Brain-inspired computing proposes a set of algorithmic principles that hold promise for advancing artificial intelligence. They endow systems with self learning capabilities, efficient energy usage, and high storage capacity. A core concept that lies at the heart of brain computation is sequence learning and prediction. This form of computation is essential for almost all our daily tasks such as movement generation, perception, and language. Understanding how the brain performs such a computation is not only important to advance neuroscience but also to pave the way to new technological brain-inspired applications. A previously developed spiking neural network implementation of sequence prediction and recall learns complex, high-order sequences in an unsupervised manner by local, biologically inspired plasticity rules. An emerging type of hardware that holds promise for efficiently running this type of algorithm is neuromorphic hardware. It emulates the way the brain processes information and maps neurons and synapses directly into a physical substrate. Memristive devices have been identified as potential synaptic elements in neuromorphic hardware. In particular, redox-induced resistive random access memories (ReRAM) devices stand out at many aspects. They permit scalability, are energy efficient and fast, and can implement biological plasticity rules. In this work, we study the feasibility of using ReRAM devices as a replacement of the biological synapses in the sequence learning model. We implement and simulate the model including the ReRAM plasticity using the neural simulator NEST. We investigate the effect of different device properties on the performance characteristics of the sequence learning model, and demonstrate resilience with respect to different on-off ratios, conductance resolutions, device variability, and synaptic failure.
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Understanding how biological neural networks carry out learning using spike-based local plasticity mechanisms can lead to the development of powerful, energy-efficient, and adaptive neuromorphic processing systems. A large number of spike-based learning models have recently been proposed following different approaches. However, it is difficult to assess if and how they could be mapped onto neuromorphic hardware, and to compare their features and ease of implementation. To this end, in this survey, we provide a comprehensive overview of representative brain-inspired synaptic plasticity models and mixed-signal CMOS neuromorphic circuits within a unified framework. We review historical, bottom-up, and top-down approaches to modeling synaptic plasticity, and we identify computational primitives that can support low-latency and low-power hardware implementations of spike-based learning rules. We provide a common definition of a locality principle based on pre- and post-synaptic neuron information, which we propose as a fundamental requirement for physical implementations of synaptic plasticity. Based on this principle, we compare the properties of these models within the same framework, and describe the mixed-signal electronic circuits that implement their computing primitives, pointing out how these building blocks enable efficient on-chip and online learning in neuromorphic processing systems.
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这项研究提出了依赖电压突触可塑性(VDSP),这是一种新型的脑启发的无监督的本地学习规则,用于在线实施HEBB对神经形态硬件的可塑性机制。拟议的VDSP学习规则仅更新了突触后神经元的尖峰的突触电导,这使得相对于标准峰值依赖性可塑性(STDP)的更新数量减少了两倍。此更新取决于突触前神经元的膜电位,该神经元很容易作为神经元实现的一部分,因此不需要额外的存储器来存储。此外,该更新还对突触重量进行了正规化,并防止重复刺激时的重量爆炸或消失。进行严格的数学分析以在VDSP和STDP之间达到等效性。为了验证VDSP的系统级性能,我们训练一个单层尖峰神经网络(SNN),以识别手写数字。我们报告85.01 $ \ pm $ 0.76%(平均$ \ pm $ s.d。)对于MNIST数据集中的100个输出神经元网络的精度。在缩放网络大小时,性能会提高(400个输出神经元的89.93 $ \ pm $ 0.41%,500个神经元为90.56 $ \ pm $ 0.27),这验证了大规模计算机视觉任务的拟议学习规则的适用性。有趣的是,学习规则比STDP更好地适应输入信号的频率,并且不需要对超参数进行手动调整。
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This chapter sheds light on the synaptic organization of the brain from the perspective of computational neuroscience. It provides an introductory overview on how to account for empirical data in mathematical models, implement them in software, and perform simulations reflecting experiments. This path is demonstrated with respect to four key aspects of synaptic signaling: the connectivity of brain networks, synaptic transmission, synaptic plasticity, and the heterogeneity across synapses. Each step and aspect of the modeling and simulation workflow comes with its own challenges and pitfalls, which are highlighted and addressed in detail.
