Joe Z. Tsien

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Joe Z. Tsien（钱卓）[1] is a neuroscientist who pioneered Cre/lox-neurogenetics in the mid-1990s,[2] a versatile toolbox for neuroscientists to study the complex relationships between genes, neural circuits, and behaviors.[3] He is also known as the creator of the smart mouse Doogie in the late 1990s while being a faculty member at Princeton University.[4][5]

Recently, he developed the Theory of Connectivity in an effort to explain the origin of intelligence, or the basic design principle underlying brain computation and intelligence.[6][7] The theory states that brain computation is organized by a power-of-two-based permutation logic in constructing cell assemblies - the basic building blocks of neural circuits.[8] The theory has received a set of validation from multiple experiments. The discovery of this basic computational logic of the brain can have important implications for the development of artificial general intelligence.

In addition, Tsien has also postulated the Neural Self-Information Theory to describe how the brain encodes the moment-to-moment perceptions, memories, spatial navigation, decision-making and conscious action executions. [9] [10] The Neural Self-Information Theory and Theory of Connectivity may provide two fundamental frameworks to not only understand how the brain works, but also the development of brain-inspired neuromorphic computation.

Education[edit]

Tsien earned his A.B. in Biology/Physiology from East China Normal University in Shanghai (1984). Tsien obtained his Ph.D. in Molecular Biology from the University of Minnesota in 1990.

Career[edit]

In the early and mid-1990s, Tsien worked with two Nobel laureates, Eric Kandel and Susumu Tonegawa. In 1997, Tsien became a faculty member in the Department of Molecular Biology at Princeton University, where he genetically engineered and created Doogie, a smart mouse. In 2007, Tsien launched the Brain Decoding Project under which he has led a team of neuroscientists, computer scientists and mathematicians to record and decipher systematically the neural codes in the mouse brain, with funding supported in part by Georgia Research Alliance. Tsien's Brain Decoding Project has provided a valuable test case and inspiration for other neuroscientists in Europe and the United States to initiate large-scale projects such as the BRAIN Initiative and Human BRAIN Projects in 2013.

Tsien is currently working in China and continues to serve as the director of the International Brain Decoding Project Consortium.

Research[edit]

Tsien pioneered Cre-loxP-mediated brain subregion- and cell type-specific genetic techniques in 1996,[3] enabling researchers to manipulate or introduce any gene in a specific brain region or a given type of neuron.[2] This transformative technique has led to NIH Blueprint for Neuroscience Research in launching several Cre-driver Mouse Resource projects. Over the past 20 years, Cre-lox recombination-mediated neurogenetics has emerged as one of the most powerful and versatile technology platforms for cell-specific gene knockouts, transgenic overexpression, neural circuit tracing, Brainbow, optogenetics, CLARITY, voltage imaging and chemical genetics.[2][11][12]

Tsien is also widely known as the creator of the smart mouse Doogie.[13] While as a faculty at Princeton University, Tsien has speculated that one of the NMDA receptor's subunits may hold the key for superior learning and memory at young ages. Accordingly, his laboratory genetically engineered a transgenic mouse in which they over-expressed the NR2B subunit of the NMDA receptor in the mouse cortex and hippocampus. In 1999, his team reported that the transgenic mouse, nicknamed Doogie, indeed showed to have enhanced synaptic plasticity and enhanced learning and retention, as well as greater flexibility in learning new patterns.[4] The discovery of the NR2B as a key genetic factor for memory enhancement prompted other researchers to discover over two dozen other genes for memory enhancement, many of which regulate the NR2B pathway.[14] One of the NR2B-based memory-enhancement strategies, via dietary supplements of a brain-penetrating magnesium ion, magnesium L-threonate, is currently undergoing clinical trials for memory improvement.[15][16]

Tsien has also made several other major discoveries, including the unified cell-assembly mechanism for explaining how episodic memory and semantic memory are generated in the memory circuits.[17][18][19] His laboratory also discovered the nest cells in the mouse brain, revealing how animals actually encode the abstract concept of nest or home.[20][21]

