有充分的神经生物学证据表明，上下文敏感的新皮质神经元使用其顶端输入来放大相干进料（FF）输入的传播。但是，到目前为止，尚未证明这种已知机制如何提供有用的神经计算。在这里，我们首次展示了这种神经信息处理的处理和学习能力与哺乳动物新皮层的能力相匹配。具体而言，我们表明，由此类本地处理器组成的网络将冲突的信息传输到更高级别，并大大减少处理大量异质现实世界数据所需的活动量，例如在处理视听语音时，这些本地处理器使用这些本地处理器时看到唇部动作可有选择地放大这些动作产生的听觉信息的FF传输，反之亦然。由于这种机制比最佳可用的深神经网的最佳形式更有效率，因此它为理解大脑的神秘能量节能机制提供了逐步改变，并激发了设计增强形式的生物学上的机器学习算法的进步。

Context-sensitive two-point layer 5 pyramidal cells (L5PCs) were discovered as long ago as 1999. However, the potential of this discovery to provide useful neural computation has yet to be demonstrated. Here we show for the first time how a transformative L5PCs-driven deep neural network (DNN), termed the multisensory cooperative computing (MCC) architecture, can effectively process large amounts of heterogeneous real-world audio-visual (AV) data, using far less energy compared to best available 'point' neuron-driven DNNs. A novel highly-distributed parallel implementation on a Xilinx UltraScale+ MPSoC device estimates energy savings up to 245759 $ \times $ 50000 $\mu$J (i.e., 62% less than the baseline model in a semi-supervised learning setup) where a single synapse consumes $8e^{-5}\mu$J. In a supervised learning setup, the energy-saving can potentially reach up to 1250x less (per feedforward transmission) than the baseline model. The significantly reduced neural activity in MCC leads to inherently fast learning and resilience against sudden neural damage. This remarkable performance in pilot experiments demonstrates the embodied neuromorphic intelligence of our proposed cooperative L5PC that receives input from diverse neighbouring neurons as context to amplify the transmission of most salient and relevant information for onward transmission, from overwhelmingly large multimodal information utilised at the early stages of on-chip training. Our proposed approach opens new cross-disciplinary avenues for future on-chip DNN training implementations and posits a radical shift in current neuromorphic computing paradigms.

预测性编码提供了对皮质功能的潜在统一说明 - 假设大脑的核心功能是最小化有关世界生成模型的预测错误。该理论与贝叶斯大脑框架密切相关，在过去的二十年中，在理论和认知神经科学领域都产生了重大影响。基于经验测试的预测编码的改进和扩展的理论和数学模型，以及评估其在大脑中实施的潜在生物学合理性以及该理论所做的具体神经生理学和心理学预测。尽管存在这种持久的知名度，但仍未对预测编码理论，尤其是该领域的最新发展进行全面回顾。在这里，我们提供了核心数学结构和预测编码的逻辑的全面综述，从而补充了文献中最新的教程。我们还回顾了该框架中的各种经典和最新工作，从可以实施预测性编码的神经生物学现实的微电路到预测性编码和广泛使用的错误算法的重新传播之间的紧密关系，以及对近距离的调查。预测性编码和现代机器学习技术之间的关系。

本文提出了一种新型的多式模式自学架构，用于节能音频 - 视听（AV）语音增强，将图形神经网络与规范相关性分析（CCA-GNN）集成在一起。所提出的方法将其基础放在最先进的CCA-GNN上，该方法通过最大化相同输入的增强视图对之间的相关性来学习代表性的嵌入，同时脱离了断开连接的特征。常规CCA-GNN的关键思想涉及丢弃增强变化的信息并保留增强不变的信息，同时阻止捕获冗余信息。我们提出的AV CCA-GNN模型涉及多模式表示学习环境。具体而言，我们的模型通过从音频和视觉嵌入的同一信道和规范相关性的增强视图中最大化的规范相关性来改善上下文AV语音处理。此外，它提出了一个位置节点编码，该位置节点在计算节点最近的邻居时考虑了先前的框架序列距离，而不是特征空间表示，并通过邻域的连接在嵌入式中引入时间信息。在基准Chime3数据集上进行的实验表明，我们提出的基于框架的AV CCA-GNN确保在时间上下文中获得更好的特征学习，从而导致比最先进的CCA-GNN更节能的语音重建感知器（MLP）和长期记忆（LSTM）模型。

本文提出了Mburst，这是一种新型的多模式解决方案，用于视听语音增强功能，该解决方案考虑了有关前额叶皮层和其他大脑区域的锥体细胞的最新神经系统发现。所谓的爆发传播实现了几个标准，以更加可行的方式解决信用分配问题：通过反馈来指导可塑性的标志和大小，并线性化反馈信号。 Mburst从这种能力中受益于学习嘈杂信号和视觉刺激之间的相关性，从而通过扩增相关信息和抑制噪声来归因于语音。通过网格语料库和基于Chime3的数据集进行的实验表明，Mburst可以将类似的掩模重建基于多模态反向传播基线，同时证明了出色的能量效率管理，从而降低了神经元的发射速率，以降低价值，最高为\ textbf {$ 70 \％$}降低。这样的功能意味着更可持续的实现，适合助听器或任何其他类似的嵌入式系统。

Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that together contribute to the development and specialization of our sensorimotor skills as well as to long-term memory consolidation and retrieval. Consequently, lifelong learning capabilities are crucial for computational systems and autonomous agents interacting in the real world and processing continuous streams of information. However, lifelong learning remains a long-standing challenge for machine learning and neural network models since the continual acquisition of incrementally available information from non-stationary data distributions generally leads to catastrophic forgetting or interference. This limitation represents a major drawback for state-of-the-art deep neural network models that typically learn representations from stationary batches of training data, thus without accounting for situations in which information becomes incrementally available over time. In this review, we critically summarize the main challenges linked to lifelong learning for artificial learning systems and compare existing neural network approaches that alleviate, to different extents, catastrophic forgetting. Although significant advances have been made in domain-specific learning with neural networks, extensive research efforts are required for the development of robust lifelong learning on autonomous agents and robots. We discuss well-established and emerging research motivated by lifelong learning factors in biological systems such as structural plasticity, memory replay, curriculum and transfer learning, intrinsic motivation, and multisensory integration.

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).

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）。

这篇理论文章研究了如何在计算机中构建类似人类的工作记忆和思维过程。应该有两个工作记忆存储，一个类似于关联皮层中的持续点火，另一个类似于大脑皮层中的突触增强。这些商店必须通过环境刺激或内部处理产生的新表示不断更新。它们应该连续更新，并以一种迭代的方式进行更新，这意味着在下一个状态下，应始终保留一组共同工作中的某些项目。因此，工作记忆中的一组概念将随着时间的推移逐渐发展。这使每个状态都是对先前状态的修订版，并导致连续的状态与它们所包含的一系列表示形式重叠和融合。随着添加新表示形式并减去旧表示形式，在这些更改过程中，有些保持活跃几秒钟。这种持续活动，类似于人工复发性神经网络中使用的活动，用于在整个全球工作区中传播激活能量，以搜索下一个关联更新。结果是能够朝着解决方案或目标前进的联想连接的中间状态链。迭代更新在这里概念化为信息处理策略，一种思想流的计算和神经生理决定因素以及用于设计和编程人工智能的算法。

在过去的几年中，计算机视觉的显着进步总的来说是归因于深度学习，这是由于大量标记数据的可用性所推动的，并与GPU范式的爆炸性增长配对。在订阅这一观点的同时，本书批评了该领域中所谓的科学进步，并在基于信息的自然法则的框架内提出了对愿景的调查。具体而言，目前的作品提出了有关视觉的基本问题，这些问题尚未被理解，引导读者走上了一个由新颖挑战引起的与机器学习基础共鸣的旅程。中心论点是，要深入了解视觉计算过程，有必要超越通用机器学习算法的应用，而要专注于考虑到视觉信号的时空性质的适当学习理论。

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning.

在过去的几十年中，人工智能领域大大进展，灵感来自生物学和神经科学领域的发现。这项工作的想法是由来自传入和横向/内部联系的人脑中皮质区域的自组织过程的过程启发。在这项工作中，我们开发了一个原始的脑激发神经模型，将自组织地图（SOM）和Hebbian学习在重新参与索马里（RESOM）模型中。该框架应用于多模式分类问题。与基于未经监督的学习的现有方法相比，该模型增强了最先进的结果。这项工作还通过在名为SPARP（自配置3D蜂窝自适应平台）的专用FPGA的平台上的模拟结果和硬件执行，演示了模型的分布式和可扩展性。头皮板可以以模块化方式互连，以支持神经模型的结构。这种统一的软件和硬件方法使得能够缩放处理并允许来自多个模态的信息进行动态合并。硬件板上的部署提供了在多个设备上并行执行的性能结果，通过专用串行链路在每个板之间的通信。由于多模式关联，所提出的统一架构，由RESOM模型和头皮硬件平台组成的精度显着提高，与集中式GPU实现相比，延迟和功耗之间的良好折衷。

