神经形态数据携带由尖峰编码的时空模式的信息。因此,神经形态计算中的核心问题是训练尖峰神经网络(SNNS)以再现时加速时空尖峰图案响应于给定的尖刺刺激。通过将每个输入分配给特定期望的输出尖刺序列,大多数现有方法通过分配每个输入来模拟SNN的输入输出行为。相比之下,为了充分利用尖峰的时间编码能力,这项工作建议训练SNN,以匹配尖刺信号的分布而不是单独的尖峰信号。为此,本文介绍了一种新颖的混合架构,包括通过SNN实现的条件发生器,以及由传统人工神经网络(ANN)实现的鉴别器。 ANN的作用是在遵循生成的对抗网络(GANS)原则的对抗迭代学习策略中对SNN的培训期间提供反馈。为了更好地捕获多模态的时空分布,所提出的方法被称为Spikegan - 进一步扩展到支持发电机重量的贝叶斯学习。最后,通过提出Spikegan的在线元学习变量来解决具有时变统计数据的设置。实验与基于(静态)信念网络的现有解决方案相比,对所提出的方法的优点带来了洞察的洞察力,以及最大可能性(或经验风险最小化)。
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生物智能的主要特征之一是能源效率,持续适应能力以及通过不确定性量化的风险管理。到目前为止,神经形态工程主要是由实施节能机器从生物学大脑的基于时间的计算范式中获得灵感的目标的驱动。在本文中,我们采取了朝着设计神经形态系统设计的步骤,这些系统能够适应改变学习任务,同时产生良好的不确定性量化估计。为此,我们得出了在贝叶斯持续学习框架内尖峰神经网络(SNN)的在线学习规则。在其中,每个突触重量都由参数表示,这些参数量化了先验知识和观察到的数据引起的当前认知不确定性。提出的在线规则在观察到数据时以流方式更新分布参数。我们实例化了实用值和二元突触权重的建议方法。使用英特尔熔岩平台的实验结果表明,贝叶斯在适应能力和不确定性定量方面的经常学习优点。
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为了在专门的神经形态硬件中进行节能计算,我们提出了尖峰神经编码,这是基于预测性编码理论的人工神经模型家族的实例化。该模型是同类模型,它是通过在“猜测和检查”的永无止境过程中运行的,神经元可以预测彼此的活动值,然后调整自己的活动以做出更好的未来预测。我们系统的互动性,迭代性质非常适合感官流预测的连续时间表述,并且如我们所示,模型的结构产生了局部突触更新规则,可以用来补充或作为在线峰值定位的替代方案依赖的可塑性。在本文中,我们对模型的实例化进行了实例化,该模型包括泄漏的集成和火灾单元。但是,我们系统所在的框架自然可以结合更复杂的神经元,例如Hodgkin-Huxley模型。我们在模式识别方面的实验结果证明了当二进制尖峰列车是通信间通信的主要范式时,模型的潜力。值得注意的是,尖峰神经编码在分类绩效方面具有竞争力,并且在从任务序列中学习时会降低遗忘,从而提供了更经济的,具有生物学上的替代品,可用于流行的人工神经网络。
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神经形态计算是一种新兴的计算范式,它从批处理的处理转向在线,事件驱动的流数据处理。当神经形态芯片与基于尖峰的传感器结合在一起时,只有在峰值时间内记录相关事件并证明对变化条件的低延迟响应时,才能通过消耗能量来固有地适应数据分布的“语义”。环境。本文为神经形态无线网络系统系统提出了端到端设计,该系统集成了基于尖峰的传感,处理和通信。在拟议的神经系统系统中,每个传感设备都配备了神经形态传感器,尖峰神经网络(SNN)和带有多个天线的脉冲无线电发射器。传输发生在配备了多Antenna脉冲无线电接收器和SNN的接收器上的共享褪色通道上进行。为了使接收器适应褪色的通道条件,我们引入了一项超网络,以使用飞行员控制解码SNN的权重。飞行员,编码SNN,解码SNN和超网络经过多个通道实现的共同训练。该系统被证明可以显着改善基于传统的基于框架的数字解决方案以及替代性非自适应训练方法,从时间到准确性和能源消耗指标方面。
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We propose that in order to harness our understanding of neuroscience toward machine learning, we must first have powerful tools for training brain-like models of learning. Although substantial progress has been made toward understanding the dynamics of learning in the brain, neuroscience-derived models of learning have yet to demonstrate the same performance capabilities as methods in deep learning such as gradient descent. Inspired by the successes of machine learning using gradient descent, we demonstrate that models of neuromodulated synaptic plasticity from neuroscience can be trained in Spiking Neural Networks (SNNs) with a framework of learning to learn through gradient descent to address challenging online learning problems. This framework opens a new path toward developing neuroscience inspired online learning algorithms.
