to the user’s environment, providing a more natural experience by integrating sensory and motor loops. Their ability to function with ultra-low power and resource efficiency ensures that these systems can operate over extended periods without frequent maintenance, which is critical for wearable and implantable applications. Additionally, I will discuss how neuromorphic technologies optimize closed-loop healthcare systems through compact designs and power-efficient operations, allowing for longer device lifespans and fewer surgical interventions for implantable systems. These innovations are paving the way for intelligent healthcare solutions that combine biological inspiration with cutting-edge technology, offering more sustainable, adaptive, and personalized medical treatments.\u003C/p>","2024-10-18T22:04:16.562Z","2024-10-18T22:04:17.746Z","2024-10-18T22:04:17.741Z","101",[],"-65",{"id":337,"session":748},{"id":425,"title":749,"teaser":750,"body":54,"createdAt":751,"updatedAt":752,"publishedAt":753,"url_path_id":754,"contacts":755,"url_path":756},"Luke Osborn","\u003Cp>\u003Cstrong>Abstract: \u003C/strong>Luke Osborn: Neuromorphic encoding is necessary for reducing complex, high dimensional sensor signals (e.g., tactile, vision, auditory) into meaningful features for driving useful sensory feedback stimulation paradigms.\u003C/p>","2024-10-18T22:06:00.779Z","2024-10-18T22:06:01.847Z","2024-10-18T22:06:01.836Z","102",[],"-66",{"id":250,"session":758},{"id":440,"title":759,"teaser":760,"body":54,"createdAt":761,"updatedAt":762,"publishedAt":763,"url_path_id":764,"contacts":765,"url_path":766},"Xing Chen","\u003Cp>\u003Cstrong>Abstract: \u003C/strong>Needs: Clinical solutions that are safe, long-lived and effective, providing assistance in daily tasks, object recognition, navigation, and independence. Challenges: Technological challenges in interfacing with the brain with sufficient resolution and specificity, allowing neuromodulation without inducing seizures and aberrant activity, building devices that are easy to implant across clinical sites and individual patients. Contributions: Development of biocompatible materials, scientific understanding of neural encoding and decoding to improve specificity and efficacy of neuromodulation techniques. Impact: Providing hope and inspiration for blind and visually impaired people, and laying the foundation for breakthrough devices. Investment: Harnessing solutions from the lab and bringing them to patients. Recruitment of patients and other stakeholders to shape and guide the field from an early stage. Raising awareness amongst the general public of both the promise and pitfalls of this technology.\u003C/p>","2024-10-18T22:06:50.901Z","2024-10-18T22:06:51.955Z","2024-10-18T22:06:51.950Z","103",[],"-67",{"id":94,"session":768},{"id":218,"title":769,"teaser":770,"body":54,"createdAt":771,"updatedAt":772,"publishedAt":773,"url_path_id":774,"contacts":775,"url_path":776},"Helen Huang","\u003Cp>\u003Cstrong>Abstract:\u003C/strong> \u003C/p>\u003Col style=\"list-style-type:decimal;\">\u003Cli>The design principles for neural-machine interfaces (NMIs) should be anchored in established human motor control and learning theories.\u003C/li>\u003Cli>The design of an efferent neural decoder should be guided by human neuromechanics.\u003C/li>\u003Cli>The design of an afferent neural encoder should aim to enhance sensorimotor integration\u003C/li>\u003Cli>Neuromorphic computing and artificial intelligence are powerful tools for addressing NMI adaptations to individual users and for evolving over time, with low power consumption and distributed processing.\u003C/li>\u003C/ol>","2024-10-18T22:07:24.092Z","2024-10-18T22:08:03.578Z","2024-10-18T22:08:03.571Z","104",[],"-68",{"id":163,"session":778},{"id":343,"title":779,"teaser":780,"body":54,"createdAt":781,"updatedAt":782,"publishedAt":783,"url_path_id":784,"contacts":785,"url_path":786},"Nitish Thakor","\u003Cp>\u003Cstrong>Abstract: \u003C/strong>Prostheses, whether upper or lower limb for amputees, have concentrated on achieving mechatronic motion and control. Control is achieved through muscle, nerve or direct cortical interface, and using either noninvasive or invasive approaches. However, prosthesis in general and neuroprosthesis in particular are only now attending to incorporating sensory encoding and feedback. I will discuss the need to develop different sensory modalities and mimicking the sensory modalities in biomimetic manner. This would involve sensors that mimic receptors in the skin and encode them as neural spiking activity. This approach has many advantages in terms of encoding, but also poses challenges in terms of novel algorithms and novel approaches to neural interface and feedback. Therefore, my goal is to present some ideas and provoke discussion on sensors, sensory encoding, neural interface and achieving sensory prosthesis solutions for building future closed loop brain machine interface technologies.