Neuronal Multi Unit Activity Processing with Metal Oxide Memristive Devices
Abstract Intra‐cortical brain‐machine interfaces (BMIs), able to decode neural activity in real‐time, represent a revolutionary opportunity for treating medical conditions. However, traditional systems focusing on single‐neuron spike detection require high processing rates and power, hindering the u...
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2024-12-01
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| Series: | Advanced Electronic Materials |
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| Online Access: | https://doi.org/10.1002/aelm.202400638 |
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| author | Caterina Sbandati Xiongfei Jiang Deepika Yadav Spyros Stathopoulos Dana Cohen Alex Serb Shiwei Wang Themis Prodromakis |
| author_facet | Caterina Sbandati Xiongfei Jiang Deepika Yadav Spyros Stathopoulos Dana Cohen Alex Serb Shiwei Wang Themis Prodromakis |
| author_sort | Caterina Sbandati |
| collection | DOAJ |
| description | Abstract Intra‐cortical brain‐machine interfaces (BMIs), able to decode neural activity in real‐time, represent a revolutionary opportunity for treating medical conditions. However, traditional systems focusing on single‐neuron spike detection require high processing rates and power, hindering the up‐scaling for neurons‐population monitoring in clinical application. An intriguing proposition is the memristive integrating sensor (MIS) approach, which uses resistive RAM (RRAM) for threshold‐based neural activity detection. MIS leverages analogue multi‐state switching properties of metal‐oxide RRAM to compress neural inputs by encoding above‐threshold events in resistance displacement, facilitating efficient data down‐sampling in the post‐processing, enabling low‐power, high‐channel systems. Initially tested on spikes and local field potentials, here MIS is adapted to process multi‐unit activity envelope (eMUA)—the envelope of entire spiking activity—which has recently been proposed as crucial input for real‐time neuro‐prosthetic control. Prior necessary modifications to the MIS for effective operation, this adaptation achieved over 95% sensitivity across two types of metal‐oxide devices: Pt/TiOx/Pt and TiN/HfOx/TiN, proving its platform‐agnostic capabilities. Furthermore, towards the integration of MIS with silicon chips, it is shown that it can reduce total system power consumption to below 1 µW, as RRAM encoding stage relaxes the signal preservation and noise requirements that challenge traditional complementary metal‐oxide‐semiconductor (CMOS) front‐ends. This eMUA‐MIS adaptation offers a viable pathway for developing more scalable and efficient BMIs for clinical use. |
| format | Article |
| id | doaj-art-9ac8941e73ef42818704b13ca2afa361 |
| institution | Kabale University |
| issn | 2199-160X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Advanced Electronic Materials |
| spelling | doaj-art-9ac8941e73ef42818704b13ca2afa3612025-01-09T11:51:13ZengWiley-VCHAdvanced Electronic Materials2199-160X2024-12-011012n/an/a10.1002/aelm.202400638Neuronal Multi Unit Activity Processing with Metal Oxide Memristive DevicesCaterina Sbandati0Xiongfei Jiang1Deepika Yadav2Spyros Stathopoulos3Dana Cohen4Alex Serb5Shiwei Wang6Themis Prodromakis7Centre for Electronics Frontiers Institute for Integrated Micro and Nano Systems School of Engineering The University of Edinburgh Edinburgh EH9 3BF UKCentre for Electronics Frontiers Institute for Integrated Micro and Nano Systems School of Engineering The University of Edinburgh Edinburgh EH9 3BF UKCentre for Electronics Frontiers Institute for Integrated Micro and Nano Systems School of Engineering The University of Edinburgh Edinburgh EH9 3BF UKCentre for Electronics Frontiers Institute for Integrated Micro and Nano Systems School of Engineering The University of Edinburgh Edinburgh EH9 3BF UKThe Gonda Brain Research Center Bar‐Ilan University Ramat‐Gan 52900 IsraelCentre