尖峰神经网络（SNN）引起了脑启发的人工智能和计算神经科学的广泛关注。它们可用于在多个尺度上模拟大脑中的生物信息处理。更重要的是，SNN是适当的抽象水平，可以将大脑和认知的灵感带入人工智能。在本文中，我们介绍了脑启发的认知智力引擎（Braincog），用于创建脑启发的AI和脑模拟模型。 Braincog将不同类型的尖峰神经元模型，学习规则，大脑区域等作为平台提供的重要模块。基于这些易于使用的模块，BrainCog支持各种受脑启发的认知功能，包括感知和学习，决策，知识表示和推理，运动控制和社会认知。这些受脑启发的AI模型已在各种受监督，无监督和强化学习任务上有效验证，并且可以用来使AI模型具有多种受脑启发的认知功能。为了进行大脑模拟，Braincog实现了决策，工作记忆，神经回路的结构模拟以及小鼠大脑，猕猴大脑和人脑的整个大脑结构模拟的功能模拟。一个名为BORN的AI引擎是基于Braincog开发的，它演示了如何将Braincog的组件集成并用于构建AI模型和应用。为了使科学追求解码生物智能的性质并创建AI，Braincog旨在提供必要且易于使用的构件，并提供基础设施支持，以开发基于脑部的尖峰神经网络AI，并模拟认知大脑在多个尺度上。可以在https://github.com/braincog-x上找到Braincog的在线存储库。

Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI systems.This article aims to provide a comprehensive tutorial and survey about the recent advances towards the goal of enabling efficient processing of DNNs. Specifically, it will provide an overview of DNNs, discuss various hardware platforms and architectures that support DNNs, and highlight key trends in reducing the computation cost of DNNs either solely via hardware design changes or via joint hardware design and DNN algorithm changes. It will also summarize various development resources that enable researchers and practitioners to quickly get started in this field, and highlight important benchmarking metrics and design considerations that should be used for evaluating the rapidly growing number of DNN hardware designs, optionally including algorithmic co-designs, being proposed in academia and industry.The reader will take away the following concepts from this article: understand the key design considerations for DNNs; be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics; understand the trade-offs between various hardware architectures and platforms; be able to evaluate the utility of various DNN design techniques for efficient processing; and understand recent implementation trends and opportunities.

Many theories, based on neuroscientific and psychological empirical evidence and on computational concepts, have been elaborated to explain the emergence of consciousness in the central nervous system. These theories propose key fundamental mechanisms to explain consciousness, but they only partially connect such mechanisms to the possible functional and adaptive role of consciousness. Recently, some cognitive and neuroscientific models try to solve this gap by linking consciousness to various aspects of goal-directed behaviour, the pivotal cognitive process that allows mammals to flexibly act in challenging environments. Here we propose the Representation Internal-Manipulation (RIM) theory of consciousness, a theory that links the main elements of consciousness theories to components and functions of goal-directed behaviour, ascribing a central role for consciousness to the goal-directed manipulation of internal representations. This manipulation relies on four specific computational operations to perform the flexible internal adaptation of all key elements of goal-directed computation, from the representations of objects to those of goals, actions, and plans. Finally, we propose the concept of `manipulation agency' relating the sense of agency to the internal manipulation of representations. This allows us to propose that the subjective experience of consciousness is associated to the human capacity to generate and control a simulated internal reality that is vividly perceived and felt through the same perceptual and emotional mechanisms used to tackle the external world.

信号处理是几乎任何传感器系统的基本组件，具有不同科学学科的广泛应用。时间序列数据，图像和视频序列包括可以增强和分析信息提取和量化的代表性形式的信号。人工智能和机器学习的最近进步正在转向智能，数据驱动，信号处理的研究。该路线图呈现了最先进的方法和应用程序的关键概述，旨在突出未来的挑战和对下一代测量系统的研究机会。它涵盖了广泛的主题，从基础到工业研究，以简明的主题部分组织，反映了每个研究领域的当前和未来发展的趋势和影响。此外，它为研究人员和资助机构提供了识别新前景的指导。

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.

人类和其他动物学会从感觉体验中提取一般概念，没有大量的教学。这种能力被认为是睡眠的离线状态，如睡眠，以前的经验在全身重放。然而，梦想的特征创造性本质表明，学习语义表示可能超越仅仅重播以前的经历。我们通过实施由生成的对冲网络（GANS）启发的皮质架构来支持这一假设。我们模型中的学习是在三种不同的全球脑状态下组织，模仿清醒，NREM和REM睡眠，优化不同但互补的客观功能。我们在自然图像的标准数据集上培训模型，并评估学习符号的质量。我们的结果表明，通过对抗睡眠期间通过对抗梦想产生新的虚拟感官输入对于提取语义概念至关重要，同时通过在NREM睡眠期间通过扰动梦想重放剧集的集更记忆，提高了潜在表示的鲁棒性。该模型在睡眠状态，记忆重放和梦想中提供了一种新的计算透视，并提出了GAN的皮质实施。

迄今为止，通信系统主要旨在可靠地交流位序列。这种方法提供了有效的工程设计，这些设计对消息的含义或消息交换所旨在实现的目标不可知。但是，下一代系统可以通过将消息语义和沟通目标折叠到其设计中来丰富。此外，可以使这些系统了解进行交流交流的环境，从而为新颖的设计见解提供途径。本教程总结了迄今为止的努力，从早期改编，语义意识和以任务为导向的通信开始，涵盖了基础，算法和潜在的实现。重点是利用信息理论提供基础的方法，以及学习在语义和任务感知通信中的重要作用。

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。