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这是一门专门针对STEM学生开发的介绍性机器学习课程。我们的目标是为有兴趣的读者提供基础知识,以在自己的项目中使用机器学习,并将自己熟悉术语作为进一步阅读相关文献的基础。在这些讲义中,我们讨论受监督,无监督和强化学习。注释从没有神经网络的机器学习方法的说明开始,例如原理分析,T-SNE,聚类以及线性回归和线性分类器。我们继续介绍基本和先进的神经网络结构,例如密集的进料和常规神经网络,经常性的神经网络,受限的玻尔兹曼机器,(变性)自动编码器,生成的对抗性网络。讨论了潜在空间表示的解释性问题,并使用梦和对抗性攻击的例子。最后一部分致力于加强学习,我们在其中介绍了价值功能和政策学习的基本概念。
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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).
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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.
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我们如何为神经系统带来隐私和能效?在本文中,我们提出了PrivateNN,旨在从预先训练的ANN模型构建低功耗尖峰神经网络(SNNS),而不会泄漏包含在数据集中的敏感信息。在这里,我们解决两种类型的泄漏问题:1)当网络在Ann-SNN转换过程中访问真实训练数据时,会导致数据泄漏。 2)当类相关的特征可以从网络参数重建时,会导致类泄漏。为了解决数据泄漏问题,我们从预先培训的ANN生成合成图像,并使用所生成的图像将ANN转换为SNNS。然而,转换的SNNS仍然容易受到类泄漏的影响,因为权重参数相对于ANN参数具有相同的(或缩放)值。因此,通过训练SNNS,通过训练基于时间尖峰的学习规则来加密SNN权重。使用时间数据更新权重参数使得SNN难以在空间域中解释。我们观察到,加密的私人没有消除数据和类泄漏问题,略微的性能下降(小于〜2),与标准ANN相比,与标准ANN相比的显着的能效增益(约55倍)。我们对各种数据集进行广泛的实验,包括CiFar10,CiFar100和Tinyimagenet,突出了隐私保留的SNN培训的重要性。
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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.
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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。
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Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image synthesis, semantic image editing, style transfer, image super-resolution and classification. The aim of this review paper is to provide an overview of GANs for the signal processing community, drawing on familiar analogies and concepts where possible. In addition to identifying different methods for training and constructing GANs, we also point to remaining challenges in their theory and application.
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基于事件的视觉传感器在事件流中编码本地像素方面的亮度变化,而不是图像帧,并且除了低延迟,高动态范围和缺乏运动模糊之外,还产生稀疏,节能编码。基于事件的传感器的对象识别的最新进展来自深度神经网络的转换,培训背部经历。但是,使用这些事件流的方法需要转换到同步范式,这不仅失去了计算效率,而且还会错过提取时空特征的机会。在本文中,我们提出了一种用于基于事件的模式识别和对象检测的深度神经网络的端到端培训的混合架构,将尖刺神经网络(SNN)骨干组合用于高效的基于事件的特征提取,以及随后的模拟神经网络(ANN)头解决同步分类和检测任务。这是通过将标准的梯度训练与替代梯度训练相结合来实现这一点来实现,以通过SNN传播梯度。可以在不转换的情况下培训混合SNN-ANN,并且导致高度准确的网络,这些网络比其ANN对应物大得多。我们演示了基于事件的分类和对象检测数据集的结果,其中只需要将ANN头的体系结构适应任务,并且不需要基于事件的输入的转换。由于ANNS和SNNS需要不同的硬件范式来最大限度地提高其效率,因此设想SNN骨干网和ANN头可以在不同的处理单元上执行,从而分析在两部分之间进行通信的必要带宽。混合网络是有前途的架构,以进一步推进基于事件的愿景的机器学习方法,而不必妥协效率。
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HEBBIAN在获奖者全方位(WTA)网络中的可塑性对于神经形态的片上学习非常有吸引力,这是由于其高效,本地,无监督和在线性质。此外,它的生物学合理性可能有助于克服人工算法的重要局限性,例如它们对对抗攻击和长期训练时间的敏感性。但是,Hebbian WTA学习在机器学习(ML)中很少使用,这可能是因为它缺少与深度学习兼容的优化理论(DL)。在这里,我们严格地表明,由标准DL元素构建的WTA网络与我们得出的Hebbian样可塑性结合在一起,维持数据的贝叶斯生成模型。重要的是,在没有任何监督的情况下,我们的算法,SOFTHEBB,可以最大程度地减少跨渗透性,即监督DL中的共同损失函数。我们在理论上和实践中展示了这一点。关键是“软” WTA,那里没有绝对的“硬”赢家神经元。令人惊讶的是,在浅网络比较与背面的比较(BP)中,SOFTHEBB表现出超出其HEBBIAN效率的优势。也就是说,它的收敛速度更快,并且对噪声和对抗性攻击更加强大。值得注意的是,最大程度地混淆SoftheBB的攻击也使人眼睛混淆,可能将人类感知的鲁棒性与Hebbian WTA Cortects联系在一起。最后,SOFTHEBB可以将合成对象作为真实对象类的插值生成。总而言之,Hebbian效率,理论的基础,跨透明拷贝最小化以及令人惊讶的经验优势,表明SOFTHEBB可能会激发高度神经态和彻底不同,但实用且有利的学习算法和硬件加速器。