\u003C/p>","2024-10-18T22:12:03.626Z","2024-10-18T22:12:04.651Z","2024-10-18T22:12:04.646Z","105",[],"-69",{"id":204,"session":788},{"id":256,"title":789,"teaser":790,"body":54,"createdAt":791,"updatedAt":792,"publishedAt":793,"url_path_id":794,"contacts":795,"url_path":796},"Shih-Chii Liu","\u003Cp>\u003Cstrong>Abstract: \u003C/strong>Needs: How to develop a closed-loop system that can dynamically analyze the underlying responses of the area being stimulated for a prosthesis and to provide continuous meaningful feedback to the network outputting the stimulation signals.\u003C/p>\u003Cp>Challenges: On the modelling side, how can we build a model that can extract the needed information from the responses of the neurons in the stimulation area and also deals with the general variability of their responses over days? And how to build the decoder that generates the feedback signal from the currently relatively small number of stimulation electrodes?\u003C/p>\u003Cp>Contribution from Neuromorphic: For the technology and computing side, how can we ensure that the system can analyze the recordings in real-time and provide sparse, low-latency meaningful feedback signals with similar time constants as the brain area under stimulation. Neuromorphic sensors are already e.g. the neuromorphic event camera DVS produces an event-triggered low-latency stream of spikes to transient changes in the scene, for e.g. the DVS is used as the camera in the work by Botond Roska and partners to restore vision using optogenetics.”\u003C/p>","2024-10-18T22:14:22.195Z","2024-10-18T22:15:00.239Z","2024-10-18T22:15:00.233Z","106",[],"-70",{"id":120,"__component":655,"componentVariation":656,"contactsVariation":657,"styles":16,"header":798,"sessionsGroup":801},{"id":316,"heading":799,"prose":54,"lead":800,"eyebrow":16,"badge":16,"componentVariation":661,"containerWidth":16,"image":16},"Topic 3: Materials for Neuromorphics (Devices)","Session 3 Abstract Submissions/Responses",[802],{"id":556,"groupTitle":16,"sessions":803},[804,815,825,835,846,856],{"id":805,"session":806},33,{"id":127,"title":807,"teaser":808,"body":54,"createdAt":809,"updatedAt":810,"publishedAt":811,"url_path_id":812,"contacts":813,"url_path":814},"Duygu Kuzum","\u003Cp>\u003Cstrong>Abstract:\u003C/strong> The next leap in implantable neural interfaces requires technological advances in materials, devices, and computing paradigms. Multimodal approaches integrating optical and electrical sensing modalities can overcome spatiotemporal resolution limits of neural sensing as well as open up new avenues for non-invasive neural recording. Integration of sensing, computation and memory on a single array can enable real-time processing of neural signals for compact, low-power and high-throughput neuromorphic brain machine interfaces. Here, I will present this vision, its challenges, and discuss recent advances in the areas of transparent neural interfaces for multimodal recordings, neuromorphic approaches for on-chip neural processing and computational co-design at the system level for minimally invasive neural interfaces.\u003C/p>","2024-10-18T21:42:15.020Z","2024-10-18T21:45:09.033Z","2024-10-18T21:45:09.028Z","91",[],"-56",{"id":358,"session":816},{"id":56,"title":817,"teaser":818,"body":54,"createdAt":819,"updatedAt":820,"publishedAt":821,"url_path_id":822,"contacts":823,"url_path":824},"M. Anthony Lewis","\u003Cp>\u003Cstrong>Abstract:\u003C/strong> M. Anthony Lewis: Intelligent devices, from wearable health monitors to implants, increasingly surround us. As these technologies evolve, more efficient AI systems are crucial. Traditional deep learning, while powerful, struggles with edge deployment due to high computational and energy demands. We introduce Flow