Tielin Zhang

(In English):

I am Tielin Zhang (Thomas Zhang) from Institute of Automation, Chinese Academy of Sciences (CASIA). I am now an assistant professor in CASIA, my research interests focus on the following parts:

(1) The biology-inspired spiking neural networks (SNNs): Following after the conceptual machines, shallow or deep neural networks, SNNs are considered as the third generation of neural networks. The hints and inspirations from biological systems will at least answer these questions: What are the intrinsic reasons that biological networks could form cognitive functions from scratch, What are the basic mathematically learning rules in this procedure, and How to construct powerful brain-inspired computational algorithms and hardware chips based on these rules, and so on. Until now, for the pattern recognition task, we have built SNNs based on pure biology plasticity rules and have reached 98.64% accuracy on standard MNIST dataset. Next, we will expand them to other temporal tasks and construct task-specific neuron chips.

(2) Biological structure reconstruction for micron-scale whole rat brain: With the provided 16,216 biological rat-brain slices from cooperators, I make a deep neural network model for the biological tissue recognition with very high accuracy. Then the data are loaded into Houdini (a 3D image processing software) for noise filtering and image reconstruction with the cooperation of my colleague Xinhe Zhang. The whole rat-brain atlas is multi-scales: on the microscale, detailed structures of somas, dendrites, axons are clear (not with the direction of synapses for the limitation of Golgi-type imaging); on the mesoscale and macroscale, the neuron group connections and their motif connection types are clear in and between different brain regions. This will be an important basis for the next-step research on brain connectome reconstruction and brain simulation.

(3) Brain-inspired software or hardware strengthened cognitive robots: Nowadays, the robotic developments of hardware are much faster than that of software. With the help of the robot operating system (ROS), the basic information processing layer for the management of hardware is built. At the same time, the structural efforts (e.g. rat brain atlas) and functional efforts are combined together for a more powerful biologically plausible robot architecture. This cognitive architecture will handle tens of cognitive functions for the purpose of artificial general intelligence (AGI).

(4) Community contributions: I have been working as reviewers of different journals and conferences, for example, Journal of Cognitive Computation, Journal of Cognitive Systems Research, SCIENCE CHINA Information Sciences, International Conference on Brain Informatics, International Conference on Agents, and so on.

 

Mail: tielin.zhang@ia.ac.cn

Institute: Institute of Automaiton, Chinese Academy of Sciences

Department: Research Center for Brain-inspired Intelligence

Address: Zhongguancun East Road 95, Haidian District, Beijing, China, 100190

 

(In Chinese):

张铁林,中国科学院自动化研究所类脑智能研究中心,现任助理研究员。我较为关注以下几个方面的研究:通过借鉴神经系统的结构与功能的特点及可塑性,建立类脑多尺度神经网络计算模型、类脑智能信息处理理论与方法;通过对全脑网络的数据分析,构建功能和结构可塑的多尺度脑神经网络计算模型,并以此作为创新源泉,启发人工智能研究中的时空数据信息融合、学习记忆等智能信息处理理论与方法;进一步研制基于类脑计算模型的智能机器人,研究类脑启发的机器人认知框架。我同时也参与认知系统研究期刊、认知计算期刊、中国科学信息科学期刊、脑信息学国际会议、智能体研究国际会议等的审稿工作。

 

【实习生招聘(Calling for interns)】有意者请联系tielin.zhang@ia.ac.cn, 或加QQ群咨询更多细节:644050963
(1)微米尺度生物脑结构三维重建实习生:主要负责生物图像信息处理,如基于深度学习的三维图像分类,或三维结构信息配准及修复等。要求计算机相关专业,具有图像识别/图像修复/python编程等方面的基础者优先(或个人十分有兴趣),要求大三及以上,要求连续实习二个月以上。我们将提供多对一的指导,采用小组组队攻关模式,专用GPU运算服务器/胖节点服务器等硬件资源,让新人尽快成长。
(2)类脑认知机器人算法及应用实习生:主要负责基于ROS的认知机器人开发,要求自动控制及相关专业(或个人十分感兴趣),具有机器人ROS开发经验/python编程基础者优先,最好能实习二个月以上。我们将提供Baxter机器人、Robotnik机器底盘、NAO机器人等,作为硬件支撑实习生的研究。
(3)脉冲神经网络算法及应用实习生:脉冲神经网络的实现原理分析及其在不同任务上的应用,具有Matlab或Python开发经验、机器学习背景者优先,最好能实习半年以上。

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