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尖峰神经网络(SNN)是大脑中低功率,耐断层的信息处理的基础,并且在适当的神经形态硬件加速器上实施时,可能构成传统深层神经网络的能力替代品。但是,实例化解决复杂的计算任务的SNN在Silico中仍然是一个重大挑战。替代梯度(SG)技术已成为培训SNN端到端的标准解决方案。尽管如此,它们的成功取决于突触重量初始化,类似于常规的人工神经网络(ANN)。然而,与ANN不同,它仍然难以捉摸地构成SNN的良好初始状态。在这里,我们为受到大脑中通常观察到的波动驱动的策略启发的SNN制定了一般初始化策略。具体而言,我们为数据依赖性权重初始化提供了实用的解决方案,以确保广泛使用的泄漏的集成和传火(LIF)神经元的波动驱动。我们从经验上表明,经过SGS培训时,SNN遵循我们的策略表现出卓越的学习表现。这些发现概括了几个数据集和SNN体系结构,包括完全连接,深度卷积,经常性和更具生物学上合理的SNN遵守Dale的定律。因此,波动驱动的初始化提供了一种实用,多功能且易于实现的策略,可改善神经形态工程和计算神经科学的不同任务的SNN培训绩效。
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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。
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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.
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Two of the main principles underlying the life cycle of an artificial intelligence (AI) module in communication networks are adaptation and monitoring. Adaptation refers to the need to adjust the operation of an AI module depending on the current conditions; while monitoring requires measures of the reliability of an AI module's decisions. Classical frequentist learning methods for the design of AI modules fall short on both counts of adaptation and monitoring, catering to one-off training and providing overconfident decisions. This paper proposes a solution to address both challenges by integrating meta-learning with Bayesian learning. As a specific use case, the problems of demodulation and equalization over a fading channel based on the availability of few pilots are studied. Meta-learning processes pilot information from multiple frames in order to extract useful shared properties of effective demodulators across frames. The resulting trained demodulators are demonstrated, via experiments, to offer better calibrated soft decisions, at the computational cost of running an ensemble of networks at run time. The capacity to quantify uncertainty in the model parameter space is further leveraged by extending Bayesian meta-learning to an active setting. In it, the designer can select in a sequential fashion channel conditions under which to generate data for meta-learning from a channel simulator. Bayesian active meta-learning is seen in experiments to significantly reduce the number of frames required to obtain efficient adaptation procedure for new frames.
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神经生成模型可用于学习从数据的复杂概率分布,从它们中进行采样,并产生概率密度估计。我们提出了一种用于开发由大脑预测处理理论启发的神经生成模型的计算框架。根据预测加工理论,大脑中的神经元形成一个层次结构,其中一个级别的神经元形成关于来自另一个层次的感觉输入的期望。这些神经元根据其期望与观察到的信号之间的差异更新其本地模型。以类似的方式,我们的生成模型中的人造神经元预测了邻近的神经元的作用,并根据预测匹配现实的程度来调整它们的参数。在这项工作中,我们表明,在我们的框架内学到的神经生成模型在练习中跨越多个基准数据集和度量来表现良好,并且保持竞争或显着优于具有类似功能的其他生成模型(例如变形自动编码器)。
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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。
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