Samuel S. Wang
- B.S., Physics, California Institute of Technology
- Ph.D., Neurosciences, Stanford University
Research FocusInformation processing and learning in mammalian brains
The Wang laboratory does basic research in several areas: (1) information processing in the cerebellum, including its contributions to motor learning; (2) cerebellar roles in cognitive and affective function and autism spectrum disorder; (3) the improvement of tools for awake, in vivo optical imaging; and (4) synaptic learning rules throughout the brain.
We rely on advanced methods for the optical control and imaging of brain activity. Research interests are represented both by published papers and by more recent work. (For Sam Wang's public writings, see WelcomeToYourBrain.com. For Presidential polling meta-analysis see the Princeton Election Consortium.)
Optical imaging of the learning cerebellum in awake mice
Recent research has revealed a broad role for cerebellum as a general processor of unexpected events. We are among the first in the world to make extensive use of multiphoton fluorescence microscopy to probe what cerebellar circuits do during awake behavior. In a central recent finding, external and internal events are encoded by overlapping populations of Purkinje cells in a behavioral state-dependent manner, in the form of synchronous complex spiking. We have also made an analogous observation in granule cells and molecular layer interneurons. Recently, in collaboration with Javier Medina at the University of Pennsylvania, our laboratory implemented head-fixed recording methods for classical eyeblink conditioning in mice, achieving well-timed responses, learning over a time course of days, and consistency from animal to animal. Head-fixed recording is applicable to eyeblink conditioning and a variety of other behavioral and learning tasks.
Autism spectrum disorder
One of the most important unanswered questions in autism research today is the identity of the neural circuit(s) responsible for autistic behavior. We are interested in identifying brain dysfunction that is developmentally “upstream” of the many problems found in autistic brains. The cerebellum is not just a motor structure, but also has cognitive and affective roles. Accumulating evidence suggests that cerebellar abnormalities may play an ongoing role in, or even act as a developmental cause of the core social and cognitive deficits experienced by autistic persons. We are using mouse models to test two questions: (a) In mice with the same genetic disruptions as those found in autistic persons, is cerebellar function also disrupted? (b) Does disruption of cerebellar function during key periods of brain development lead to autistic-like behaviors?
Optical methods and genetically encodable calcium indicator proteins
Central tools in our laboratory include multiphoton in vivo imaging of neural activity, head-fixed awake recording, and the use of fluorescent calcium indicators. In a current project, we are at work on improving GECIs while avoiding the de-optimization of existing beneficial features. In the case of Green fluorescent protein / Calmodulin protein sensor (GCaMP), such features include low degradation, high per-molecule brightness, and large fluorescence changes. We making two improvements. First, we seek to engineer KD by making targeted changes that vary the affinity but preserve probe performance thus allowing a larger range of activity levels (i.e. firing rates) to be monitored accurately. Second, we are engineering faster kinetics to better track variations in calcium. Dendritic calcium signals can rise in 1 ms and fall in 10-100 ms. Faster variants of GCaMP will allow many laboratories, including our own, to track neuronal signaling events with unprecedented precision.
Synaptic learning rules
In addition, in past years the laboratory has identified fundamental principles by which molecular signaling mechanisms shape learning rules. For example, we have found that calcium signaling mechanisms drive the switchlike strengthening and weakening of single synapses. The likelihood and direction of this change is closely dependent on the precise occurrence of certain presynaptic and postsynaptic spike patterns. In the case of cerebellum, we have identified a learning rule that favors parallel fiber activity that leads the complex spike by tens to hundreds of milliseconds, consistent with order-dependent learning seen in vivo.
In vitro, the laboratory studies how single-neuron function is modified by dynamic changes in neural activity such as complex input patterns of neurotransmitters and neuromodulators. Rapid barrages of dendritic input activation may alter function in a fraction of a second, thus altering circuit function and driving synaptic plasticity. These questions are being pursued using uncaging methods, which allow neurotransmitters such as glutamate to be generated in femtoliter (1 cubic micron) volumes within a millisecond. With rapid beamsteering technology we can uncage at tens of thousands of locations per second. Projects focus on large neurons such as cerebellar Purkinje neurons and pyramidal neurons of the neocortex and hippocampus, all of which receive a large convergence of synaptic input.
Brain scaling and evolution
We use comparative biophysical principles to infer functional principles of brain architecture. For example, the mammalian neocortex (also known as cerebral cortex) shows regularities of structure that suggest that brain structure may be subject to universal design constraints. From shrews to whales, mammalian brains vary over 100,000-fold in volume. Over this range large brains are more folded than small brains: the surface area of the cerebral cortex follows a power law relative to cortical volume greater than simple geometry would predict. We want to understand how these power laws are constructed from the cellular architecture of the cortex. Using electron microscopy, we find that on average, axons are wider in large brains than in small brains. The space demanded by these axons is sufficient to account for the increased folding seen in large brains. This widening of axons may be driven by an evolutionary need to preserve the time it takes for a nerve impulse to cross the brain.