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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。
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人工智能革命(AI)提出了巨大的存储和数据处理要求。大量的功耗和硬件开销已成为构建下一代AI硬件的主要挑战。为了减轻这种情况,神经形态计算引起了极大的关注,因为它在功耗非常低的功能方面具有出色的数据处理能力。尽管无情的研究已经进行了多年,以最大程度地减少神经形态硬件的功耗,但我们离达到人脑的能源效率还有很长的路要走。此外,设计复杂性和过程变化阻碍了当前神经形态平台的大规模实现。最近,由于其出色的速度和功率指标,在低温温度中实施神经形态计算系统的概念引起了人们的兴趣。可以设计几种低温装置,可作为具有超低功率需求的神经形态原始设备。在这里,我们全面回顾了低温神经形态硬件。我们将现有的低温神经形态硬件分类为几个分层类别,并根据关键性能指标绘制比较分析。我们的分析简洁地描述了相关电路拓扑的操作,并概述了最先进的技术平台遇到的优势和挑战。最后,我们提供了见解,以规避这些挑战,以实现未来的研究发展。
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过去十年来,人们对人工智能(AI)的兴趣激增几乎完全由人工神经网络(ANN)的进步驱动。尽管ANN为许多以前棘手的问题设定了最先进的绩效,但它们需要大量的数据和计算资源进行培训,并且由于他们采用了监督的学习,他们通常需要知道每个培训示例的正确标记的响应,并限制它们对现实世界域的可扩展性。尖峰神经网络(SNN)是使用更多类似脑部神经元的ANN的替代方法,可以使用无监督的学习来发现输入数据中的可识别功能,而又不知道正确的响应。但是,SNN在动态稳定性方面挣扎,无法匹配ANN的准确性。在这里,我们展示了SNN如何克服文献中发现的许多缺点,包括为消失的尖峰问题提供原则性解决方案,以优于所有现有的浅SNN,并等于ANN的性能。它在使用无标记的数据和仅1/50的训练时期使用无监督的学习时完成了这一点(标记数据仅用于最终的简单线性读数层)。该结果使SNN成为可行的新方法,用于使用未标记的数据集快速,准确,有效,可解释的机器学习。
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In the brain, information is encoded, transmitted and used to inform behaviour at the level of timing of action potentials distributed over population of neurons. To implement neural-like systems in silico, to emulate neural function, and to interface successfully with the brain, neuromorphic circuits need to encode information in a way compatible to that used by populations of neuron in the brain. To facilitate the cross-talk between neuromorphic engineering and neuroscience, in this Review we first critically examine and summarize emerging recent findings about how population of neurons encode and transmit information. We examine the effects on encoding and readout of information for different features of neural population activity, namely the sparseness of neural representations, the heterogeneity of neural properties, the correlations among neurons, and the time scales (from short to long) at which neurons encode information and maintain it consistently over time. Finally, we critically elaborate on how these facts constrain the design of information coding in neuromorphic circuits. We focus primarily on the implications for designing neuromorphic circuits that communicate with the brain, as in this case it is essential that artificial and biological neurons use compatible neural codes. However, we also discuss implications for the design of neuromorphic systems for implementation or emulation of neural computation.