Tsien is also the first to show that defective Alzheimer's genes (e.g. presenilin-1) impaired adult neurogenesis in the dentate gyrus of the hippocampus,[22] revealing the role of adult neurogenesis in memory clearance.[23][24]

In addition, Tsien has developed a method capable of selectively erasing a given fear memory in the mouse brain while leaving other memories intact.[25][26]

Tsien also demonstrated that the NMDA receptor in the dopamine circuit plays a crucial role in the formation of habit.[27][28][29]

Tsien is currently leading a team of neuroscientists, computer scientists and mathematicians, who are working on the Brain Decoding Project,[30] a large-scale brain activity mapping effort, which he and his colleagues have initiated since 2007 with the support from the Georgia Research Alliance (GRA).[31]

In 2015, Tsien developed the Theory of Connectivity to explain the design principle upon which evolution and development may construct the brain to be capable of generating intelligence.[6][7] This theory has made six predictions which have received supportive evidence by a recent set of experiments on both the mouse brain and hamster brain.[8] At its core, the Theory of Connectivity predicts that the cell assemblies in the brain are not random, rather they should conform to the power-of-two-based equation, N = 2i - 1, to form the pre-configured building block termed as the functional connectivity motif (FCM). Instead of using a single neuron as the computational unit in some extremely simple brains, the theory denotes that in most brains, a group of neurons exhibiting similar tuning properties, termed as a neural clique, should serve as the basic computing processing unit (CPU). Defined by the power-of-two-based equation, N = 2i - 1, each FCM consists of principal-projection neuron cliques (N), ranging from those specific cliques receiving specific information inputs (i) to those general and sub-general cliques receiving various combinatorial convergent inputs.

As the evolutionarily conserved logic, the validation of Theory of Connectivity requires experimental demonstrations of the following basic properties: 1) Anatomical prevalence - FCMs are prevalent across neural circuits, regardless of gross anatomical shapes; 2) Species conservancy - FCMs are conserved across different animal species; and 3) Cognitive universality - FCMs serve as a universal computational logic at the cell-assembly level for processing a variety of cognitive experiences and flexible behaviors. 4) More importantly, this Theory of Connectivity further predicts that the specific-to-general combinatorial connectivity pattern within FCMs should be pre-configured by evolution, and emerge innately from development as the brain's computational primitives. 5) This Theory of Connectivity also explains the general purpose and computational algorithm of the neocortex. This proposed design principle of intelligence can be examined via various experiments and also be modeled by neuromorphic engineers and computer scientists. The same power-of-two-based permutation logic has recently been described for lexical retrieval processes in humans, which shows parallels to the computing base of the quantum computer.[32][33] However, Dr. Joe Tsien cautions that artificial general intelligence based on the brain's principles can come with great benefits and, potentially, even greater risks.[34]

Moreover, Tsien lab has focused on the cracking of real-time neural code—the rule under which information is signaled to generate the moment-to-moment cognitions including seeing a car, recalling a memory or being consciously aware of time and location. Traditionally, the rate code, which firing spike rasters were averaged over multiple trials to overcome firing variability, was proposed as a way for scientists to anaylyze the tuning properties of a given neuron. However, it is obvious the rate code is not how the brain actually uses to represent real-time cognitions due to the enormous firing variabiity from one moment to another. To solve this fundamental problem, Tsien has proposed the Neural Self-Information Theory which states that the interspike-interval (ISI), or the silence-duration between 2 adjoining spikes, carries self-information that is inversely proportional to its variability-probability. Specifically, higher-probability ISIs convey minimal information because they reflect the ground state, whereas lower-probability ISIs carry more information, in the form of “positive” or “negative surprisals,” signifying the excitatory or inhibitory shifts from the ground state, respectively. These surprisals serve as the quanta of information to construct temporally coordinated cell-assembly ternary codes representing real-time cognitions. [35]