Machines, a novel paradigm for biomedical wearables leveraging state space models and neuromorphic computing. Unlike conventional frame-based deep learning, Flow Machines create internal representations of the external world incrementally, utilizing processing history. This approach contrasts with typical edge-deployed deep learning networks that recompute from scratch, ignoring past computations. Flow Machines accumulate internal representations over time, enabling more compact and efficient AI models suitable for resource-constrained wearables. By transcending traditional paradigms, we aim to enable sophisticated analysis and decision-making in a new class of intelligent biomedical devices. Our research co-optimizes Flow Machines using state space models as their core architecture. This method addresses edge computing limitations by leveraging internal state and incremental learning, achieving state-of-the-art performance across various edge challenges, from health monitoring to adaptive therapeutics. Flow Machines promise to revolutionize wearable healthcare technology, paving the way for personalized, context aware solutions and improved patient outcomes.\u003C/p>","2024-10-18T21:52:25.931Z","2024-10-18T21:52:27.242Z","2024-10-18T21:52:27.237Z","93",[],"-58",{"id":365,"session":826},{"id":556,"title":827,"teaser":828,"body":54,"createdAt":829,"updatedAt":830,"publishedAt":831,"url_path_id":832,"contacts":833,"url_path":834},"Gert Cauwenberghs","\u003Cp>\u003Cstrong>Abstract: \u003C/strong>Our work offers an efficient neuromorphic hardware-software co-design framework rectifying the energy-efficiency imbalance between the training and the inference phases in machine learning (ML) artificial intelligence (AI) systems. It leverages the great tolerance of task performance achievable during inference to memory leakage during training, to implement neuromorphic paradigms for energy-efficient memory throughout the learning phase. Hence it substantially reduces the overall energy footprint of ML-based AI, as it is dominated by the energy costs of training which requires significantly more parameter updates and memory writes compared to inference. Operating at multiple time-scales to implement synaptic metaplasticity where fast episodic-memory work in conjunction with one-shot continual online learning architectures, these learning-in memory systems strike a balance between the adaptation rate of the synaptic elements and their parameter retention capability to realize an optimal traded-off with respect to the energy dissipated per memory write.\u003C/p>","2024-10-18T21:53:14.973Z","2024-10-18T21:53:16.002Z","2024-10-18T21:53:15.997Z","94",[],"-59",{"id":384,"session":836},{"id":170,"title":837,"teaser":838,"body":839,"createdAt":840,"updatedAt":841,"publishedAt":842,"url_path_id":843,"contacts":844,"url_path":845},"Shantanu Chakrabarty","\u003Cp>\u003Cstrong>Abstract: \u003C/strong>Hybrid neural interfaces, comprising biological and synthetic neurons, provide a pathway for designing the next generation of neuroengineering systems. Existing wetware hardware implementations have non-overlapping architectural and operational boundaries, with each subsystem optimized independently using platform-specific signal representations. In contrast, a true hybrid neural interface should facilitate cross-mapping between biological and synthetic neurons, enabling the system to seamlessly optimize and delegate computational capabilities between the two domains. This type of interface would not only better control the capabilities of biological systems at the neural circuit level but also augment and repair neural functions at the system level. \u003C/p>","\u003Cp>Currently, several challenges exist in achieving such a hybrid interface. Besides the difficulties in designing biomaterials, electrodes, and optrodes that can seamlessly stimulate and record from biological neurons over long periods, and the challenge of long-term powering of such systems, a critical computational challenge lies in the mismatch at the interface between the neurobiological and neuromorphic domains. First, there is a bandwidth and interface challenge due to the lack of specificity in stimulation and recording, with efficacy that drifts over time. Second, there is a representation challenge arising from the mismatch between the complex spatiotemporal representations in coupled neuronal systems and the simpler spatiotemporal dynamics in current neuromorphic systems. These computational challenges prevent effective cross-mapping between biological and synthetic neurons. We believe that employing an energy-based or Hamiltonian-like joint modeling and synthesis of biological and neuromorphic systems might address these challenges. Such a hybrid neural interface could then continuously evolve and delegate functions across both neurobiology and neuromorphic systems.