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我们训练神经形态硬件芯片以通过变分能最小化近似Quantum旋转模型的地面状态。与使用马尔可夫链蒙特卡罗进行样品生成的变分人工神经网络相比,这种方法具有优点:神经形态器件以快速和固有的并行方式产生样品。我们开发培训算法,并将其应用于横向场介绍模型,在中等系统尺寸下显示出良好的性能($ n \ LEQ 10 $)。系统的普遍开心研究表明,较大系统尺寸的可扩展性主要取决于样品质量,该样品质量受到模拟神经芯片上的参数漂移的限制。学习性能显示阈值行为作为ansatz的变分参数的数量的函数,大约为50美元的隐藏神经元,足以表示关键地位,最高$ n = 10 $。网络参数的6 + 1位分辨率不会限制当前设置中的可达近似质量。我们的工作为利用神经形态硬件的能力提供了一种重要的一步,以解决量子数量问题中的维数诅咒。
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尖峰神经网络(SNN)提供了一个新的计算范式,能够高度平行,实时处理。光子设备是设计与SNN计算范式相匹配的高带宽,平行体系结构的理想选择。 CMO和光子元件的协整允许将低损耗的光子设备与模拟电子设备结合使用,以更大的非线性计算元件的灵活性。因此,我们在整体硅光子学(SIPH)过程上设计和模拟了光电尖峰神经元电路,该过程复制了超出泄漏的集成和火(LIF)之外有用的尖峰行为。此外,我们探索了两种学习算法,具有使用Mach-Zehnder干涉法(MZI)网格作为突触互连的片上学习的潜力。实验证明了随机反向传播(RPB)的变体,并在简单分类任务上与标准线性回归的性能相匹配。同时,将对比性HEBBIAN学习(CHL)规则应用于由MZI网格组成的模拟神经网络,以进行随机输入输出映射任务。受CHL训练的MZI网络的性能比随机猜测更好,但不符合理想神经网络的性能(没有MZI网格施加的约束)。通过这些努力,我们证明了协调的CMO和SIPH技术非常适合可扩展的SNN计算体系结构的设计。
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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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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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基于旋转扭矩振荡器的复合值Hopfield网络模拟可以恢复相位编码的图像。存储器增强逆变器的序列提供可调谐延迟元件,通过相位转换振荡器的振荡输出来实现复合权重的可调延迟元件。伪逆培训足以存储在一组192个振荡器中,至少代表16 $ \倍数为12个像素图像。恢复图像所需的能量取决于所需的错误级别。对于这里考虑的振荡器和电路,来自理想图像的5%均方方偏差需要大约5 00美元$ S并消耗大约130 NJ。模拟显示,当振荡器的谐振频率可以调整为具有小于10 ^ {-3} $的分数扩展时,网络功能良好,具体取决于反馈的强度。
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Synaptic plasticity allows cortical circuits to learn new tasks and to adapt to changing environments. How do cortical circuits use plasticity to acquire functions such as decision-making or working memory? Neurons are connected in complex ways, forming recurrent neural networks, and learning modifies the strength of their connections. Moreover, neurons communicate emitting brief discrete electric signals. Here we describe how to train recurrent neural networks in tasks like those used to train animals in neuroscience laboratories, and how computations emerge in the trained networks. Surprisingly, artificial networks and real brains can use similar computational strategies.
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在这项工作中,我们介绍了一种光电尖峰,能够以超速率($ \ \左右100磅/光学尖峰)和低能耗($ <$ PJ /秒码)运行。所提出的系统结合了具有负差分电导的可激发谐振隧道二极管(RTD)元件,耦合到纳米级光源(形成主节点)或光电探测器(形成接收器节点)。我们在数值上学习互连的主接收器RTD节点系统的尖峰动态响应和信息传播功能。使用脉冲阈值和集成的关键功能,我们利用单个节点来对顺序脉冲模式进行分类,并对图像特征(边缘)识别执行卷积功能。我们还展示了光学互连的尖峰神经网络模型,用于处理超过10 Gbps的时空数据,具有高推理精度。最后,我们展示了利用峰值定时依赖性可塑性的片外监督的学习方法,使能RTD的光子尖峰神经网络。这些结果证明了RTD尖峰节点用于低占地面积,低能量,高速光电实现神经形态硬件的潜在和可行性。
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Efficient and robust control using spiking neural networks (SNNs) is still an open problem. Whilst behaviour of biological agents is produced through sparse and irregular spiking patterns, which provide both robust and efficient control, the activity patterns in most artificial spiking neural networks used for control are dense and regular -- resulting in potentially less efficient codes. Additionally, for most existing control solutions network training or optimization is necessary, even for fully identified systems, complicating their implementation in on-chip low-power solutions. The neuroscience theory of Spike Coding Networks (SCNs) offers a fully analytical solution for implementing dynamical systems in recurrent spiking neural networks -- while maintaining irregular, sparse, and robust spiking activity -- but it's not clear how to directly apply it to control problems. Here, we extend SCN theory by incorporating closed-form optimal estimation and control. The resulting networks work as a spiking equivalent of a linear-quadratic-Gaussian controller. We demonstrate robust spiking control of simulated spring-mass-damper and cart-pole systems, in the face of several perturbations, including input- and system-noise, system disturbances, and neural silencing. As our approach does not need learning or optimization, it offers opportunities for deploying fast and efficient task-specific on-chip spiking controllers with biologically realistic activity.