Accordingly, Tsien devised a general decoding method and unbiasedly uncovered 15 cell assemblies underlying different sleep cycles, fear-memory experiences, spatial navigation, and 5-choice serial-reaction time (5CSRT) visual-discrimination behaviors. His team revealed that robust cell-assembly codes were generated by ISI surprisals constituted of ~20% of the skewed ISI gamma-distribution tails, conforming to the “Pareto Principle” that specifies, for many events—including communication—roughly 80% of the output or consequences come from 20% of the input or causes. These results demonstrate that real-time neural codes arise from the temporal assembly of neural-clique members via ISI variability-based self-information principle. [36]


Recognition[edit]

Tsien has received awards for his research contributions, including:

  • 2012 Distinguished Scientist Award from the International Behavioral and Neural Genetics Society
  • Keck Distinguished Young Scholar Award
  • Burroughs Wellcome Young Investigator Award
  • Scientific Achievement Award from the Association of Chinese Americans
  • Beckman Young Investigator Award

Tsien's work on the creation of smart mouse was also selected for TIME magazine cover story in 1999 as well as New York Times's Scientist At Work section story.

Popular science[edit]

Due to his breakthrough in discoveries of brain mechanisms, Tsien has been invited to contribute two cover-story articles for Scientific American in the areas of neuroscience of memory enhancement and memory decoding.[5][37] He has written chapters on learning and memory for several popular textbooks.

Historiography[edit]

According to the Song dynasty book, Tongzhi, the Qian surname (Tsien；钱) descends from one of the legendary Five Emperors (Zhuanxu, mythological emperor of ancient China, Shang dynasty, Chinese: 商朝). Emperor Zhuanxu (Chinese: trad. 顓頊, simp. 颛顼, pinyin Zhuānxū), also known as Gaoyang (t 高陽, s 高阳, p Gāoyáng) who was the grandson of the first Chinese Emperor known as Yellow Emperor, ruled the Yellow River valley, the origin of China, in the second millennium BC from 2514 BC – 2436 BC (Early Bronze Age). During the Five Dynasties and Ten Kingdoms period (907-960), King Qian Liu and his descendants ruled the independent kingdom of Wuyue in south-eastern China, covering Shanghai, Jiangsu province, Zhejiang Province, and Fujian Province and regions. Joe Tsien was born in October of 1962 in the city of Wuxi and is the 36th generation of King Qian Liu descendants.

References[edit]