\u003C/p>","2024-10-18T21:54:07.019Z","2024-10-18T21:54:25.215Z","2024-10-18T21:54:25.210Z","95",[],"-60",{"id":329,"session":847},{"id":40,"title":848,"teaser":849,"body":54,"createdAt":850,"updatedAt":851,"publishedAt":852,"url_path_id":853,"contacts":854,"url_path":855},"Jennifer Hasler","\u003Cp>\u003Cstrong>Title: \u003C/strong>What Steps are Required to Reach a Silicon Cortex? \u003C/p>\u003Cp>\u003Cstrong>Abstract:\u003C/strong> We are now over 10 years from the Neurmorphic Roadmap paper outlining a path towards building a silicon cortex, and over 15 years from the start of the DARPA SyNAPSE program that promised to build a mouse brain at the end of the end of its five year program. A number of electronics advances over these last several years, as well as the wide interest in Neural Networks / Neuromorphic Engineering, make the opportunities even more possible if we understand the next major steps along these directions. These directions can have profound impact on both engineering applications and neuroscience. This discussion will highlight a few of these questions, including the importance of analog computation, neuron + dendritic models, and the need to effectively model the engineering-level computation and build cortical columns and cortical layers.\u003C/p>","2024-10-18T21:55:26.561Z","2024-10-18T21:55:27.677Z","2024-10-18T21:55:27.672Z","96",[],"-61",{"id":507,"session":857},{"id":44,"title":858,"teaser":859,"body":54,"createdAt":860,"updatedAt":861,"publishedAt":862,"url_path_id":863,"contacts":864,"url_path":865},"Ulkuhan Guler","\u003Cp>\u003Cstrong>Abstract: \u003C/strong>Biomedical sensors generate continuous streams of data, often requiring real time processing to ensure timely, accurate analysis and prompt action based on the interpretation of this information. By implementing neuromorphic techniques directly on chip, data can be processed efficiently with low power consumption, enabling wireless transmission of the processed signals. This reduces the need for off-chip data handling, resulting in compact, energy-efficient, and highly responsive biomedical systems, which are crucial for wearable or implantable health monitoring devices.\u003C/p>","2024-10-18T21:57:25.700Z","2024-10-18T21:57:28.224Z","2024-10-18T21:57:28.215Z","97",[],"-62",{"id":127,"__component":655,"componentVariation":656,"contactsVariation":657,"styles":867,"header":868,"sessionsGroup":871},{"id":440,"edgeTop":57,"edgeBottom":57,"background":709,"containerWidth":16},{"id":113,"heading":869,"prose":54,"lead":870,"eyebrow":16,"badge":16,"componentVariation":661,"containerWidth":16,"image":16},"Topic 4: (Medical Imaging, wearables and analysis)","Session 4 Abstract Submissions/Responses",[872,917],{"id":170,"groupTitle":16,"sessions":873},[874,886,898],{"id":875,"session":876},39,{"id":120,"title":877,"teaser":878,"body":879,"createdAt":880,"updatedAt":881,"publishedAt":882,"url_path_id":883,"contacts":884,"url_path":885},"Amparo Guemes","\u003Cp>\u003Cstrong>Abstract:\u003C/strong> Peripheral nerve recording and stimulation are vital for advancing neuromodulation therapies for chronic conditions like chronic pain, epilepsy, and diabetes. Conventional neural interfaces are limited by high power consumption, low adaptability, and the complexity of data processing in long-term applications. The demand for implantable devices that interface with the nervous system in real time necessitates innovative solutions. Neuromorphic computing utilizes biologically inspired designs characterized by event-based, asynchronous processing, which enables efficient real-time data handling while significantly reducing energy consumption, making it a promising approach. Spiking Neural Networks (SNNs) in this field allow for more biologically plausible representations of neural activity, enabling precise classification of