Philip Sabes, PhD

SchoolUCSF School of Medicine
Address675 Nelson Rising Lane
San Francisco CA 94158
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    The ability to flexibly and adaptively integrate information from a variety of sources is a fundamental feature of brain function, from higher cognition to sensory and motor processing. Even a simple behavior such as reaching to a target relies on the integration of multimodal sensory signals and, moreover, exhibits rapid adaptation in response to changes in these signals. Our research uses reaching and similar goal-directed movements as a model system for understanding these abilities and their underlying neural mechanisms and, ultimately, for harnessing these abilities to repair brain dysfunction.

    Our lab employs a combination of complementary approaches:

    Cortical Physiology. Cutting-edge physiological techniques allow us to study and manipulate large-scale activity in sensorimotor cortex during behavior. We record from multiple 96-channel electrode cortical arrays, allowing us to study neural activity at the level of the population responses and across cortical circuits. We are also developing techniques to control the activity of cortical populations, both with patterned electrical stimulation across many electrodes with and patterned light stimulation in tissue expressing light-sensitive ion channels ("optogenetics").

    Computational and Theoretical Modeling. We use computational and theoretical models to link our understanding of brain and behavior. Two levels of modeling are used. We develop predictive models of behavior, typically cast in statistical or control-theoretical terms, in order to gain intuition about why the behavior is the way it is. We develop network models that approximation our behavioral models in order to gain intuition and about candidate neural mechanisms. These models generate testable hypotheses about the dynamics of cortical networks, and we use these models to design of our physiological experiments.

    Human Psychophysics and Physiology. With human psychophysics (or quantitative behavioral studies), we identify behavioral phenomena that illustrate important features of sensorimotor processing. The goal of this component our work is to find phenomena that are experimentally tractable for human and animals and are amenable to theoretical/computational modeling. We also have access to a variety of human neurophysiological tools, including functional magnetic resonance imaging (fMRI) and electro-corticography (ECoG).

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    Collapse Research Activities and Funding
    Learning in neural circuits: applied optogenetics in non-genetic models
    NIH/NINDS RC2NS069350Sep 30, 2009 - Aug 31, 2012
    Role: Co-Principal Investigator
    Variation as a Neural Code
    NIH/NIMH P50MH077970Sep 8, 2006 - Aug 31, 2012
    Role: Co-Investigator
    Visuomotor adaptation in reaching
    NIH/NEI R01EY015679Sep 1, 2004 - Jun 30, 2013
    Role: Principal Investigator

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    Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Researchers can login to make corrections and additions, or contact us for help.
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    1. Dadarlat M, Sabes P. Encoding and decoding of multi-channel ICMS in macaque somatosensory cortex. IEEE Trans Haptics. 2016 Oct 11. PMID: 27740497.
      View in: PubMed
    2. Yazdan-Shahmorad A, Diaz-Botia C, Hanson TL, Kharazia V, Ledochowitsch P, Maharbiz MM, Sabes P. A Large-Scale Interface for Optogenetic Stimulation and Recording in Nonhuman Primates. Neuron. 2016 Mar 2; 89(5):927-39. PMID: 26875625.
      View in: PubMed
    3. Makin JG, Dichter BK, Sabes PN. Learning to Estimate Dynamical State with Probabilistic Population Codes. PLoS Comput Biol. 2015 Nov; 11(11):e1004554. PMID: 26540152; PMCID: PMC4634970.
    4. Ledochowitsch P, Yazdan-Shahmorad A, Bouchard KE, Diaz-Botia C, Hanson TL, He JW, Seyboldt B, Olivero E, Phillips EA, Blanche TJ, Schreiner CE, Hasenstaub A, Chang EF, Sabes P, Maharbiz MM. Strategies for optical control and simultaneous electrical readout of extended cortical circuits. J Neurosci Methods. 2015 Dec 30; 256:220-31. PMID: 26296286.
      View in: PubMed
    5. Dadarlat MC, O'Doherty JE, Sabes PN. A learning-based approach to artificial sensory feedback leads to optimal integration. Nat Neurosci. 2015 Jan; 18(1):138-44. PMID: 25420067; PMCID: PMC4282864.
    6. Chaisanguanthum KS, Shen HH, Sabes PN. Motor variability arises from a slow random walk in neural state. J Neurosci. 2014 Sep 3; 34(36):12071-80. PMID: 25186752; PMCID: PMC4152607.
    7. Makin JG, Fellows MR, Sabes PN. Learning multisensory integration and coordinate transformation via density estimation. PLoS Comput Biol. 2013 Apr; 9(4):e1003035. PMID: 23637588; PMCID: PMC3630212.
    8. Verstynen T, Sabes PN. How each movement changes the next: an experimental and theoretical study of fast adaptive priors in reaching. J Neurosci. 2011 Jul 6; 31(27):10050-9. PMID: 21734297; PMCID: PMC3148097.
    9. McGuire LM, Sabes PN. Heterogeneous representations in the superior parietal lobule are common across reaches to visual and proprioceptive targets. J Neurosci. 2011 May 4; 31(18):6661-73. PMID: 21543595; PMCID: PMC3100795.
    10. Sabes PN. Sensory integration for reaching: models of optimality in the context of behavior and the underlying neural circuits. Prog Brain Res. 2011; 191:195-209. PMID: 21741553; PMCID: PMC3361512.
    11. McGuire LM, Sabes PN. Sensory transformations and the use of multiple reference frames for reach planning. Nat Neurosci. 2009 Aug; 12(8):1056-61. PMID: 19597495; PMCID: PMC2749235.
    12. Simani MC, McGuire LM, Sabes PN. Visual-shift adaptation is composed of separable sensory and task-dependent effects. J Neurophysiol. 2007 Nov; 98(5):2827-41. PMID: 17728389; PMCID: PMC2536598.
    13. Cheng S, Sabes PN. Calibration of visually guided reaching is driven by error-corrective learning and internal dynamics. J Neurophysiol. 2007 Apr; 97(4):3057-69. PMID: 17202230; PMCID: PMC2536620.
    14. Cheng S, Sabes PN. Modeling sensorimotor learning with linear dynamical systems. Neural Comput. 2006 Apr; 18(4):760-93. PMID: 16494690; PMCID: PMC2536592.
    15. Sober SJ, Sabes PN. Flexible strategies for sensory integration during motor planning. Nat Neurosci. 2005 Apr; 8(4):490-7. PMID: 15793578; PMCID: PMC2538489.
    16. Sober SJ, Sabes PN. Multisensory integration during motor planning. J Neurosci. 2003 Aug 6; 23(18):6982-92. PMID: 12904459.
      View in: PubMed
    17. Sabes PN, Breznen B, Andersen RA. Parietal representation of object-based saccades. J Neurophysiol. 2002 Oct; 88(4):1815-29. PMID: 12364508.
      View in: PubMed
    18. Sabes PN. The planning and control of reaching movements. Curr Opin Neurobiol. 2000 Dec; 10(6):740-6. PMID: 11240283.
      View in: PubMed
    19. Sabes PN, Jordan MI, Wolpert DM. The role of inertial sensitivity in motor planning. J Neurosci. 1998 Aug 1; 18(15):5948-57. PMID: 9671681.
      View in: PubMed
    20. Sabes PN, Jordan MI. Obstacle avoidance and a perturbation sensitivity model for motor planning. J Neurosci. 1997 Sep 15; 17(18):7119-28. PMID: 9278546.
      View in: PubMed
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