Publisher Correction: SLEAP: A deep learning system for multi-animal pose tracking. Nat Methods. 2022 ;19(5):628. .
Transcriptomic mapping uncovers Purkinje neuron plasticity driving learning. Nature. 2022 ;605(7911):722-727. .
Deep phenotyping reveals movement phenotypes in mouse neurodevelopmental models. Mol Autism. 2022 ;13(1):12. .
Automated high-throughput mouse transsynaptic viral tracing using iDISCO+ tissue clearing, light-sheet microscopy, and BrainPipe. STAR Protoc. 2022 ;3(2):101289. .
A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning. J Vis Exp. 2022 ;(179). .
SLEAP: A deep learning system for multi-animal pose tracking. Nat Methods. 2022 ;19(4):486-495. .
Homologous organization of cerebellar pathways to sensory, motor, and associative forebrain. Cell Rep. 2021 ;36(12):109721. .
A systems framework for remedying dysfunction in US democracy. Proc Natl Acad Sci U S A. 2021 ;118(50). .
The p75NTR Influences Cerebellar Circuit Development and Adult Behavior via Regulation of Cell Cycle Duration of Granule Cell Progenitors. J Neurosci. 2019 ;39(46):9119-9129. .
Cerebellar disruption impairs working memory during evidence accumulation. Nat Commun. 2019 ;10(1):3128. .
Publisher Correction: Stability, affinity, and chromatic variants of the glutamate sensor iGluSnFR. Nat Methods. 2019 ;16(2):206. .
Author Correction: Stability, affinity, and chromatic variants of the glutamate sensor iGluSnFR. Nat Methods. 2019 ;16(4):351. .
Stability, affinity, and chromatic variants of the glutamate sensor iGluSnFR. Nat Methods. 2018 ;15(11):936-939. .
Cerebellar involvement in an evidence-accumulation decision-making task. Elife. 2018 ;7. .
Automated gesture tracking in head-fixed mice. J Neurosci Methods. 2018 ;300:184-195. .
Cerebellar granule cells acquire a widespread predictive feedback signal during motor learning. Nat Neurosci. 2017 ;20(5):727-734. .
Cerebellar associative sensory learning defects in five mouse autism models. Elife. 2015 ;4:e06085. .
Fast calcium sensor proteins for monitoring neural activity. Neurophotonics. 2014 ;1(2):025008. .
Cerebellar plasticity and motor learning deficits in a copy-number variation mouse model of autism. Nat Commun. 2014 ;5:5586. .
Gap junctions in the ventral hippocampal-medial prefrontal pathway are involved in anxiety regulation. J Neurosci. 2014 ;34(47):15679-88. .
The cerebellum, sensitive periods, and autism. Neuron. 2014 ;83(3):518-32. .
Sam Wang is professor of molecular biology and neuroscience at Princeton University. His work focuses on the neurobiology of learning, at levels ranging from single synapses to the whole brain. Dr. Wang’s research places special emphasis on the cerebellum, a brain region generally associated with the coordination of muscle movements. He is particularly curious about the cerebellum’s role in cognition and social thought processes, and he is using neural imaging of this part of the brain to search for clues to the causes of autism, a major concern of his laboratory.
An alumnus of the California Institute of Technology, where he received a B.S. with honor in physics, Dr. Wang went on to earn a Ph.D. in neuroscience from the Stanford University School of Medicine in 1994. He conducted postdoctoral research at Duke University Medical Center and then Bell Labs Lucent Technologies. In the mid-1990s, he also worked on science and education policy for the U.S. Senate Committee on Labor and Human Resources. Dr. Wang joined the Princeton University faculty in 2000.
The recipient of a 2004 National Science Foundation Young Investigator Award, Dr. Wang has also been an Alfred P. Sloan Fellow and a W.M. Keck Foundation Distinguished Young Investigator. Last year, he received a McKnight Technological Innovations in Neuroscience Award.
Dr. Wang is also noted for developing statistical methods to analyze U.S. presidential election polls with unusually high accuracy. His research has been featured by the New York Times, the Wall Street Journal, and National Public Radio.
Dr. Wang’s first book, Welcome to Your Brain: Why You Lose Your Car Keys But Never Forget How to Drive and Other Puzzles of Everyday Life, published in 2008, was named Young Adult Science Book of the Year by the American Association for the Advancement of Science. In 2011, he published Welcome to Your Child’s Brain: How the Mind Grows from Conception to College, which is available in 15 international translations.
- McKnight Technological Innovations in Neuroscience Award, The McKnight Foundation
- Appointed to the Rita Allen Board of Directors, Rita Allen Foundation