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雷鋒網AI科技評論按：當前計算機技術面臨著兩個重要瓶頸：（1）摩爾定律失效；（2）「馮諾依曼」架構導致的能效低下。
隨著集成電路的規模越來越接近物理極限，人類若想進一步提升計算機的性能，必然要考慮新的計算機架構。而另一方面，「馮諾依曼」架構中，運算單元和存儲單元分離，使得大部分能量和時間都消耗在數據的讀取和存儲過程中；并且數據處理是基于串行結構，即同一時刻只能執行一個任務。
這與人腦處理信息的方式差別巨大。在進行學習和認知等復雜計算時，人腦的功耗只有 20 瓦；而目前最先進的計算機模擬人腦功能，功耗也高達 800 萬瓦以上，速度比人腦要慢 1000 倍以上。究其原因，是因為現代計算機一般使用固定的數字化的程序模型，同步、串行、集中、快速、具有通用性地處理問題，數據存儲與計算過程在不同地址空間完成。而與之形成鮮明對比的是，人的大腦會重復利用神經元，并突觸、異步、并行、分布式、緩慢、不具通用性地處理問題，是可重構的、專門的、容錯的生物基質，并且人腦記憶數據與進行計算的邊界是模糊的。
鑒于此，當前借鑒人腦發展的類腦計算技術，被認為是應對當前挑戰的重要方案。“類腦計算”本質來說，即利用神經計算來模擬人類大腦處理信息的過程，被認為“下一代人工智能”的重要方向，也是當前人工智能領域的熱點方向。
與經典人工智能符號主義、連接主義、行為主義以及機器學習的統計主義這些技術路線不同，類腦計算采取的是仿真主義：
結構層次模仿腦（非馮·諾依曼體系結構）
器件層次逼近腦（神經形態器件替代晶體管）
智能層次超越腦（主要靠自主學習訓練而不是人工編程）
因此，類腦計算試圖建立一個類似的架構，使得計算機也能保持類腦的復雜性，達到可處理小數據 & 小標注問題、適用于弱監督和無監督問題、關聯分析能力強、魯棒性強、計算資源消耗較少、具備認知推理能力、時序相關性好、可能解決通用場景問題的目的，最終實現強人工智能和通用智能。
目前，國際上類腦計算研究已經取得顯著進展，技術探索階段已經過去，技術預研已經開始，一些關鍵技術獲得突破，相關的技術原型和系統原型已開發成功。
總的來說，類腦智能技術體系分四層：基礎理論層、硬件層、軟件層、產品層。
基礎理論層基于腦認知與神經計算，主要從生物醫學角度研究大腦可塑性機制、腦功能結構、腦圖譜等大腦信息處理機制研究；
硬件層主要是實現類腦功能的神經形態芯片，也就是非馮諾依曼架構的類腦芯片，如脈沖神經網絡芯片、憶阻器、憶容器、憶感器等；
軟件層包含核心算法和通用技術，核心算法主要是弱監督學習和無監督學習機器學習機制，如脈沖神經網絡、增強學習、對抗神經網絡等；
通用技術主要是包含視覺感知、聽覺感知、多模態融合感知、自然語言理解、推理決策等；產品層主要包含交互產品和整機產品，交互產品包含腦機接口、腦控設備、神經接口、智能假體等，整機產品主要有類腦計算機、類腦機器人等。
類腦計算，被視為未來信息技術最具有發展前景的重要領域之一，正如歐盟人腦旗艦研究計劃所指出的：「在未來 20 到 30 年內，誰要想主導世界經濟，誰必須在類腦計算這個領域領先」。
中國學術領域近年來也越來越關注類腦計算的研究。這表現在多個方面。其一，國家層面上，從2016年起制定了為期15年的「腦計劃」，類腦計算正是其核心研究領域之一。另一方面，在近些年來，有越來越多的會議開始設置「類腦計算」專場或論壇。例如前不久剛結束的由中國計算機學會、雷鋒網、香港中文大學共同舉辦的 CCF-GAIR 2019大會便設置了此專場。
近期將舉辦的國際圖像圖形學學術會議也將舉辦「類腦智能論壇」。
國際圖象圖形學學術會議（ICIG）是中國圖象圖形學學會主辦的最高級別的系列國際會議，創建于2000年，每兩年舉辦一屆，迄今已經成功舉辦九屆。
第十屆國際圖象圖形學學術會議（ICIG2019）將于2019年8月23-25日在北京友誼賓館召開，主題為“人工智能時代的圖像圖形前沿研究”，由清華大學、北京大學和中國科學院自動化研究所承辦，得到了國際模式識別協會（IAPR）的支持。本次的會議共包含了3個特邀報告、2個講習班、3個workshops，多個論壇。其中之一便為「類腦智能論壇」。
類腦智能論壇由CSIG機器視覺專委會承辦，由中科院自動化所何暉光研究員和深圳職業技術學院人工智能學院院長楊金峰教授共同組織，邀請到6位專家從不同的角度來介紹類腦智能的研究進展、類腦研究中的難點問題，并對今后的研究進行展望。
各專家報告內容可參考如下：
Si Wu
Peking University
Title：Push-pull Feedback Implements Rough-to-fine Information Processing
Abstract: Experimental data has revealed that in addition to feedforward connections, there exist abundant feedback connections in a hierarchical neural pathway. Although the importance of feedback in neural information processing has been widely recognized in the field, the detailed mechanism of how it works remains largely unknown. Here, we investigate the role of feedback in hierarchical memory retrieval. Specifically, we consider a multi-layer network which stores hierarchical memory patterns, and each layer of the network behaves as an associative memory of the corresponding hierarchy. We find that to achieve good retrieval performance, the feedback needs to be dynamical: at the early phase, the feedback is positive (push), which suppresses inter-class noises between memory patterns; at the late phase, the feedback is negative (pull), which suppresses intra-class noises between memory patterns. Overall, memory retrieval in the network progresses from rough to fine. Our model agrees with the push-pull phenomenon observed in neural data and sheds light on our understanding of the role of feedback in neural information processing.
