BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20261101T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20261005T195604Z UID:46BC0CB5-C389-4F89-8091-3342C7F1582A DTSTART;TZID=America/New_York:20261016T100000 DTEND;TZID=America/New_York:20261016T110000 DESCRIPTION:Brain-inspired computing hardware is emerging as an alternative to silicon complementary metal-oxide semiconductor chips in order to addr ess the energy crisis of rapidly increasing digital data generation and pr ocessing. Conventional non-volatile memories can realize highly paralleliz ed in-memory computing in neural networks\, but they lack the adaptability and reconfigurability that are the key attributes of low-energy biologica l systems. To this end\, neuromorphic devices based on low-dimensional nan omaterials embody bio-realistic tunable learning\, coupled state variables \, non-linear responses\, and multi-terminal architectures of synapses.[1- 5] In this talk\, I will make the case for two-dimensional (2D) nanomateri als for neuromorphic hardware by drawing parallels between fundamental pro perties\, form factors\, and required functionalities in artificial synaps es\, neurons\, and network architectures.[3\,6\,7] As a few examples\, MoS 2 memtransistors show bio-realistic synaptic learning in scalable crossbar arrays for higher-dimensional dynamic neural networks\, multiple-agent re inforcement learning\, and cerebellum-inspired anomaly detection.[7\,8\,9] Anomalous ferroelectricity in bilayer graphene moiré synaptic transistor s achieves unprecedented adaptive learning.[10] Dual-gated\, self-aligned Gaussian heterojunction transistors not only realize complex spiking behav ior but also enable low-power hardware for machine learning algorithms in edge computing.[11\,12] Bio-realistic spiking neurons can be printed on fl exible substrates and demonstrate biocompatibility by direct communication with mouse cerebellum cells.[13]\n\nRefs: [1] Chem Rev. (2025)\, [2] Natu re Electronics (2024)\, [3] Advanced Materials (2022)\, [4] Matter (2022)\ , [5] Nature Nano. (2020)\, [6] Nature Nano. (2015)\, [7] Nature (2018)\, [8] ACS Nano (2026)\, [9] Nature Comm (2026)\, [10] Nature (2023)\, [11] N ature Comm. (2020)\, [12] Nature Electronics (2023)\, [13] Nature Nano. (2 026)\n\nVirtual: https://events.vtools.ieee.org/m/582640 LOCATION:Virtual: https://events.vtools.ieee.org/m/582640 ORGANIZER:ehyang@ieee.org SEQUENCE:13 SUMMARY:Emerging Nanomaterials for Bio-Realistic Neuromorphic Computing URL;VALUE=URI:https://events.vtools.ieee.org/m/582640 X-ALT-DESC:Description: <br /><p class="MsoNormal" style="margin-bottom: 0i n\; text-align: justify\; text-justify: inter-ideograph\; line-height: nor mal\; layout-grid-mode: char\; background: white\; mso-layout-grid-align: none\;"><span lang="EN-IN" style="font-family: 'Times New Roman'\,serif\; color: #333333\;">Brain-inspired computing hardware is emerging as an alte rnative to silicon complementary metal-oxide semiconductor chips in order to address the energy crisis of rapidly increasing digital data generation and processing. Conventional non-volatile memories can realize highly par allelized in-memory computing in neural networks\, but they lack the adapt ability and reconfigurability that are the key attributes of low-energy bi ological systems. To this end\, neuromorphic devices based on low-dimensio nal nanomaterials embody bio-realistic tunable learning\, coupled state va riables\, non-linear responses\, and multi-terminal architectures of synap ses.<sup>[1-5]</sup> In this talk\, I will make the case for two-dimension al (2D) nanomaterials for neuromorphic hardware by drawing parallels betwe en fundamental properties\, form factors\, and required functionalities in artificial synapses\, neurons\, and network architectures.<sup>[3\,6\,7] </sup>As a few examples\, MoS<sub>2</sub> memtransistors show bio-realisti c synaptic learning in scalable crossbar arrays for higher-dimensional dyn amic neural networks\, multiple-agent reinforcement learning\, and cerebel lum-inspired anomaly detection.<sup>[7\,8\,9]</sup> Anomalous ferroelectri city in bilayer graphene moir&eacute\; synaptic transistors achieves unpre cedented adaptive learning.<sup>[10]</sup> Dual-gated\, self-aligned Gauss ian heterojunction transistors not only realize complex spiking behavior b ut also enable low-power hardware for machine learning algorithms in edge computing.<sup>[11\,12]</sup> Bio-realistic spiking neurons can be printed on flexible substrates and demonstrate biocompatibility by direct communi cation with mouse cerebellum cells.<sup>[13]</sup></span></p>\n<p class="M soNormal" style="margin-bottom: 0in\; line-height: normal\; layout-grid-mo de: char\; background: white\; mso-layout-grid-align: none\;"><span lang=" EN-IN" style="font-family: 'Times New Roman'\,serif\; color: #333333\;">&n bsp\;</span></p>\n<p class="MsoNormal" style="margin-bottom: 0in\; line-he ight: normal\; layout-grid-mode: char\; background: white\; mso-layout-gri d-align: none\;"><span lang="EN-IN" style="font-size: 9.0pt\; mso-bidi-fon t-size: 8.0pt\; font-family: 'Times New Roman'\,serif\; color: #333333\;"> Refs: [1] Chem Rev. (2025)\, [2] Nature Electronics (2024)\, [3] Advanced Materials (2022)\, [4] Matter (2022)\, [5] Nature Nano. (2020)\, [6] Natur e Nano. (2015)\, [7] Nature (2018)\, [8] ACS Nano (2026)\, [9] Nature Comm (2026)\, [10] Nature (2023)\, [11] Nature Comm. (2020)\, [12] Nature Elec tronics (2023)\, [13] Nature Nano. (2026)</span></p> END:VEVENT END:VCALENDAR