Imaging How A Neuron Computes

Imaging How A Neuron Computes

A Multidisciplinary University Research Initiative (MURI)

Funded by the Army Research Office

  •         The primary goal of this project is to understand how two different types of cortical cells, excitatory pyramidal cells and inhibitory GABAergic interneurons, integrate spatio-temporal patterns of inputs. To determine this, one must first understand: (i) their connectivity diagram (i.e., which neurons are their presynaptic partners), (ii) the position of these inputs on their dendritic tree (since the spatio-temporal input pattern will determine the computation) and (iii) the synaptic strength of each of these inputs. In this MURI, we will employ several novel imaging methods to answer these three challenging questions, which are among the most central in neuroscience. We will perform this work using a variety of biological samples, from primary neuronal cell cultures, to brain slices, to in vivo mouse preparations. Our end goal is to generate computational models of pyramidal cells and of neocortical interneurons with which we can predict their response to any arbitrary pattern of inputs. Successful completion of this project will result in a comprehensive understanding of dendritic integration.

Collaborating Labs

Columbia University

Harvard University


Princeton University

MURI Funded Publications

  • Packer, A.M., Peterka, D.S., Hirtz, J.J., Prakash, R., Deisseroth, K., and Yuste, R. (2012). Two-photon optogenetics of dendritic spines and neural circuits. Nature Methods 9: 1202-1205.  Full Text  Supplementary Information
  • Pakman, A. and Paninski, L. (2013). Exact Hamiltonian Monte Carlo for Truncated Multivariate Gaussians. Journal of Computational and Graphical Statistics. In press.  Full Text
  • Chen, E.H., Gaathon, O., Trusheim, M.E., and Englund, D. (2013). Wide-Field Multispectral Super-Resolution Imaging Using Spin-Dependent Fluorescence in Nanodiamonds. Nano Letters. Article ASAP.  Full Text  Supplementary Information
  • Sippy, T. and Yuste, R. (2013). Decorrelating actions of inhibition in neocortical networks. Journal of Neuroscience. 33(23):9813-9830.  Full Text
  • Araya, R., Andino-Pavlovsky, V., and Yuste, R., Etchenique, R. (2013). Two-Photon Optical Interrogation of Dendritic Spines with Caged Dopamine. ACS Chemical Neuroscience. In Press.  Full Text
  • Yuste, R. (2013) Electrical Compartmentalization in Dendritic Spines. Annual Review of Neuroscience. 36: 429-444.  Full Text
  • Quirin, S., Peterka, D.S., and Yuste, R. (2013). Instantaneous three-dimensional sensing using spatial light modulator illumination with extended depth of field imaging . Optics Express. 21: 16007-16021.  Full Text
  • Pakman, A., Huggins, J., Smith, C., and Paninski, L. (2013). Fast penalized state-space methods for inferring dendritic synaptic connectivity. Journal of Computational Neuroscience. In press.  Full Text
  • Zhang, L., Fang X., Chen Z., Zhu X., and Min W. (2013). Bioluminescence Assisted Switching and Fluorescence Imaging (BASFI). Biophysical Chemistry and Biomolecules. 4:3897-3902. Full Text
  • Shababo, B., Paige, B., Pakman, A., and Paniniski, L. (2013). Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits NIPS  Full Text
  • Pnevmatikakis, E. and Paninski, L. (2013). Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions. NIPS  Full Text
  • Pfau, D., Pnevmatikakis, E. and Paninski, L. (2013). Robust learning of low-dimensional dynamics from large neural ensembles. NIPS.  Full Text
  • Pakman, A. and Paninski, L. (2013). Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions. NIPS.  Full Text
  • Keshri, S., Pnevmatikakis, E., Pakman, A., and Paninski, L. (2013). A shotgun sampling method for the common input problem in neural connectivity inference. arXiv  Full Text
  • Pnevmatikakis, E., Merel, J., Pakman, A., and Paninski, L. (2014). Bayesian spike inference from calcium imaging data. Asilomar Conf. on Signals, Systems, and Computers.  Full Text
  • Field, R.M., Realov, S., and Shepard, K. (2014). A 100-fps, Time-Correlated Single-Photon-Counting-Based Fluorescence-Lifetime Imager in 130-nm CMOS. IEEE Journal of Solid-State Circuits vol.49, no.4. Advanced Online Version.  Full Text
  • Zhu, X., Zhang, L., Kao, Y.-T., Xu, F., and Min,W. (2014). A tunable fluorescent timer method for imaging spatial-temporal protein dynamics using light-driven photoconvertible protein. J. Biophotonics  Full Text
  • Zhang, L., Xu, F., Chen, Z., Zhu, X., and Min,W. (2013). Bioluminescence assisted switching and fluorescence imaging (BASFI) J. Phys. Chem. Lett. 4, 3897.  Full Text
  • Wei, L., Hu, F., Shen, Y., Chen, Z., Yu Y., Lin, C., Wang, M., and Min, W. (2014). Live-cell imaging of alkyne-tagged small biomolecules by stimulated Raman Scattering. Nature Methods 11, 410.  Full Text
  • Santana, R., McGarry, L.M., Bielza, C., Larrañaga, P., and Yuste, R. (2013). Classification of neocortical interneurons using affinity propagation. Front. Neural Circuits 7:185.  Full Text
  • Karnani, M.M., Agetsuma, M., and Yuste, R. (2014). A blanket of inhibition: functional inferences from dense inhibitory connectivity. Current Opinion in Neurobiology 26:96-102.  Full Text
  • Quirin, S., Jackson, J., Peterka, D.S., and Yuste, R. (2014). Simultaneous imaging of neural activity in three dimensions. Front. Neural Circuits 8:29.  Full Text
  • Izquierdo-Serra, M., Gascón-Moya, M., Hirtz, J.J., Pittolo, S., Poskanzer, K., Ferrer, É., Alibés, R., Busqué, F., Yuste, R., Hernando, J., and Gorostiza, P. (2014). Two-Photon Neuronal and Astrocytic Stimulation with Azobenzene-Based Photoswitches. Journ. Am. Chem. Soc. 136:8693-8701.  Full Text
  • Araya, R., Vogels, T.P., and Yuste, R. (2014). Activity-dependent dendritic spine neck changes are correlated with synaptic strength. Proc. Natl. Acad. Sci. USA  Full Text