Biography: Dr. Si Wu is Professor at School of Electronics Engineering & Computer Science, Principle Investigator at IDG/McGovern Institute for Brain Research, and Principle Investigator at PKU-Tsinghua Center for Life Science in Peking University. He was originally trained as a theoretical physicist and received his BSc, MSc, and PhD degrees all from Beijing Normal University (87-95). His research interests have turned to Artificial Intelligence and Computational Neuroscience since graduation. He worked as Postdocs at Hong Kong University of Science & Technology (95-97), Limburg University of Belgium (97-98), and Riken Brain Science Institute of Japan (98-00), and as Lecturer/Senior Lecturers at Sheffield University (00-02) and Sussex University (03-08) of UK. He came back to China in 2008, and worked as PI at Institute of Neuroscience in Chinese Academy of Sciences (08-11) and Professor in Beijing Normal University (11-18). His research interests focus on Computational Neuroscience and Brain-inspired Computing. He has published more than 100 papers, including top journals in neuroscience, such as Neuron, Nature Neuroscience, PNAS, J. Neurosci., and top conferences in AI, such as NIPS. He is now Co-editor-in-chief of Frontiers in Computational Neuroscience.
Sen Song
Tsinghua University
Title: Recent progress in brain research and inspirations for neurocomputing
Abstract: Recently, big scale neuronal recordings are starting to reveal the way information is represented in the nervous system. At the same time, analysis of artificial neural networks trained by deep learning is also starting to reveal its representations. In this talk, I will try to summarize and compare representations in deep neural networks and the brain, regarding objects, object features, object relations, tree like structures and graph-like structures, and start to build a mathematical framework to describe them.
Biography: Dr. Sen Song is an principal investigator at Tsinghua Laboratory for Brain and Intelligence and Department of Biomedical Engineering at Tsinghua University. He received his Ph.D. degree in Neuroscience from Brandeis University in 2002. Before joining Tsinghua in 2010, he did post-doctoral research at Cold Spring Harbor Laboratory and Massachusetts Institute of Technology. His work in computational neuroscience on spike-timing dependent plasticity and motif analysis of cortical connectivity have been widely cited and form some of the theoretical foundations of brain-inspired computing. His current work involves computational neuroscience, neural circuits underlying emotions and motivations, and the interface between neuroscience and artificial intelligence.
Wenming Zheng
Southeast University
Title: Action Intention Understanding and Emotion Recognition for Human Computer Interface
Abstract: Action intention understanding and emotion recognition play an important role in human computer interface. In this talk, I will address the methods of action intention understanding and emotion recognition from psychophysiological signals, such as EEG or audiovisual signals. Then, I will also briefly address the applications of this research in medical treatment and education.
Biography: Wenming Zheng received his PhD degree in signal and information processing from the Department of Radio Engineering, Southeast University, Nanjing, China, in 2004. He is currently a Professor and the Director of the Key Laboratory of Child Development and Learning Science, Southeast University. He ever worked as a visiting scholar or visiting professor at Microsoft Research Asia (MSRA), Chinese University of Hong Kong (CUHK), University of Illinois at Urbana-Champaign (UIUC), and Cambridge University, respectively. His current research interests include affective information processing for multi-modal signals, e.g., facial expression, speech, and EEG signals, and their applications in education and medical care. Dr. Zheng was an Awardee the Microsoft Young Professor Professorship. He won the Second Prize of the National Technological Invention in 2018, the Second Prize of the Natural Science of Ministry of Education in 2008 and 2015, the Second Prize of the Jiangsu Provincial Science and Technology Progress in 2009. He served as an Associated Editor of several peer reviewed journals, such as IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, Neurocomputing, and The Visual Computer. He is a Council Member of the Chinese Society of Cognitive Science.
