BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Rome BEGIN:DAYLIGHT DTSTART:20260329T030000 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST END:DAYLIGHT BEGIN:STANDARD DTSTART:20261025T020000 TZOFFSETFROM:+0200 TZOFFSETTO:+0100 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260610T092505Z UID:CED239D3-2EAC-459C-8467-A2D166F66E3C DTSTART;TZID=Europe/Rome:20260609T140000 DTEND;TZID=Europe/Rome:20260609T160000 DESCRIPTION:Memristive devices establish a compelling connection between no nlinear physical phenomena\, biological dynamics\, and intelligent hardwar e\, opening alternative computational pathways beyond conventional digital architectures. Nature itself demonstrates efficient\, distributed\, and a daptive information processing mechanisms\, which can serve as blueprints for novel circuit-level computation paradigms. In parallel\, neurodegenera tive disorders such as Parkinson’s disease\, one of the most prevalent c onditions of its kind\, demand improved diagnostic and monitoring solution s supported by intelligent\, low-power hardware. In this context\, memrist ive networks can be exploited to model pathological dynamics\, while analo g front-end circuits enable accurate tremor detection\, one of the most ch aracteristic motor symptoms of the disease. Furthermore\, physics-informed and reaction-diffusion frameworks inspired by living systems\, including mycelium growth models\, provide compact and physically grounded approache s for capturing complex spatiotemporal behavior through a memristive reser voir layer relevant to disease characterization. Collectively\, this talk illustrates how memristive circuits evolve from passive memory components into adaptive computational substrates\, supporting biologically inspired modeling\, intelligent diagnostic platforms with closed-loop potential\, a nd neuromorphic control circuits (such as obstacle avoidance in mini-robot ic systems) that further demonstrate the versatility of this hardware para digm.\n\nSpeaker(s): \, Ioannis Chatzipaschalis\n\nRoom: Maxwell Room\, Bl dg: DET\, Corso Castelfidardo\, 42/A\, Turin\, Piemonte\, Italy\, 10129 LOCATION:Room: Maxwell Room\, Bldg: DET\, Corso Castelfidardo\, 42/A\, Turi n\, Piemonte\, Italy\, 10129 ORGANIZER:sb.polito@ieee.org SEQUENCE:26 SUMMARY:Intelligent Memristive Circuits for Bioengineering Applications URL;VALUE=URI:https://events.vtools.ieee.org/m/562095 X-ALT-DESC:Description: <br /><p>Memristive devices establish a compelling connection between nonlinear physical phenomena\, biological dynamics\, an d intelligent hardware\, opening alternative computational pathways beyond conventional digital architectures. Nature itself demonstrates efficient\ , distributed\, and adaptive information processing mechanisms\, which can serve as blueprints for novel circuit-level computation paradigms. In par allel\, neurodegenerative disorders such as Parkinson&rsquo\;s disease\, o ne of the most prevalent conditions of its kind\, demand improved diagnost ic and monitoring solutions supported by intelligent\, low-power hardware. In this context\, memristive networks can be exploited to model pathologi cal dynamics\, while analog front-end circuits enable accurate tremor dete ction\, one of the most characteristic motor symptoms of the disease. Furt hermore\, physics-informed and reaction-diffusion frameworks inspired by l iving systems\, including mycelium growth models\, provide compact and phy sically grounded approaches for capturing complex spatiotemporal behavior through a memristive reservoir layer relevant to disease characterization. Collectively\, this talk illustrates how memristive circuits evolve from passive memory components into adaptive computational substrates\, support ing biologically inspired modeling\, intelligent diagnostic platforms with closed-loop potential\, and neuromorphic control circuits (such as obstac le avoidance in mini-robotic systems) that further demonstrate the versati lity of this hardware paradigm.</p> END:VEVENT END:VCALENDAR