Zhang Danke

Zhang Danke

PhD,Associate investigator
computational models of neural information processing, currently working on learning and memory models to explain basic structural and dynamical properties of cortical networks
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Research

Cortical circuit encodes, transmits and stores neural information. Our research in general is to develop biologically motivated single neuron and network models that can explain neuroscience experimental observations, and, can be used to mathematically analyze their computational roles in terms of neural information processing. Ongoing work includes:
1)develop a general associative learning model that can explain basic structural and dynamical properties of local cortical networks;
2) reconstruct the micro-scale connectome of a cortical column based on various 3-D morphologically defined cell types;
3) develop a local, online and biologically plausible learning algorithm that can reproduce experimentally observed structural properties of cortical networks.

Biography

2019.08 - now, Associate investigator at SIAT, CAS, Shenzhen
2016.06 - 2019.07, Postdoc at Northeastern University, Boston 
2013.09 - 2016.06, Lecturer at Hangzhou Dianzi University, Hangzhou
2011.09 - 2013.09, PHD student at Beijing Normal University, Beijing
2009.09 - 2011.07, PHD student at Institute of Neuroscience, CAS, Shanghai
2009.09 - 2013.09, PhD Student at South China University of Technology, Guangzhou

Selected publications

[1] Danke Zhang, Chi Zhang and Armen Stepanyants, Reconstructing connectome of the cortical column with biologically-constrained associative learning. (in preparation)

[2] Chi Zhang, Danke Zhang, and Armen Stepanyants, Biologically inspired model of associative memory storage with noisy neurons and synapses. Submitted. Website: www.biorxiv.org/content/10.1101/583922v1 (in preparation)

[3] Danke Zhang, Chi Zhang and Armen Stepanyants, Robust associative learning is sufficient to explain structural and dynamical properties of local cortical circuits, Journal of Neuroscience, 2019. Math details: www.biorxiv.org/content/10.1101/320432v1

[4] Danke Zhang, Si Wu, Malte J. Rasch, Circuit Motifs for Contrast-Adaptive Differentiation in Early Sensory Systems: The Role of Presynaptic Inhibition and Short-Term Plasticity, PloS one, 1-25, 2015.

[5] Danke Zhang, Yuanqing Li, Si Wu, Malte J. Rasch. Design Principles of the Sparse Coding Network and the Role of “Sister Cells” in the Olfactory System of Drosophila, Frontiers in Computational Neuroscience, 7, 1-14, 2013.

[6] Danke Zhang, Yuanqing Li, Malte J. Rasch, Si Wu. Nonlinear Multiplicative Dendritic Integration in Neuron and Network Model, Frontiers in Computational Neuroscience, 7,1-15, 2013.

[7] Lei Xiao, Danke Zhang, Peiji Liang, Si Wu. Adaptive Neural Information Processing with Dynamical Electrical Synapses, Frontiers in Computational Neuroscience, 7, 1-9, 2013.

[8] Longwen Huang*, Yuwei Cui*, Danke Zhang*, Si Wu. Impact of Noise Structure and Network Topology on Tracking Speed of Neural Networks, Neural Networks, 24(10):1110-1119, 2011. (*co-first author)

[9] Danke Zhang, Yuanqing Li, Si Wu. Concentration-Invariant Representation in the Olfactory System by Presynaptic Inhibition, Computational and Mathematical Methods in Medicine, 1-6, 2013.

Book chapters
[10] Danke Zhang, Malte J. Rasch, Si Wu. Simple Models of Sensory Information Processing, Chapter 12 in Computational Models of Brain and Behavior, John Wiley&Sons, 2017.

Selected conference Papers
[11] Danke Zhang, Chi Zhang and Armen Stepanyants. Reconstructing connectome of the cortical column with biologically-constrained associative learning. CNS2019, Barcelona, 2019. (travel award)

[12] Chi Zhang, Danke Zhang, and Armen Stepanyants. Fluctuations in neural activity are reflected in the structure of associative memory networks. COSYNE2019, Lisbon, Portugal, 2019. (travel award)

[13] Danke Zhang, Chi Zhang and Armen Stepanyants. Structural and dynamical properties of local cortical networks result from robust associative learning. CNS2018, Seattle, 2018. (oral presentation, top 5%)

[14] Danke Zhang, Chi Zhang and Armen Stepanyants. Order-to-chaos phase transition in recurrent networks operating at maximum capacity for storing sequences of network states, ICMNS2017, Boulder, 2017. (Oral presentation)

[15] Danke Zhang, Xichun Zhang, Malte J. Rasch, Si Wu. Divisive Normalization by shunting inhibition in neural networks, IJCAI-WIS2013, Beijing, 2013. (Oral presentation)

[16] Danke Zhang, Yuwei Cui, Yuanqing Li, Si Wu. Simple Models for Synaptic Information Integration, International Conference on Neural Information Processing 2011 (ICONIP2011), 7064: 210-216, 2011. (Oral presentation)

[17] Danke Zhang, Simple Neuron and Network models for Shunting Inhibition, The 12th China-Japan-Korea Joint workshop on Neurobiology and Neuroinformatics, Seoul, 2012. (Oral presentation)