Neuromorphic computing and spiking neural networks (SNN) mimic the behavior of biological systems and have drawn interest for their potential to perform cognitive tasks with high energy efficiency. However, some factors such as temporal dynamics and spike timings prove critical for information processing but are often ignored by existing works, limiting the performance and applications of neuromorphic computing. On one hand, due to the lack of effective SNN training algorithms, it is difficult to utilize the temporal neural dynamics. Many existing algorithms still treat neuron activation statistically. On the other hand, utilizing temporal neural dynamics also poses challenges to hardware design. Synapses exhibit temporal dynamics, serving as memory units that hold historical information, but are often simplified as a connection with weight. Most current models integrate synaptic activations in some storage medium to represent membrane potential and institute a hard reset of membrane potential after the neuron emits a spike. This is done for its simplicity in hardware, requiring only a "clear" signal to wipe the storage medium, but destroys temporal information stored in the neuron. In this work, we derive an efficient training algorithm for Leaky Integrate and Fire neurons, which is capable of training a SNN to learn complex spatial temporal patterns. We achieved competitive accuracy on two complex datasets. We also demonstrate the advantage of our model by a novel temporal pattern association task. Codesigned with this algorithm, we have developed a CMOS circuit implementation for a memristor-based network of neuron and synapses which retains critical neural dynamics with reduced complexity. This circuit implementation of the neuron model is simulated to demonstrate its ability to react to temporal spiking patterns with an adaptive threshold.

中文翻译：

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用于时间模式学习的神经形态算法-硬件协同设计

神经形态计算和尖峰神经网络（SNN）模仿生物系统的行为，并以其潜在的潜力以高能效执行认知任务而引起了人们的兴趣。但是，某些因素（例如时间动态性和尖峰定时）被证明对信息处理至关重要，但现有工作经常忽略这些因素，从而限制了神经形态计算的性能和应用。一方面，由于缺乏有效的SNN训练算法，因此难以利用时间神经动力学。许多现有算法仍在统计学上处理神经元激活。另一方面，利用时间神经动力学也对硬件设计提出了挑战。突触表现出时间动态，用作保存历史信息的存储单元，但通常会简化为与重量的关系。当前大多数模型将突触激活整合到某些存储介质中以表示膜电位，并在神经元发出尖峰后建立膜电位的硬复位。这样做是为了简化硬件，仅需要“清除”信号即可擦除存储介质，但会破坏存储在神经元中的时间信息。在这项工作中，我们导出了一个针对泄漏整合和火灾神经元的有效训练算法，该算法能够训练SNN来学习复杂的空间时间模式。我们在两个复杂的数据集上获得了竞争性的准确性。我们还通过新颖的时间模式关联任务证明了我们模型的优势。使用此算法进行代码签名，我们已经为基于忆阻器的神经元和突触网络开发了CMOS电路实现，该电路保留了关键的神经动力学，并降低了复杂性。对神经元模型的这种电路实现进行了仿真，以证明其对具有自适应阈值的时间尖峰模式做出反应的能力。