neural signals and improved decision making for stimulation protocols. Moreover, SNNs can adaptively modify their response based on incoming signals, allowing for real-time adjustments in stimulation protocols. \u003C/p>","\u003Cp>Additionally, the integration of oscillators into neuromorphic architectures can enhance the temporal dynamics of neuromorphic systems, making them well-suited for continuous nerve stimulation applications. However, several challenges remain. Miniaturizing neuromorphic chips to fit implantable devices while ensuring biocompatibility and long-term stability is a significant hurdle. Furthermore, developing algorithms that can decode and stimulate nerve signals effectively while adapting to biological variability poses ongoing challenges. Recent contributions emphasize the integration of compression techniques compatible with neuromorphic systems, enabling more robust and efficient signal processing. Additionally, advancements in organic electrochemical neurons based on organic electrochemical transistors have demonstrated great promise due to their biocompatibility and ability to operate at low voltages, facilitating real-time analysis and response to neural signals. We will discuss how incorporating neuromorphic technologies in neuromodulation could revolutionize bioelectronic medicine, enabling more effective treatments for chronic disorders. We will also explore the importance of fostering cross-disciplinary collaboration among neural and electronics engineers, neuroscientists, data scientists, and clinicians, which is essential for accelerating the development of practical and scalable solutions.\u003C/p>","2024-10-18T21:41:36.962Z","2024-10-18T21:45:05.882Z","2024-10-18T21:45:05.876Z","90",[],"-55",{"id":887,"session":888},40,{"id":102,"title":889,"teaser":890,"body":891,"createdAt":892,"updatedAt":893,"publishedAt":894,"url_path_id":895,"contacts":896,"url_path":897},"Pamela Abshire","\u003Cp>\u003Cstrong>Abstract: \u003C/strong>The truly surprising thing about recent advances in neural networks (i.e. large language model etc.) is the extent to which functions that we generally think of as cognitive – like language and writing – can be accomplished by what is essentially the computational equivalent of a lookup table. What these neural networks are actually doing is finding the patterns and correlations inherent in these problems, and extrapolating from them to produce surprisingly complex outputs that seem to express thoughts. What they are really doing is piecing together bits of patterns from huge libraries of inputs in these spaces that they have seen many many many times, until they are able to pick out the patterns in these extremely high dimensional spaces.\u003C/p>","\u003Cp>For somewhat simpler inference and correlation problems in health care challenges, then neuromorphic systems should be able to provide adequate performance and functionality. For many applications, it remains a significant technical challenge to perform estimation and prediction tasks with very low power operation. Simple neuromorphic systems can achieve this by leveraging sparse signal representations alongside the correlations and dependencies inherent to many problem spaces. What are some standing challenges that meet these criteria (i.e. wearables, sensing with a communication/power bottleneck, application with remote agents or fast dynamics that necessitate closed loop feedback)?\u003C/p>\u003Cul>\u003Cli>Where is the low-hanging fruit in this field?\u003C/li>\u003Cli>Where do we find the efficiencies of representation and power that will give us killer medical apps for neuromorphic systems?\u003C/li>\u003C/ul>\u003Cp>I’d like to introduce another potential application and technology paradigm in this field – that of neuronal networks, built using living biological neurons as functional, programmable, and efficient hardware components and interfaces to body tissues, organs, and systems. One thing that we do know is that cells like to stick together; neurons and other cells in culture are often attracted to each other and form clumps. Many challenges arise when the immune system recognizes devices as foreign bodies and encapsulate or isolate them through inflammation responses, reducing their efficacy over time. In other cases, interfaces for neurons (i.e. sieves), struggle to maintain long term viable stable connections to neurons and nerves.