Yijun Wang
Chinese Academy of Sciences
Title: Recent Progress in Brain-Machine Integration Technology
Abstract: The brain-computer interface (BCI) technology establishes a direct communication channel between the brain and external devices, which can replace, restore or enhance human’s perception, cognition and motor functions. In recent years, as a new form of hybrid intelligence, the BCI-based brain-machine integration technology has shown great potential in the fields of healthcare, human-computer interaction, and national defense. In this talk, I will introduce recent progress in the development of the brain-machine integration technology. I will first review the history, current status, methodology, and challenges in this field. I will then present examples of progress of the brain-machine integration technology in communication and control, human augmentation, multi-modal integration, and biometrics.
Biography: Yijun Wang is a Research Fellow at the Institute of Semiconductors, Chinese Academy of Sciences, and a member of CAS Center for Excellence in Brain Science and Intelligence Technology. He was selected by the Thousand Youth Talents Plan of China in 2015. He received a B.E. degree and a Ph.D. degree in biomedical engineering from Tsinghua University in 2001 and 2007, respectively. From 2008 to 2015, he was first a Postdoctoral Fellow and later an Assistant Project Scientist at the Institute for Neural Computation, University of California San Diego, USA. His research mainly focuses on neural engineering and neural computation. His research interests include brain-computer interface (BCI), biomedical signal processing, and machine learning. He has published more than 100 papers in scientific journals and conferences such as PNAS, Journal of Neuroscience, IEEE Transactions on Biomedical Engineering. His papers have been cited more than 4500 times according to Google Scholar.
Xiaolin Hu
Tsinghua University
Title: Deep Learning Predicts Correlation between a Functional Signature of Higher Visual Areas and Sparse Firing of Neurons
Abstract: Visual information in the visual cortex is processed in a hierarchical manner. Recent studies show that higher visual areas, such as V2, V3, and V4, respond more vigorously to images with naturalistic higher-order statistics than to images lacking them. This property is a functional signature of higher areas, as it is much weaker or even absent in the primary visual cortex (V1). However, the mechanism underlying this signature remains elusive. We studied this problem using computational models. In several typical hierarchical visual models including the AlexNet, VggNet and SHMAX, this signature was found to be prominent in higher layers but much weaker in lower layers. By changing both the model structure and experimental settings, we found that the signature strongly correlated with sparse firing of units in higher layers but not with any other factors, including model structure, training algorithm (supervised or unsupervised), receptive field size, and property of training stimuli. The results suggest an important role of sparse neuronal activity underlying this special feature of higher visual areas.
Biography: Xiaolin Hu is an associate professor in the Department of Computer Science and Technology, Tsinghua University, Beijing, China. He got his PhD degree in Automation and Computer-Aided Engineering at The Chinese University of Hong Kong in 2017. He was a postdoc at Tsinghua University during 2017-2019. His research areas include artificial neural networks and computational neuroscience. His main research interests include developing brain-inspired computational models and revealing the visual and auditory information processing mechanism in the brain. He has published over 70 research papers in journals include IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Image Processing, IEEE Transactions on Cybernetics, PLoS Computational Biology, Neural Computation, and conferences include CVPR, NIPS, AAAI. He serve as an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems and Associate Editor of Cognitive Neurodynamics.
Jian Liu
University of Leicester
Title: Towards the next generation of computer vision: visual computation with spikes
Abstract: Neuromorphic computing has been suggested as the next generation of computational strategy. In terms of vision, the retina is the first stage of visual processing in the brain. The retinal coding is for understanding how the brain processes stimulus from the environment, moreover, it is also a cornerstone for designing algorithms of visual coding, where encoding and decoding of incoming stimulus are needed for better performance of physical devices. Here, by using the retina as a model system, we develop some data-driven approaches, spike-triggered non-negative matrix factorization and deep learning nets for characterizing the encoding and decoding of natural scenes by retinal neuronal spikes. I further demonstrate how these computational principles of neuroscience can be transferred to neuromorphic chips for the next generation of the artificial retina. As a proof of concept, the revealed mechanisms and proposed algorithms here for the retinal visual processing can provide new insights into neuromorphic computing with the signal of events or neural spikes.
Biography: Dr. Jian Liu received the Ph.D. in mathematics from UCLA, then worked as Postdoc Fellow at CNRS, France, and University of Goettingen, Germany. He is currently a Lecturer of Computational Neuroscience at University of Leicester, UK. His area of research includes computational neuroscience and brain-inspired computation for artificial intelligence. His work was published in Nature communications, eLife, Journal of neuroscience, PLoS computational biology, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on cybernetics, etc.
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