Compared to conventional artificial neurons that produce dense and real-valued responses, biologically-inspired spiking neurons transmit sparse and binary information, which can also lead to energy-efficient implementations. Recent research has shown that spiking neural networks can be trained like standard recurrent neural networks using the surrogate gradient method. They have shown promising results on speech command recognition tasks. Using the same technique, we show that they are scalable to large vocabulary continuous speech recognition, where they are capable of replacing LSTMs in the encoder with only minor loss of performance. This suggests that they may be applicable to more involved sequence-to-sequence tasks. Moreover, in contrast to their recurrent non-spiking counterparts, they show robustness to exploding gradient problems without the need to use gates.

自动语音识别（ASR）是一种能力，使程序能够将人类演讲进入书面形式。人工智能（AI）的最新发展导致基于深神经网络的高精度ASR系统，例如经常性神经网络传感器（RNN-T）。然而，这些方法的核心组件和所执行的操作从强大的生物对应，即人脑中脱离。另一方面，基于尖刺神经网络（SNNS）的生物启发模型中的当前发展，落后于准确性并主要关注小规模应用。在这项工作中，我们通过从大脑中发现的多样性神经和突触动态吸引灵感来重新审视生物学上可合理的模型并大大提高他们的能力。特别是，我们介绍了模拟轴体和轴突突触的神经连接概念。基于此，我们提出了具有丰富神经突触动态的新型深度学习单元，并将它们集成到RNN-T架构中。我们首次展示，与现有的深度学习模型相比，大规模ASR模型的生物学现实实际实施可以产生竞争性能水平。具体地，我们表明这种实现具有若干优点，例如降低的计算成本和更低的延迟，这对于语音识别应用至关重要。

我们提出了一种新的学习算法，使用传统的人工神经网络（ANN）作为代理训练尖刺神经网络（SNN）。我们分别与具有相同网络架构和共享突触权重的集成和火（IF）和Relu神经元进行两次SNN和ANN网络。两个网络的前进通过完全独立。通过假设具有速率编码的神经元作为Relu的近似值，我们将SNN中的SNN的误差进行了回复，以更新共享权重，只需用SNN的ANN最终输出替换ANN最终输出。我们将建议的代理学习应用于深度卷积的SNNS，并在Fahion-Mnist和CiFar10的两个基准数据集上进行评估，分别为94.56％和93.11％的分类准确性。所提出的网络可以优于培训的其他深鼻涕，训练，替代学习，代理梯度学习，或从深处转换。转换的SNNS需要长时间的仿真时间来达到合理的准确性，而我们的代理学习导致高效的SNN，模拟时间较短。

穗状花序的神经形状硬件占据了深度神经网络（DNN）的更节能实现的承诺，而不是GPU的标准硬件。但这需要了解如何在基于事件的稀疏触发制度中仿真DNN，否则能量优势丢失。特别地，解决序列处理任务的DNN通常采用难以使用少量尖峰效仿的长短期存储器（LSTM）单元。我们展示了许多生物神经元的面部，在每个尖峰后缓慢的超积极性（AHP）电流，提供了有效的解决方案。 AHP电流可以轻松地在支持多舱神经元模型的神经形状硬件中实现，例如英特尔的Loihi芯片。滤波近似理论解释为什么AHP-Neurons可以模拟LSTM单元的功能。这产生了高度节能的时间序列分类方法。此外，它为实现了非常稀疏的大量大型DNN来实现基础，这些大型DNN在文本中提取单词和句子之间的关系，以便回答有关文本的问题。

超低功耗本地信号处理是始终安装在设备上的边缘应用的关键方面。尖刺神经网络的神经形态处理器显示出很大的计算能力，同时根据该领域的需要满足有限的电力预算。在这项工作中，我们提出了尖峰神经动力学作为扩张时间卷积的自然替代品。我们将这个想法扩展到WaveSense，这是一个由Wavenet Architects的激发灵感的尖峰神经网络。WaveSense使用简单的神经动力学，固定时间常数和简单的前馈结构，因此特别适用于神经形态实现。我们在几个数据集中测试此模型的功能，以用于关键字斑点。结果表明，该网络击败了其他尖刺神经网络的领域，并达到了诸如CNN和LSTM的人工神经网络的最先进的性能。

