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Dezhe Z Jin

Dezhe Z Jin

Main Content

  • Associate Professor of Physics
123 Davey
Email: dzj2 [ AT ] psu [ DOT ] edu
Phone: (814) 863-6673


  1. Tsinghua University (B.S., Physics), 1990
  2. Tsinghua University (M.S., Physics), 1994
  3. University of California, San Diego (Ph.D., Physics), 1999


Biological Physics
  • Theoretical
  • Computational

Honors and Awards

  • Alfred P. Sloan Research Fellowship, 2006 to 2008.
  • Guang Hua Fellowship, Tsinghua University, 1994.
  • Graduation with distinction, Tsinghua University, 1990.
  • American Physical Society, member.
  • Society for Neuroscience, member.
  • Sigma Xi, The Scientific Research Society, member.

Selected Publications

Full list of publications on Google Scholar

  1. P. B. Schafer and D. Z. Jin, "Noise-robust speech recognition through auditory feature detection and spike sequence decoding", Neural Computation, accepted (2013).

  2. J. Wittenbach, K. Bouchard, M. Brainard, and D. Z. Jin, “The role of adaptive auditory feedback in controlling repeating syllables in birdsong”, to be submitted (2013).
  3. Y. Zhang, J. Wittenbach, D. Z. Jin, A. A. Kozhevnikov, “Neural substrate of birdsong syntax identified by cooling the brain”, submitted to Proceedings of National Academy of Science (2013).
  4. A. Miller and D. Z. Jin, “Potentiation decay of synapses and the length distributions of synfire chains self-organized in recurrent neural networks”, submitted to Physical Review E (2013).
  5. D. Z. Jin, “The Neural Basis of Birdsong Syntax”, in “Progress in Cognitive Science: From Cellular Mechanisms to Computational Theories”, Peking University Press (2013).
  6. D. Z. Jin and A. A. Kozhevnikov, “A compact statistical model of the song syntax in Bengalese finch”, PLoS Computational Biology, 7(3), e1001108 (2011).
  7. M. A. Long, D. Z. Jin, and M. S. Fee, “Support for a synaptic chain model of sequence generation from intracellular recordings in the singing bird”, Nature, 468, 394 (2010).
  8. T. M. Desrochers, D. Z. Jin, N. D. Goodman, and A. M. Graybiel, “Optimal habits can develop spontaneously through sensitivity to local cost”, Proceedings of National Academy of Science, 107, 20512 (2010).
  9. D. Z. Jin, “Generating variable birdsong syllable sequences with branching chain networks in avian premotor nucleus HVC”, Physical Review E, 80, 051902 (2009).
  10. D. Z. Jin, N. Fujii, and A. M. Graybiel, “Neural representation of time in cortico-basal ganglia circuits”, Proceedings of National Academy of Science, 106, 19156 (2009).
  11. W. Chang and D. Z. Jin, “Spike propagation in driven chain networks with dominant global inhibition”, Physical Review E, 79, 051917 (2009).

Research Interests

Consisting of a large number of intricately connected neurons, the brain is one of the most sophisticated dynamical systems in nature. Understanding how the brain computes is at the forefront of current scientific research. Research in our group focuses on theoretical analysis of biological neural networks and computational models of song generation in songbirds, speech recognition in humans, and learning and memory in the hippocampus. The modeling is done in close collaboration with experimental groups.

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