Leaky Integrate‐and‐Fire Neuron Based on Organic Electrochemical Transistor for Spiking Neural Networks with Temporal‐Coding
Abstract Spiking neural networks (SNNs) employ discrete spikes that mimic the firing of neurons in biological systems to process and transmit information. This characteristic enables SNNs to effectively capture temporal dynamics and capitalize on the time information inherent in time‐varying inputs,...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2024-03-01
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| Series: | Advanced Electronic Materials |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aelm.202300565 |
| _version_ | 1797269997905510400 |
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| author | Yuanyuan Zhu Xiang Wan Jie Yan Li Zhu Run Li Cheeleong Tan Zhihao Yu Liuyang Sun Shanchen Yan Yong Xu Huabin Sun |
| author_facet | Yuanyuan Zhu Xiang Wan Jie Yan Li Zhu Run Li Cheeleong Tan Zhihao Yu Liuyang Sun Shanchen Yan Yong Xu Huabin Sun |
| author_sort | Yuanyuan Zhu |
| collection | DOAJ |
| description | Abstract Spiking neural networks (SNNs) employ discrete spikes that mimic the firing of neurons in biological systems to process and transmit information. This characteristic enables SNNs to effectively capture temporal dynamics and capitalize on the time information inherent in time‐varying inputs, such as motion, audio/video streams, and other sequential data. Currently, most hardware implementations of SNNs are designed to use rate‐coding, where information is encoded in the rate of spikes. However, it still remains challenging for the hardware implementation of temporal coding in SNNs, which allows for higher input sparsity and exploits additional dimensions such as precise spike timing and relative spike timings. This study presents hardware implementations of SNNs constructed by organic electrochemical transistors (OECTs), processing temporal‐coded information. The protic dynamics in response to electrical stimuli enable the emulation of temporal integration, reset, and leaking of membrane potential in a simple leaky integrate‐and‐fire (LIF) neuron circuit. By utilizing these features, the emulated LIF neuron can be employed to construct SNNs capable of processing temporal‐coded information in complex tasks including coincidence detection and dynamic handwriting recognition, exhibiting high performance and good tolerance even when dealing with noisy datasets. |
| first_indexed | 2024-04-25T01:57:16Z |
| format | Article |
| id | doaj.art-411ef4c42399447f9ccaded421d1f13a |
| institution | Directory Open Access Journal |
| issn | 2199-160X |
| language | English |
| last_indexed | 2024-04-25T01:57:16Z |
| publishDate | 2024-03-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Advanced Electronic Materials |
| spelling | doaj.art-411ef4c42399447f9ccaded421d1f13a2024-03-07T15:46:04ZengWiley-VCHAdvanced Electronic Materials2199-160X2024-03-01103n/an/a10.1002/aelm.202300565Leaky Integrate‐and‐Fire Neuron Based on Organic Electrochemical Transistor for Spiking Neural Networks with Temporal‐CodingYuanyuan Zhu0Xiang Wan1Jie Yan2Li Zhu3Run Li4Cheeleong Tan5Zhihao Yu6Liuyang Sun7Shanchen Yan8Yong Xu9Huabin Sun10School of Integrated Circuit Science and EngineeringNanjing University of Posts and TelecommunicationsNanjing 210023 ChinaSchool of Integrated Circuit Science and EngineeringNanjing University of Posts and TelecommunicationsNanjing 210023 ChinaSchool of Integrated Circuit Science and EngineeringNanjing University of Posts and TelecommunicationsNanjing 210023 ChinaSchool of Integrated Circuit Science and EngineeringNanjing University of Posts and TelecommunicationsNanjing 210023 ChinaGuangdong Greater Bay Area Institute of Integrated Circuit and System Guangzhou 510535 ChinaSchool of Integrated Circuit Science and EngineeringNanjing University of Posts and TelecommunicationsNanjing 210023 ChinaSchool of Integrated Circuit Science and EngineeringNanjing University of Posts and TelecommunicationsNanjing 210023 ChinaShanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 ChinaSchool of Integrated Circuit Science and EngineeringNanjing University of Posts and TelecommunicationsNanjing 210023 ChinaSchool of Integrated Circuit Science and EngineeringNanjing University of Posts and TelecommunicationsNanjing 210023 ChinaSchool of Integrated Circuit Science and EngineeringNanjing University of Posts and TelecommunicationsNanjing 210023 ChinaAbstract Spiking neural networks (SNNs) employ discrete spikes that mimic the firing of neurons in biological systems to process and transmit information. This characteristic enables SNNs to effectively capture temporal dynamics and capitalize on the time information inherent in time‐varying inputs, such as motion, audio/video streams, and other sequential data. Currently, most hardware implementations of SNNs are designed to use rate‐coding, where information is encoded in the rate of spikes. However, it still remains challenging for the hardware implementation of temporal coding in SNNs, which allows for higher input sparsity and exploits additional dimensions such as precise spike timing and relative spike timings. This study presents hardware implementations of SNNs constructed by organic electrochemical transistors (OECTs), processing temporal‐coded information. The protic dynamics in response to electrical stimuli enable the emulation of temporal integration, reset, and leaking of membrane potential in a simple leaky integrate‐and‐fire (LIF) neuron circuit. By utilizing these features, the emulated LIF neuron can be employed to construct SNNs capable of processing temporal‐coded information in complex tasks including coincidence detection and dynamic handwriting recognition, exhibiting high performance and good tolerance even when dealing with noisy datasets.https://doi.org/10.1002/aelm.202300565leaky integrate‐and‐fireorganic electrochemical transistorspiking neural networkstemporal‐coding |
| spellingShingle | Yuanyuan Zhu Xiang Wan Jie Yan Li Zhu Run Li Cheeleong Tan Zhihao Yu Liuyang Sun Shanchen Yan Yong Xu Huabin Sun Leaky Integrate‐and‐Fire Neuron Based on Organic Electrochemical Transistor for Spiking Neural Networks with Temporal‐Coding Advanced Electronic Materials leaky integrate‐and‐fire organic electrochemical transistor spiking neural networks temporal‐coding |
| title | Leaky Integrate‐and‐Fire Neuron Based on Organic Electrochemical Transistor for Spiking Neural Networks with Temporal‐Coding |
| title_full | Leaky Integrate‐and‐Fire Neuron Based on Organic Electrochemical Transistor for Spiking Neural Networks with Temporal‐Coding |
| title_fullStr | Leaky Integrate‐and‐Fire Neuron Based on Organic Electrochemical Transistor for Spiking Neural Networks with Temporal‐Coding |
| title_full_unstemmed | Leaky Integrate‐and‐Fire Neuron Based on Organic Electrochemical Transistor for Spiking Neural Networks with Temporal‐Coding |
| title_short | Leaky Integrate‐and‐Fire Neuron Based on Organic Electrochemical Transistor for Spiking Neural Networks with Temporal‐Coding |
| title_sort | leaky integrate and fire neuron based on organic electrochemical transistor for spiking neural networks with temporal coding |
| topic | leaky integrate‐and‐fire organic electrochemical transistor spiking neural networks temporal‐coding |
| url | https://doi.org/10.1002/aelm.202300565 |
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