Professional Education

  • Bachelor of Arts, Cornell University, Neurobiology, Physics (2011)
  • Doctor of Philosophy, Columbia University (2019)
  • PhD, Columbia University, Neurobiology (2019)
  • BA, Cornell University, Physics, Biology (2011)

Stanford Advisors

All Publications

  • Structural basis for ion selectivity in potassium-selective channelrhodopsins. Cell Tajima, S., Kim, Y. S., Fukuda, M., Jo, Y., Wang, P. Y., Paggi, J. M., Inoue, M., Byrne, E. F., Kishi, K. E., Nakamura, S., Ramakrishnan, C., Takaramoto, S., Nagata, T., Konno, M., Sugiura, M., Katayama, K., Matsui, T. E., Yamashita, K., Kim, S., Ikeda, H., Kim, J., Kandori, H., Dror, R. O., Inoue, K., Deisseroth, K., Kato, H. E. 2023


    KCR channelrhodopsins (K+-selective light-gated ion channels) have received attention as potential inhibitory optogenetic tools but more broadly pose a fundamental mystery regarding how their K+ selectivity is achieved. Here, we present 2.5-2.7 Å cryo-electron microscopy structures of HcKCR1 and HcKCR2 and of a structure-guided mutant with enhanced K+ selectivity. Structural, electrophysiological, computational, spectroscopic, and biochemical analyses reveal a distinctive mechanism for K+ selectivity; rather than forming the symmetrical filter of canonical K+ channels achieving both selectivity and dehydration, instead, three extracellular-vestibule residues within each monomer form a flexible asymmetric selectivity gate, while a distinct dehydration pathway extends intracellularly. Structural comparisons reveal a retinal-binding pocket that induces retinal rotation (accounting for HcKCR1/HcKCR2 spectral differences), and design of corresponding KCR variants with increased K+ selectivity (KALI-1/KALI-2) provides key advantages for optogenetic inhibition in vitro and in vivo. Thus, discovery of a mechanism for ion-channel K+ selectivity also provides a framework for next-generation optogenetics.

    View details for DOI 10.1016/j.cell.2023.08.009

    View details for PubMedID 37652010

  • All-optical physiology resolves a synaptic basis for behavioral timescale plasticity. Cell Fan, L. Z., Kim, D. K., Jennings, J. H., Tian, H., Wang, P. Y., Ramakrishnan, C., Randles, S., Sun, Y., Thadhani, E., Kim, Y. S., Quirin, S., Giocomo, L., Cohen, A. E., Deisseroth, K. 2023


    Learning has been associated with modifications of synaptic and circuit properties, but the precise changes storing information in mammals have remained largely unclear. We combined genetically targeted voltage imaging with targeted optogenetic activation and silencing of pre- and post-synaptic neurons to study the mechanisms underlying hippocampal behavioral timescale plasticity. In mice navigating a virtual-reality environment, targeted optogenetic activation of individual CA1 cells at specific places induced stable representations of these places in the targeted cells. Optical elicitation, recording, and modulation of synaptic transmission in behaving mice revealed that activity in presynaptic CA2/3 cells was required for the induction of plasticity in CA1 and, furthermore, that during induction of these place fields in single CA1 cells, synaptic input from CA2/3 onto these same cells was potentiated. These results reveal synaptic implementation of hippocampal behavioral timescale plasticity and define a methodology to resolve synaptic plasticity during learning and memory in behaving mammals.

    View details for DOI 10.1016/j.cell.2022.12.035

    View details for PubMedID 36669484

  • Structural basis for channel conduction in the pump-like channelrhodopsin ChRmine. Cell Kishi, K. E., Kim, Y. S., Fukuda, M., Inoue, M., Kusakizako, T., Wang, P. Y., Ramakrishnan, C., Byrne, E. F., Thadhani, E., Paggi, J. M., Matsui, T. E., Yamashita, K., Nagata, T., Konno, M., Quirin, S., Lo, M., Benster, T., Uemura, T., Liu, K., Shibata, M., Nomura, N., Iwata, S., Nureki, O., Dror, R. O., Inoue, K., Deisseroth, K., Kato, H. E. 1800


