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Updated: Jun 7, 2025

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
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记忆性泄漏的整合和发射神经元和可学习的直通估计器在尖端神经网络中

Tao Chen1, Chunyan She1, Lidan Wang1,2,3

  • 1College of Artificial Intelligence, Southwest University, Chongqing, 400715 China.

Cognitive neurodynamics
|November 18, 2024
PubMed
概括

尖端神经网络 (SNN) 通过使用一种新型记忆漏洞整合和火 (MLIF) 模型和可学习值估计器 (LSTE) 增强. 这提高了SNN的学习和表达,在神经形态数据集上实现了高精度.

关键词:
适应性的替代品梯度渐变.异质性 异质性 异质性可学习的射击门.记忆力 记忆力 记忆力尖的神经网络的神经网络.

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