  1. ^ "聪明鼠"之父、美国华裔生物学家---钱卓(Sina article on Joe Z. Tsien)". March 2018.
  2. ^ a b c Joe Z. Tsien; et al. (1996). "Subregion- and cell type-restricted gene knockout in mouse brain". Cell. 87 (7): 1317–1326. doi:10.1016/S0092-8674(00)81826-7. PMID 8980237. S2CID 863399.
  3. ^ a b Tsien JZ. (2016). Cre-lox neurogenetics: 20 years of versatile applications in brain research and counting...Front. Genet. | doi:10.3389/fgene.2016.00019 http://journal.frontiersin.org/article/10.3389/fgene.2016.00019/abstract
  4. ^ a b Tang, YP; Shimizu, E; Dube, GR; Rampon, C; Kerchner, GA; Zhuo, M; Liu, G; Tsien, JZ (Sep 1999). "Genetic enhancement of learning and memory in mice". Nature. 401 (6748): 63–9. Bibcode:1999Natur.401...63T. doi:10.1038/43432. PMID 10485705. S2CID 481884.
  5. ^ a b Tsien, Building a Brainer Mouse. Scientific American, April, p62-68, 2000. http://www.bio.utexas.edu/courses/kalthoff/bio346/PDF/Readings/11Tsien%282000%29brainier.pdf
  6. ^ a b Tsien, JZ (2016). "Principles of Intelligence: On Evolutionary Logic of the Brains". Front. Syst. Neurosci. 9: 186. doi:10.3389/fnsys.2015.00186. PMC 4739135. PMID 26869892.
  7. ^ a b Tsien, JZ (Nov 2015). "A Postulate on the Brain's Basic Wiring Logic". Trends Neurosci. 38 (11): 669–71. doi:10.1016/j.tins.2015.09.002. PMC 4920130. PMID 26482260.
  8. ^ a b Kun Xie; et al. (2016). "Brain Computation Is Organized via Power-of-Two-Based Permutation Logic". Frontiers in Systems Neuroscience. 10: 95. doi:10.3389/fnsys.2016.00095. PMC 5108790. PMID 27895562.
  9. ^ Li, M; Tsien, JZ (2017). "Neural Code-Neural Self-information Theory on How Cell-Assembly Code Rises from Spike Time and Neuronal Variability". Front Cell Neurosci. 11: article 236. doi:10.3389/fncel.2017.00236. PMC 5582596. PMID 28912685.
  10. ^ Li, M; Xie, K; Kuang, H; Liu, J; Wang, D; Fox, GE; Shi, Z; Chen, L; Zhao, F; Mao, Y; Tsien, JZ (2018). "Neural Coding of Cell Assemblies via Spike-Timing Self-Information". Cereb Cortex. 28 (7): 2563–2576. doi:10.1093/cercor/bhy081. PMC 5998964. PMID 29688285.
  11. ^ Taniguchi H, He M, Wu P, Kim S, Paik R, Sugino K, Kvitsiani D, Fu Y, Lu J, Lin Y, Miyoshi G, Shima Y, Fishell G, Nelson SB, Huang ZJ (September 22, 2011). "A Resource of Cre Driver Lines for Genetic Targeting of GABAergic Neurons in Cerebral Cortex". Neuron. 71 (6): 995–1013. doi:10.1016/j.neuron.2011.07.026. PMC 3779648. PMID 21943598.
  12. ^ Cre lines characterized by the JAX Cre Resource (http://cre.jax.org/data.html )
  13. ^ Wade, Nicholas (1999-09-07). "SCIENTIST AT WORK: Joe Z. Tsien; Of Smart Mice and an Even Smarter Man". The New York Times. ISSN 0362-4331.
  14. ^ Lehrer, Jonah (2009-10-14). "Neuroscience: Small, furry … and smart". Nature News. 461 (7266): 862–864. doi:10.1038/461862a. PMID 19829344.
  15. ^ Cyranoski, David (2012). "Testing magnesium's brain-boosting effects". Nature. doi:10.1038/nature.2012.11665. S2CID 87848888.
  16. ^ Liu, G; Weinger, JG; Lu, ZL; Xue, F; Sadeghpour, S. (2015). "Efficacy and Safety of MMFS-01, a Synapse Density Enhancer, for Treating Cognitive Impairment in Elderly: A Randomized, Double-Blind, Placebo-Controlled Trial". J Alzheimers Dis. 49 (2015 Oct 27): 971–990. doi:10.3233/JAD-150538. PMC 4927823. PMID 26519439.
  17. ^ Lin, L; Osan, R; Shoham, S; Jin, W; Zuo, W; Tsien, JZ (Apr 2005). "Identification of network-level coding units for real-time representation of episodic experiences in the hippocampus". Proc Natl Acad Sci U S A. 102 (17): 6125–30. Bibcode:2005PNAS..102.6125L. doi:10.1073/pnas.0408233102. PMC 1087910. PMID 15833817.
  18. ^ Lin, L; Osan, R; Tsien, JZ (Jan 2006). "Organizing principles of real-time memory encoding: neural clique assemblies and universal neural codes". Trends Neurosci. 29 (1): 48–57. doi:10.1016/j.tins.2005.11.004. PMID 16325278. S2CID 53177323.