    ChRmine, a recently discovered pump-like cation-conducting channelrhodopsin, exhibits puzzling properties (large photocurrents, red-shifted spectrum, and extreme light sensitivity) that have created new opportunities in optogenetics. ChRmine and its homologs function as ion channels but, by primary sequence, more closely resemble ion pump rhodopsins; mechanisms for passive channel conduction in this family have remained mysterious. Here, we present the 2.0A resolution cryo-EM structure of ChRmine, revealing architectural features atypical for channelrhodopsins: trimeric assembly, a short transmembrane-helix 3, a twisting extracellular-loop 1, large vestibules within the monomer, and an opening at the trimer interface. We applied this structure to design three proteins (rsChRmine and hsChRmine, conferring further red-shifted and high-speed properties, respectively, and frChRmine, combining faster and more red-shifted performance) suitable for fundamental neuroscience opportunities. These results illuminate the conduction and gating of pump-like channelrhodopsins and point the way toward further structure-guided creation of channelrhodopsins for applications across biology.

    View details for DOI 10.1016/j.cell.2022.01.007

    View details for PubMedID 35114111

  • Evolving the olfactory system with machine learning NEURON Wang, P. Y., Sun, Y., Axel, R., Abbott, L. F., Yang, G. 2021; 109 (23): 3879-+


    The convergent evolution of the fly and mouse olfactory system led us to ask whether the anatomic connectivity and functional logic of olfactory circuits would evolve in artificial neural networks trained to perform olfactory tasks. Artificial networks trained to classify odor identity recapitulate the connectivity inherent in the olfactory system. Input units are driven by a single receptor type, and units driven by the same receptor converge to form a glomerulus. Glomeruli exhibit sparse, unstructured connectivity onto a larger expansion layer of Kenyon cells. When trained to both classify odor identity and to impart innate valence onto odors, the network develops independent pathways for identity and valence classification. Thus, the defining features of fly and mouse olfactory systems also evolved in artificial neural networks trained to perform olfactory tasks. This implies that convergent evolution reflects an underlying logic rather than shared developmental principles.

    View details for DOI 10.1016/j.neuron.2021.09.010

    View details for Web of Science ID 000726750500004

    View details for PubMedID 34619093

  • Transient and Persistent Representations of Odor Value in Prefrontal Cortex NEURON Wang, P. Y., Boboila, C., Chin, M., Higashi-Howard, A., Shamash, P., Wu, Z., Stein, N. P., Abbott, L. F., Axel, R. 2020; 108 (1): 209-+


    The representation of odor in olfactory cortex (piriform) is distributive and unstructured and can only be afforded behavioral significance upon learning. We performed 2-photon imaging to examine the representation of odors in piriform and in two downstream areas, the orbitofrontal cortex (OFC) and the medial prefrontal cortex (mPFC), as mice learned olfactory associations. In piriform, we observed that odor responses were largely unchanged during learning. In OFC, 30% of the neurons acquired robust responses to conditioned stimuli (CS+) after learning, and these responses were gated by internal state and task context. Moreover, direct projections from piriform to OFC can be entrained to elicit learned olfactory behavior. CS+ responses in OFC diminished with continued training, whereas persistent representations of both CS+ and CS- odors emerged in mPFC. Optogenetic silencing indicates that these two brain structures function sequentially to consolidate the learning of appetitive associations.

    View details for DOI 10.1016/j.neuron.2020.07.033

    View details for Web of Science ID 000579703400017

    View details for PubMedID 32827456

    View details for PubMedCentralID PMC7886003

  • Evaluating Attribution for Graph Neural Networks NeurIPS Sanchez-Lengeling, B., Wei, J., Lee, B., Reif, E., Wang, P., Qian, W. W., McCloskey, K., Colwell, L., Wiltschko, A. 2020
  • Emergence of functional and structural properties of the head direction system ICLR Cueva*, C. J., Wang*, P. Y., Chin*, M., Wei, X. 2019
  • Evolving the olfactory system NeurIPS 2019 Workshop Neuro AI Yang*, G. R., Wang*, P. Y., Sun, Y., Litwin-Kumar, A., Axel, R., Abbott, L. 2019