MURI Related Publications

  • Alivisatos, A.P., Chun, M., Church, G.M., Deisseroth, K., Donoghue, J.P., Greenspan, R.J., McEuen, P.L., Roukes, M.L., Sejnowski, T.J., Weiss, P.S., and Yuste, R. (2013). The Brain Activity Map. Science. 339:1284-5. Full Text
  • Tapia, J.C., Wylie, J.D., Kasthuri, N., Hayworth, K., Schalek, R., Berger, D., Guatimosim, C., Seung, H.S. & Lichtman, J.W. (2012). Pervasive synaptic branch removal in the mammalian neuromuscular system at birth. Neuron. 74:816–829.  Full Text
  • Lichtman, J.W. and W. Denk. (2011) The big and the small: challenges of imaging the brain's circuits. Science. 334(6056):618-23.  Full Text
  • Dawen Cai, Kimberly B. Cohen, Tuanlian Luo, Jeff W. Lichtman, and Joshua R. Sanes. (2013). New Tools for the Brainbow Toolbox. Nature Methods. [in process]
  • Xu, K., Zhong, G. Zhuang, X. (2011) Actin, Spectrin, and Associated Proteins Form a Periodic Cytoskeletal Structure in Axons Science. 339:452-56.  Full Text
  • Alvisatos, A.P., Andrews, A.M., Boyden, E.S., Chun, M., Church, K., Deisseroth, J., Donoghue, P., Fraser, S.E., Lippincott-Schwartz, J., Looger, L.L., Masmanidis, S., McEuen, P.L., Nurmikko, A.V., Park, H., Peterka, D.S., Reid, C., Roukes, M.L., Scherer, A., Schnitzer M., Sejnowski, T.J., Shepard, K.L., Tsao, D., Turrigiano, G., Weiss, P.S., Xu, C., Yuste, R., Zhuang, X. (2013). Nanotoools for neuroscience and brain activity mapping. ACS Nano 7:1850-1866.  Full Text
  • Vaughan, J., Jia, S., and Zhuang, X. (2012). Ultra-bright Photoactivatable Fluorophores Created by Reductive Caging. Nature Methods 9:1181-1184.  Full Text
  • Jia, S., Vaughan, C., and Zhuang, X. (2014). Isotropic 3D super-resolution imaging with a self-bending point-spread function. Nature Photonics 8:302-306.  Full Text
  • Vaughan, J.C., Dempsey G.T., Sun, E., and Zhuang, X. (2013). Phospine Quenching of Cyanin Dyes as a Versatile Tool for Fluorescence Microscopy. J. Am. Chem. Soc. 135:1197-1200.  Full Text

Project Meeting Schedule

  • Meetings are held at 10 AM in 703 NWC on Columbia's campus. To teleconference in to meetings, please contact (see bottom of page).


  • 3/01     Yuste Lab:   Masayuki Sakamoto, "Voltage Imaging of Dendritic Spines"  Slides
  • 4/05     Hillman Lab: Dr. Hillman, "Methods for fast, 3D neuronal imaging in-vivo"
  • 5/03     Min Lab: Dr. Min, "Seeing the invisible: discovering new contrasts of optical bio-imaging"
  • 6/07     Owen Lab:   Abe Wolcott, "The best of both worlds: bulk properties of diamond at the nanoscale"  Slides
  • 7/12     Englund Lab: "Super-Resolution Imaging and Precision Sensing with Nitrogen Vacancy Color Centers in Diamond"
  • 9/06     Sahin Lab: "Nanomechanical tools for probing synapses"
  • 10/04    Guest Speaker, Dr. Shalom Wind: "Engineering Site-Selective Interactions at the Molecular Scale"
  • 11/01    Guest Speaker, Dr. Dalibor Sanes: "Chemical Approaches to Selective Neuronal and Synaptic Markers and Sensors"
  • 12/13    ARO Visit, All Groups Present, 10am - 5pm
  • 2014

  • 2/07    Shepard Lab
  • 4/04    Englud Lab: "Super-Resolution Imaging and Precision Sensing with Fluorescent Diamond Nanocrystals"
  • 5/02    Paninski Lab: "Progress in Spike Extraction From Calcium Recordings, Network Inference, and Factor Analysis of Multineuronal Data"
  • 6/06    Yuste Lab: "Visual stimuli recruit intrinsically-generated cortical ensembles
  • 9/12    Sahin Lab: "Probing synaptic strength via nano-electromechanical coupling”
  • 10/03    Min Lab: "Developing new chemical imaging tools for neuroscience"
  • 12/05    ARO Visit, All Groups Present
  • 2015

  • 1/23    Seung Lab: "ZNN - A CPU Implementation of Convolutional Neural Networks for Deep Learning"
  • 2/20    Hillman Lab: TBD
  • 3/13    Lichtman Lab: "New Circuit Training Approaches and Results"
  • 4/03    Zhuang Lab: TBD
  • 5/01    Shepard Lab: "CMOS-based electrophysiology"
  • 6/05    Owen Lab: “Imaging voltage with luminescent nanocrystals”
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