大脑中尖刺神经元之间的沟通的事件驱动和稀疏性质对灵活和节能的AI来说具有很大的承诺。学习算法的最新进展已经证明，与标准经常性神经网络相比，可以有效地培训尖刺神经元的复发网络以实现竞争性能。尽管如此，随着这些学习算法使用错误 - 反复通过时间（BPTT），它们遭受了高的内存要求，慢慢训练，并且与在线学习不兼容。这将这些学习算法的应用限制为相对较小的网络和有限的时间序列长度。已经提出了具有较低计算和内存复杂性的BPTT的在线近似（E-PROP，OSTL），但在实践中也遭受内存限制，并且作为近似，不要倾销标准BPTT训练。在这里，我们展示了最近开发的BPTT替代方法，通过时间（FPTT）可以应用于尖峰神经网络。与BPTT不同，FPTT试图最大限度地减少损失的持续动态正常风险。结果，可以以在线方式计算FPTT，并且相对于序列长度具有固定的复杂性。与新型动态尖刺神经元模型结合时，液态常数神经元，我们表明SNNS培训了FPTT优于在线BPTT近似，并在时间分类任务上接近或超过离线BPTT精度。因此，这种方法使得在长期序列中以记忆友好的在线方式训练SNNS并向新颖和复杂的神经架构进行扩展。

In the past years, artificial neural networks (ANNs) have become the de-facto standard to solve tasks in communications engineering that are difficult to solve with traditional methods. In parallel, the artificial intelligence community drives its research to biology-inspired, brain-like spiking neural networks (SNNs), which promise extremely energy-efficient computing. In this paper, we investigate the use of SNNs in the context of channel equalization for ultra-low complexity receivers. We propose an SNN-based equalizer with a feedback structure akin to the decision feedback equalizer (DFE). For conversion of real-world data into spike signals we introduce a novel ternary encoding and compare it with traditional log-scale encoding. We show that our approach clearly outperforms conventional linear equalizers for three different exemplary channels. We highlight that mainly the conversion of the channel output to spikes introduces a small performance penalty. The proposed SNN with a decision feedback structure enables the path to competitive energy-efficient transceivers.

我们最近提出了S4NN算法，基本上是对多层尖峰神经网络的反向化的适应，该网上网络使用简单的非泄漏整合和火神经元和一种形式称为第一峰值编码的时间编码。通过这种编码方案，每次刺激最多一次都是神经元火灾，但射击令携带信息。这里，我们引入BS4NN，S4NN的修改，其中突触权重被约束为二进制（+1或-1），以便减少存储器（理想情况下，每个突触的一个比特）和计算占地面积。这是使用两组权重完成：首先，通过梯度下降更新的实际重量，并在BackProjagation的后退通行证中使用，其次是在前向传递中使用的迹象。类似的策略已被用于培训（非尖峰）二值化神经网络。主要区别在于BS4NN在时域中操作：尖峰依次繁殖，并且不同的神经元可以在不同时间达到它们的阈值，这增加了计算能力。我们验证了两个流行的基准，Mnist和Fashion-Mnist上的BS4NN，并获得了这种网络的合理精度（分别为97.0％和87.3％），具有可忽略的准确率，具有可忽略的重量率（0.4％和0.7％，分别）。我们还展示了BS4NN优于具有相同架构的简单BNN，在这两个数据集上（分别为0.2％和0.9％），可能是因为它利用时间尺寸。建议的BS4NN的源代码在HTTPS://github.com/srkh/bs4nn上公开可用。