for Electronics Frontiers Institute for Integrated Micro and Nano Systems School of Engineering The University of Edinburgh Edinburgh EH9 3BF UKCentre for Electronics Frontiers Institute for Integrated Micro and Nano Systems School of Engineering The University of Edinburgh Edinburgh EH9 3BF UKCentre for Electronics Frontiers Institute for Integrated Micro and Nano Systems School of Engineering The University of Edinburgh Edinburgh EH9 3BF UKAbstract Intra‐cortical brain‐machine interfaces (BMIs), able to decode neural activity in real‐time, represent a revolutionary opportunity for treating medical conditions. However, traditional systems focusing on single‐neuron spike detection require high processing rates and power, hindering the up‐scaling for neurons‐population monitoring in clinical application. An intriguing proposition is the memristive integrating sensor (MIS) approach, which uses resistive RAM (RRAM) for threshold‐based neural activity detection. MIS leverages analogue multi‐state switching properties of metal‐oxide RRAM to compress neural inputs by encoding above‐threshold events in resistance displacement, facilitating efficient data down‐sampling in the post‐processing, enabling low‐power, high‐channel systems. Initially tested on spikes and local field potentials, here MIS is adapted to process multi‐unit activity envelope (eMUA)—the envelope of entire spiking activity—which has recently been proposed as crucial input for real‐time neuro‐prosthetic control. Prior necessary modifications to the MIS for effective operation, this adaptation achieved over 95% sensitivity across two types of metal‐oxide devices: Pt/TiOx/Pt and TiN/HfOx/TiN, proving its platform‐agnostic capabilities. Furthermore, towards the integration of MIS with silicon chips, it is shown that it can reduce total system power consumption to below 1 µW, as RRAM encoding stage relaxes the signal preservation and noise requirements that challenge traditional complementary metal‐oxide‐semiconductor (CMOS) front‐ends. This eMUA‐MIS adaptation offers a viable pathway for developing more scalable and efficient BMIs for clinical use.https://doi.org/10.1002/aelm.202400638brain‐machine interfaceHfOxmetal‐oxide RRAMMISmulti‐unit activity envelopeTiOx |
| spellingShingle | Caterina Sbandati Xiongfei Jiang Deepika Yadav Spyros Stathopoulos Dana Cohen Alex Serb Shiwei Wang Themis Prodromakis Neuronal Multi Unit Activity Processing with Metal Oxide Memristive Devices Advanced Electronic Materials brain‐machine interface HfOx metal‐oxide RRAM MIS multi‐unit activity envelope TiOx |
| title | Neuronal Multi Unit Activity Processing with Metal Oxide Memristive Devices |
| title_full | Neuronal Multi Unit Activity Processing with Metal Oxide Memristive Devices |
| title_fullStr | Neuronal Multi Unit Activity Processing with Metal Oxide Memristive Devices |
| title_full_unstemmed | Neuronal Multi Unit Activity Processing with Metal Oxide Memristive Devices |
| title_short | Neuronal Multi Unit Activity Processing with Metal Oxide Memristive Devices |
| title_sort | neuronal multi unit activity processing with metal oxide memristive devices |
| topic | brain‐machine interface HfOx metal‐oxide RRAM MIS multi‐unit activity envelope TiOx |
| url | https://doi.org/10.1002/aelm.202400638 |
| work_keys_str_mv | AT caterinasbandati neuronalmultiunitactivityprocessingwithmetaloxidememristivedevices AT xiongfeijiang neuronalmultiunitactivityprocessingwithmetaloxidememristivedevices AT deepikayadav neuronalmultiunitactivityprocessingwithmetaloxidememristivedevices AT spyrosstathopoulos neuronalmultiunitactivityprocessingwithmetaloxidememristivedevices AT danacohen neuronalmultiunitactivityprocessingwithmetaloxidememristivedevices AT alexserb neuronalmultiunitactivityprocessingwithmetaloxidememristivedevices AT shiweiwang neuronalmultiunitactivityprocessingwithmetaloxidememristivedevices AT themisprodromakis neuronalmultiunitactivityprocessingwithmetaloxidememristivedevices |