4.6 Article

Tunable Synaptic Plasticity in Crystallized Conjugated Polymer Nanowire Artificial Synapses

期刊

ADVANCED INTELLIGENT SYSTEMS
卷 2, 期 3, 页码 -

出版社

WILEY
DOI: 10.1002/aisy.201900176

关键词

conjugated polymers; nanowires; synaptic plasticity; neuromorphic devices; synaptic transistors

资金

  1. Tianjin Science Foundation for Distinguished Young Scholars [19JCJQJC61000]
  2. Guandong Key RD Project [2018B030338001]
  3. Hundred Young Academic Leaders Program of Nankai University [2122018218]
  4. Natural Science Foundation of Tianjin [18JCYBJC16000]
  5. 111 Project [B16027]
  6. International Cooperation Base [2016D01025]
  7. Tianjin International Joint Research and Development Center

向作者/读者索取更多资源

In biological synapses, short-term plasticity is important for computation and signal transmission, whereas long-term plasticity is essential for memory formation. Comparably, designing a strategy that can easily tune the synaptic plasticity of artificial synapses can benefit constructing an artificial neural system, where synapses with different short-term plasticity (STP) and long-term plasticity (LTP) are required. Herein, a strategy is designed that can easily tune the plasticity of crystallized conjugated polymer nanowire-based synaptic transistors (STs) by low-temperature solvent engineering. Essential synaptic functions are achieved, such as excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), spike-frequency-dependent plasticity (SFDP), spike-duration-dependent plasticity (SDDP) and spike-number-dependent plasticity (SNDP), and potentiation/depression. The balance between crystallinity and roughness is successfully adjusted by altering solvent compositions, and plasticity of the synaptic device is easily tuned between short term and long term. The evident transition from STP to LTP, good linearity and symmetry of potentiation and depression, and the broad dynamic working range of synaptic weight are achieved. This provides a facile way to tune synaptic plasticity at low temperatures and is applicable to future organic and flexible artificial nervous systems.

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