深度估计是一个重要的计算机视觉任务，特别是用于自主车辆中的导航，或者在机器人中的对象操纵。在这里，我们使用端到端的神经形态方法解决了它，将两个事件的相机和尖峰神经网络（SNN）与略微修改的U-Net的编码器 - 解码器架构结合起来，我们命名为Sterepike。更具体地说，我们使用了多车辆立体声事件相机数据集（MVSEC）。它提供了深度地面真理，用于使用替代梯度下降以监督方式训练立体摩托车。我们提出了一种新颖的读数范式来获得密集的模拟预测 - 从解码器的尖峰中获得每个像素的深度。我们证明，这种体系结构概括得非常好，甚至比其非尖峰对应物更好，导致最先进的测试精度。据我们所知，这是第一次通过完全尖峰网络解决了这样一个大规模的回归问题。最后，我们表明，可以通过规范化获得低发射速率（<10％），精度最低的成本。这意味着可以在神经芯片上有效地实现Sterepositike，用于为低功率和实时嵌入式系统开门。

我们提出了Memprop，即采用基于梯度的学习来培训完全的申请尖峰神经网络（MSNNS）。我们的方法利用固有的设备动力学来触发自然产生的电压尖峰。这些由回忆动力学发出的尖峰本质上是类似物，因此完全可区分，这消除了尖峰神经网络（SNN）文献中普遍存在的替代梯度方法的需求。回忆性神经网络通常将备忘录集成为映射离线培训网络的突触，或者以其他方式依靠关联学习机制来训练候选神经元的网络。相反，我们直接在循环神经元和突触的模拟香料模型上应用了通过时间（BPTT）训练算法的反向传播。我们的实现是完全的综合性，因为突触重量和尖峰神经元都集成在电阻RAM（RRAM）阵列上，而无需其他电路来实现尖峰动态，例如模数转换器（ADCS）或阈值比较器。结果，高阶电物理效应被充分利用，以在运行时使用磁性神经元的状态驱动动力学。通过朝着非同一梯度的学习迈进，我们在以前报道的几个基准上的轻巧密集的完全MSNN中获得了高度竞争的准确性。

Emergence of deep neural networks (DNNs) has raised enormous attention towards artificial neural networks (ANNs) once again. They have become the state-of-the-art models and have won different machine learning challenges. Although these networks are inspired by the brain, they lack biological plausibility, and they have structural differences compared to the brain. Spiking neural networks (SNNs) have been around for a long time, and they have been investigated to understand the dynamics of the brain. However, their application in real-world and complicated machine learning tasks were limited. Recently, they have shown great potential in solving such tasks. Due to their energy efficiency and temporal dynamics there are many promises in their future development. In this work, we reviewed the structures and performances of SNNs on image classification tasks. The comparisons illustrate that these networks show great capabilities for more complicated problems. Furthermore, the simple learning rules developed for SNNs, such as STDP and R-STDP, can be a potential alternative to replace the backpropagation algorithm used in DNNs.

由于它们的低能量消耗，对神经形态计算设备上的尖刺神经网络（SNNS）越来越兴趣。最近的进展使培训SNNS在精度方面开始与传统人工神经网络（ANNS）进行竞争，同时在神经胸壁上运行时的节能。然而，培训SNNS的过程仍然基于最初为ANNS开发的密集的张量操作，这不利用SNN的时空稀疏性质。我们在这里介绍第一稀疏SNN BackPropagation算法，该算法与最新的现有技术实现相同或更好的准确性，同时显着更快，更高的记忆力。我们展示了我们对不同复杂性（时尚 - MNIST，神经影像学 - MNIST和Spiking Heidelberg数字的真实数据集的有效性，在不失精度的情况下实现了高达150倍的后向通行证的加速，而不会减少精度。

Event-based simulations of Spiking Neural Networks (SNNs) are fast and accurate. However, they are rarely used in the context of event-based gradient descent because their implementations on GPUs are difficult. Discretization with the forward Euler method is instead often used with gradient descent techniques but has the disadvantage of being computationally expensive. Moreover, the lack of precision of discretized simulations can create mismatches between the simulated models and analog neuromorphic hardware. In this work, we propose a new exact error-backpropagation through spikes method for SNNs, extending Fast \& Deep to multiple spikes per neuron. We show that our method can be efficiently implemented on GPUs in a fully event-based manner, making it fast to compute and precise enough for analog neuromorphic hardware. Compared to the original Fast \& Deep and the current state-of-the-art event-based gradient-descent algorithms, we demonstrate increased performance on several benchmark datasets with both feedforward and convolutional SNNs. In particular, we show that multi-spike SNNs can have advantages over single-spike networks in terms of convergence, sparsity, classification latency and sensitivity to the dead neuron problem.