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更具体地说,神经系统能够简单有效地解决复杂的问题,超过现代计算机。在这方面,神经形态工程是一个研究领域,重点是模仿控制大脑的基本原理,以开发实现此类计算能力的系统。在该领域中,生物启发的学习和记忆系统仍然是要解决的挑战,这就是海马涉及的地方。正是大脑的区域充当短期记忆,从而从大脑皮层的所有感觉核中学习,非结构化和快速存储信息及其随后的回忆。在这项工作中,我们提出了一个基于海马的新型生物启发的记忆模型,具有学习记忆的能力,从提示中回顾它们(与其他内容相关的记忆的一部分),甚至在尝试时忘记记忆通过相同的提示学习其他人。该模型已在使用尖峰神经网络上在大型摩托车硬件平台上实现,并进行了一组实验和测试以证明其正确且预期的操作。所提出的基于SPIKE的内存模型仅在接收输入,能提供节能的情况下才能生成SPIKES,并且需要7个时间步,用于学习步骤和6个时间段来召回以前存储的存储器。这项工作介绍了基于生物启发的峰值海马记忆模型的第一个硬件实现,为开发未来更复杂的神经形态系统的发展铺平了道路。
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Organic neuromorphic device networks can accelerate neural network algorithms and directly integrate with microfluidic systems or living tissues. Proposed devices based on the bio-compatible conductive polymer PEDOT:PSS have shown high switching speeds and low energy demand. However, as electrochemical systems, they are prone to self-discharge through parasitic electrochemical reactions. Therefore, the network's synapses forget their trained conductance states over time. This work integrates single-device high-resolution charge transport models to simulate neuromorphic device networks and analyze the impact of self-discharge on network performance. Simulation of a single-layer nine-pixel image classification network reveals no significant impact of self-discharge on training efficiency. And, even though the network's weights drift significantly during self-discharge, its predictions remain 100\% accurate for over ten hours. On the other hand, a multi-layer network for the approximation of the circle function is shown to degrade significantly over twenty minutes with a final mean-squared-error loss of 0.4. We propose to counter the effect by periodically reminding the network based on a map between a synapse's current state, the time since the last reminder, and the weight drift. We show that this method with a map obtained through validated simulations can reduce the effective loss to below 0.1 even with worst-case assumptions. Finally, while the training of this network is affected by self-discharge, a good classification is still obtained. Electrochemical organic neuromorphic devices have not been integrated into larger device networks. This work predicts their behavior under nonideal conditions, mitigates the worst-case effects of parasitic self-discharge, and opens the path toward implementing fast and efficient neural networks on organic neuromorphic hardware.
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Data-driven modeling approaches such as jump tables are promising techniques to model populations of resistive random-access memory (ReRAM) or other emerging memory devices for hardware neural network simulations. As these tables rely on data interpolation, this work explores the open questions about their fidelity in relation to the stochastic device behavior they model. We study how various jump table device models impact the attained network performance estimates, a concept we define as modeling bias. Two methods of jump table device modeling, binning and Optuna-optimized binning, are explored using synthetic data with known distributions for benchmarking purposes, as well as experimental data obtained from TiOx ReRAM devices. Results on a multi-layer perceptron trained on MNIST show that device models based on binning can behave unpredictably particularly at low number of points in the device dataset, sometimes over-promising, sometimes under-promising target network accuracy. This paper also proposes device level metrics that indicate similar trends with the modeling bias metric at the network level. The proposed approach opens the possibility for future investigations into statistical device models with better performance, as well as experimentally verified modeling bias in different in-memory computing and neural network architectures.
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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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