
Peter Wang
Postdoctoral Scholar, Bioengineering
Professional Education
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PhD, Columbia University, Neurobiology (2019)
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BA, Cornell University, Physics, Biology (2011)
All Publications
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Structural basis for channel conduction in the pump-like channelrhodopsin ChRmine.
Cell
1800
Abstract
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
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Evolving the olfactory system with machine learning
NEURON
2021; 109 (23): 3879-+
Abstract
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
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Transient and Persistent Representations of Odor Value in Prefrontal Cortex
NEURON
2020; 108 (1): 209-+
Abstract
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 2020
- Emergence of functional and structural properties of the head direction system ICLR 2019
- Evolving the olfactory system NeurIPS 2019 Workshop Neuro AI 2019