The term ``neuromorphic'' refers to systems that are closely resembling the architecture and/or the dynamics of biological neural networks. Typical examples are novel computer chips designed to mimic the architecture of a biological brain, or sensors that get inspiration from, e.g., the visual or olfactory systems in insects and mammals to acquire information about the environment. This approach is not without ambition as it promises to enable engineered devices able to reproduce the level of performance observed in biological organisms -- the main immediate advantage being the efficient use of scarce resources, which translates into low power requirements. The emphasis on low power and energy efficiency of neuromorphic devices is a perfect match for space applications. Spacecraft -- especially miniaturized ones -- have strict energy constraints as they need to operate in an environment which is scarce with resources and extremely hostile. In this work we present an overview of early attempts made to study a neuromorphic approach in a space context at the European Space Agency's (ESA) Advanced Concepts Team (ACT).

Tactile sensing is essential for a variety of daily tasks. And recent advances in event-driven tactile sensors and Spiking Neural Networks (SNNs) spur the research in related fields. However, SNN-enabled event-driven tactile learning is still in its infancy due to the limited representation abilities of existing spiking neurons and high spatio-temporal complexity in the event-driven tactile data. In this paper, to improve the representation capability of existing spiking neurons, we propose a novel neuron model called "location spiking neuron", which enables us to extract features of event-based data in a novel way. Specifically, based on the classical Time Spike Response Model (TSRM), we develop the Location Spike Response Model (LSRM). In addition, based on the most commonly-used Time Leaky Integrate-and-Fire (TLIF) model, we develop the Location Leaky Integrate-and-Fire (LLIF) model. Moreover, to demonstrate the representation effectiveness of our proposed neurons and capture the complex spatio-temporal dependencies in the event-driven tactile data, we exploit the location spiking neurons to propose two hybrid models for event-driven tactile learning. Specifically, the first hybrid model combines a fully-connected SNN with TSRM neurons and a fully-connected SNN with LSRM neurons. And the second hybrid model fuses the spatial spiking graph neural network with TLIF neurons and the temporal spiking graph neural network with LLIF neurons. Extensive experiments demonstrate the significant improvements of our models over the state-of-the-art methods on event-driven tactile learning. Moreover, compared to the counterpart artificial neural networks (ANNs), our SNN models are 10x to 100x energy-efficient, which shows the superior energy efficiency of our models and may bring new opportunities to the spike-based learning community and neuromorphic engineering.

Spiking Neural Networks (SNNs) are bio-plausible models that hold great potential for realizing energy-efficient implementations of sequential tasks on resource-constrained edge devices. However, commercial edge platforms based on standard GPUs are not optimized to deploy SNNs, resulting in high energy and latency. While analog In-Memory Computing (IMC) platforms can serve as energy-efficient inference engines, they are accursed by the immense energy, latency, and area requirements of high-precision ADCs (HP-ADC), overshadowing the benefits of in-memory computations. We propose a hardware/software co-design methodology to deploy SNNs into an ADC-Less IMC architecture using sense-amplifiers as 1-bit ADCs replacing conventional HP-ADCs and alleviating the above issues. Our proposed framework incurs minimal accuracy degradation by performing hardware-aware training and is able to scale beyond simple image classification tasks to more complex sequential regression tasks. Experiments on complex tasks of optical flow estimation and gesture recognition show that progressively increasing the hardware awareness during SNN training allows the model to adapt and learn the errors due to the non-idealities associated with ADC-Less IMC. Also, the proposed ADC-Less IMC offers significant energy and latency improvements, $2-7\times$ and $8.9-24.6\times$, respectively, depending on the SNN model and the workload, compared to HP-ADC IMC.