\u003C/p>\u003Cul>\u003Cli>Can we overcome these challenges by leveraging the natural affinity of cells and neurons? This suggests the conjecture that neuronal networks could be the killer app for neuronal interfaces, particularly for low density neuronal populations such as the enteric nervous systems. There is much that we still don’t know about how computation is implemented when scaled up to larger networks of real neurons. In the last decade there have been substantial advances in the technology available to observe and control neurons at high temporal and spatial resolution (i.e. high-density microelectrode arrays, optogenetics, voltage sensitive dyes), offering the potential to gain new insight into neuronal computing mechanisms and to create new engineering approaches for realizing computation with real, complex neurons.\u003C/li>\u003Cli>What kinds of computations are possible to implement in a neural computer?\u003C/li>\u003Cli>What kinds of computations can be efficiently implemented in neural computers?\u003C/li>\u003Cli>What aspects give rise to the special capabilities of neuronal computation? The cells?\u003C/li>\u003C/ul>\u003Cp>Their connections? The plasticity mechanisms? The network architecture?\u003Cbr>When will this become feasible? What are the critical technologies to make this happen?\u003C/p>","2024-10-18T21:49:32.753Z","2024-10-18T22:36:00.384Z","2024-10-18T21:51:08.002Z","92",[],"-57",{"id":899,"session":900},41,{"id":113,"title":901,"teaser":902,"body":903,"createdAt":904,"updatedAt":905,"publishedAt":906,"url_path_id":907,"contacts":908,"url_path":916},"Robert Stevens","\u003Cp style=\"text-align:justify;\">\u003Cstrong>Abstract:\u003C/strong> Challenges Acute brain injuries (ABI) namely those sustained in the setting of trauma or cerebrovascular\u003Cbr>disease are leading global causes of death and disability, and they increase the likelihood of later-onset neurodegenerative disorders. Current interventions to treat ABI are marginally effective or ineffective, perhaps owing to unpredictable treatment effects seen in heterogeneous\u003Cbr>patient samples\u003C/p>","\u003Cp>Needs:\u003Cbr>There is an imperative need for a breakthrough paradigm which effectively restores brain health\u003Cbr>in individuals sustaining ABI\u003C/p>\u003Cp>Contributions:\u003Cbr>The leveraging of advanced computational simulations could significantly advance treatments\u003Cbr>for ABI. Digital twins are computational representations of a physical system that is dynamically\u003Cbr>updated with data to simulate its real-world counterpart. The digital twin is able to simulate not\u003Cbr>only the past and current states of the physical entity but also can predict future states by\u003Cbr>simulating how it will respond under different conditions.\u003Cbr>In the context of ABI, inputs to the digital twin might include continuous high frequency\u003Cbr>physiological data streams recorded with wearable or implantable sensors (e.g. EEG, ICP, PPG,\u003Cbr>ABP, ECG), neuroimaging data, and transcriptomic, epigenomic or proteomic expression arrays.\u003Cbr>Data-driven (inductive) and mechanistic (deductive) modeling approaches can be used to (a)\u003Cbr>infer imminent and delayed changes in clinical and physiological states, (b) predict clinically\u003Cbr>meaningful outcomes, and (c) test responses to specific therapeutic interventions.\u003C/p>\u003Cp>Impact:\u003Cbr>Successful design and implementation of a digital twin focused on ABI could enable\u003Cbr>individualized treatment strategies overcoming some of the limitations of heterogeneous\u003Cbr>treatment effects.\u003C/p>","2024-10-21T17:41:13.398Z","2024-10-21T17:44:46.944Z","2024-10-21T17:44:46.938Z","111",[909],{"id":910,"name":901,"committee":16,"position":16,"affiliation":911,"email":16,"biography":54,"createdAt":912,"updatedAt":912,"url_path_id":913,"contactPhoto":16,"socialLinks":914,"url_path":915},51,"Johns Hopkins University","2024-10-21T17:41:43.269Z","112",[],"-76","-75",{"id":40,"groupTitle":16,"sessions":918},[],{"data":920,"meta":925},{"id":316,"heading":651,"createdAt":921,"updatedAt":922,"publishedAt":16,"url_path_id":923,"url_path":924,"contentType":92},"2024-10-18T22:00:57.372Z","2024-10-21T17:44:22.217Z","98","/workshop-information/panelist-abstracts",{},1778851524619]