  19. ^ The Boston Globe: The mouse that remembered Terror of Disney ride sparks brain insight. http://archive.boston.com/yourlife/health/mental/articles/2005/04/12/the_mouse_that_remembered/?page=full
  20. ^ Lin, L; Chen, G; Kuang, H; Wang, D; Tsien, JZ (Apr 2007). "Neural encoding of the concept of nest in the mouse brain". Proc Natl Acad Sci U S A. 104 (14): 6066–71. Bibcode:2007PNAS..104.6066L. doi:10.1073/pnas.0701106104. PMC 1851617. PMID 17389405.
  21. ^ "Like Goldilocks, mice know a bed that's 'just right'".
  22. ^ Feng; et al. (2001). "Deficient neurogenesis in forebrain-specific presenilin-1 knockout mice is associated with reduced clearance of hippocampal memory traces". Neuron. 32 (5): 911–26. doi:10.1016/s0896-6273(01)00523-2. PMID 11738035. S2CID 17731574.
  23. ^ News by Nature magazine. http://www.nature.com/news/2001/011207/full/news0111213-2.html
  24. ^ "Neurogenesis—A Mechanism for Memory Storage, Clearance? | ALZFORUM".
  25. ^ Cao; et al. (Oct 2008). "Inducible and selective erasure of memories in the mouse brain via chemical-genetic manipulation". Neuron. 60 (2): 353–66. doi:10.1016/j.neuron.2008.08.027. PMC 2955977. PMID 18957226.
  26. ^ 'Eternal Sunshine' drug selectively erases memories by New Scientist. https://www.newscientist.com/article/dn15025-eternal-sunshine-drug-selectively-erases-memories
  27. ^ Wang; et al. (2011). "NMDA Receptors in Dopaminergic Neurons Are Crucial for Habit Learning". Neuron. 72 (6): 1055–1066. doi:10.1016/j.neuron.2011.10.019. PMC 3246213. PMID 22196339.
  28. ^ Wall Street Journal: How Habits Hold Us. http://archive.boston.com/yourlife/health/mental/articles/2005/04/12/the_mouse_that_remembered/?page=full
  29. ^ Video Abstract from NEURON magazine. https://www.youtube.com/watch?v=IVX69AXdYaw
  30. ^ "Brain Decoding Project".
  31. ^ Tsien, Joe Z.; Li, Meng; Osan, Remus; Chen, Guifen; Lin, Longian; Wang, Phillip Lei; Frey, Sabine; Frey, Julietta; Zhu, Dajiang; Liu, Tianming; Zhao, Fang; Kuang, Hui (2013). "On initial Brain Activity Mapping of episodic and semantic memory code in the hippocampus". Neurobiology of Learning and Memory. 105: 200–210. doi:10.1016/j.nlm.2013.06.019. PMC 3769419. PMID 23838072.
  32. ^ Ehlen F, Fromm O, Vonberg I, Klostermann F (2016). "Overcoming duality: the fused bousfieldian function for modeling word production in verbal fluency tasks". Psychonomic Bulletin & Review. 23 (5): 1354–1373. doi:10.3758/s13423-015-0987-0. PMID 26715583.
  33. ^ Fromm O, Klosterman F, Ehlen F (2020). "A Vector Space Model for Neural Network Functions: Inspirations From Similarities Between the Theory of Connectivity and the Logarithmic Time Course of Word Production". Frontiers in Systems Neuroscience. 14: 58. doi:10.3389/fnsys.2020.00058. PMC 7485382. PMID 32982704.
  34. ^ Tsien, Joe Z. (2016). "Principles of Intelligence: On Evolutionary Logic of the Brain". Frontiers in Systems Neuroscience. 9: 186. doi:10.3389/fnsys.2015.00186. ISSN 1662-5137. PMC 4739135. PMID 26869892.
  35. ^ Li, M; Tsien, JZ (2017). "Neural Code-Neural Self-information Theory on How Cell-Assembly Code Rises from Spike Time and Neuronal Variability". Front Cell Neurosci. 11: article 236. doi:10.3389/fncel.2017.00236. PMC 5582596. PMID 28912685.
  36. ^ Li, M; Xie, K; Kuang, H; Liu, J; Wang, D; Fox, GE; Shi, Z; Chen, L; Zhao, F; Mao, Y; Tsien, JZ (2018). "Neural Coding of Cell Assemblies via Spike-Timing Self-Information". Cereb Cortex. 28 (7): 2563–2576. doi:10.1093/cercor/bhy081. PMC 5998964. PMID 29688285.
  37. ^ Tsien, The memory code, Scientific American, July, 2007; http://redwood.psych.cornell.edu/courses/psych512fall07/papers/Tsien_memorycode_07.pdf

External links[edit]

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