由于它们的时间加工能力及其低交换（尺寸，重量和功率）以及神经形态硬件中的节能实现，尖峰神经网络（SNNS）已成为传统人工神经网络（ANN）的有趣替代方案。然而，培训SNNS所涉及的挑战在准确性方面有限制了它们的表现，从而限制了他们的应用。因此，改善更准确的特征提取的学习算法和神经架构是SNN研究中的当前优先级之一。在本文中，我们展示了现代尖峰架构的关键组成部分的研究。我们在从最佳执行网络中凭经验比较了图像分类数据集中的不同技术。我们设计了成功的残余网络（Reset）架构的尖峰版本，并测试了不同的组件和培训策略。我们的结果提供了SNN设计的最新版本，它允许在尝试构建最佳视觉特征提取器时进行明智的选择。最后，我们的网络优于CIFAR-10（94.1％）和CIFAR-100（74.5％）数据集的先前SNN架构，并将现有技术与DVS-CIFAR10（71.3％）相匹配，参数较少而不是先前的状态艺术，无需安静转换。代码在https://github.com/vicenteax/spiking_resnet上获得。

We propose a novel backpropagation algorithm for training spiking neural networks (SNNs) that encodes information in the relative multiple spike timing of individual neurons without single-spike restrictions. The proposed algorithm inherits the advantages of conventional timing-based methods in that it computes accurate gradients with respect to spike timing, which promotes ideal temporal coding. Unlike conventional methods where each neuron fires at most once, the proposed algorithm allows each neuron to fire multiple times. This extension naturally improves the computational capacity of SNNs. Our SNN model outperformed comparable SNN models and achieved as high accuracy as non-convolutional artificial neural networks. The spike count property of our networks was altered depending on the time constant of the postsynaptic current and the membrane potential. Moreover, we found that there existed the optimal time constant with the maximum test accuracy. That was not seen in conventional SNNs with single-spike restrictions on time-to-fast-spike (TTFS) coding. This result demonstrates the computational properties of SNNs that biologically encode information into the multi-spike timing of individual neurons. Our code would be publicly available.

已知尖峰神经网络（SNN）对于神经形态处理器实施非常有效，可以在传统深度学习方法上提高能效和计算潜伏期的数量级。最近，随着监督培训算法对SNN的背景，最近也使可比的算法性能成为可能。但是，包括音频，视频和其他传感器衍生数据在内的信息通常被编码为不适合SNN的实用值信号，从而阻止网络利用SPIKE定时信息。因此，从实价信号到尖峰的有效编码是至关重要的，并且会显着影响整个系统的性能。为了有效地将信号编码为尖峰，必须考虑与手头任务相关的信息以及编码尖峰的密度。在本文中，我们在扬声器独立数字分类系统的背景下研究了四种尖峰编码方法：发送三角洲，第一次尖峰的时间，漏水的集成和火神经元和弯曲尖刺算法。我们首先表明，与传统的短期傅立叶变换相比，在编码生物启发的耳蜗时，使用较少的尖峰会产生更高的分类精度。然后，我们证明了两种对三角洲变体的发送导致分类结果可与最先进的深卷积神经网络基线相媲美，同时降低了编码的比特率。最后，我们表明，几种编码方法在某些情况下导致比传统深度学习基线的性能提高，进一步证明了编码实用值信号中编码算法的尖峰力量艺术技术。

由于稀疏，异步和二进制事件（或尖峰）驱动加工，尖峰神经网络（SNNS）最近成为深度学习的替代方案，可以在神经形状硬件上产生巨大的能效益。然而，从划痕训练高精度和低潜伏期的SNN，患有尖刺神经元的非微弱性质。要在SNNS中解决此培训问题，我们重新批准批量标准化，并通过时间（BNTT）技术提出时间批量标准化。大多数先前的SNN工程到现在忽略了批量标准化，认为它无效地训练时间SNN。与以前的作品不同，我们提出的BNTT沿着时轴沿着时间轴解耦的参数，以捕获尖峰的时间动态。在BNTT中的时间上不断发展的可学习参数允许神经元通过不同的时间步长来控制其尖峰率，从头开始实现低延迟和低能量训练。我们对CiFar-10，CiFar-100，微小想象特和事件驱动的DVS-CIFAR10数据集进行实验。 BNTT允许我们首次在三个复杂的数据集中培训深度SNN架构，只需25-30步即可。我们还使用BNTT中的参数分布提前退出算法，以降低推断的延迟，进一步提高了能量效率。