Shanshan Qin

Shanshan Qin

Associate Research Scientist

Flatiron Institute

I am an Associate Research Scientist at the Center for Computational Neuroscience, Flatiron Institute. Driven by a broad interest in theoretical and computational neuroscience, my research seeks to understand the fundamental principles driving neural computation and cognition.

Current Research Focus:

  • Sensory processing and representation: How does the brain efficiently perceive and encode the external world?
  • Memory organization and update: How does the brain store and dynamically update memories to support adaptive behavior and lifelong learning?
  • Algorithmic foundations of computation: What are the underlying algorithms the brain uses to solve complex problems?

Background:

Prior to joining Flatiron Institute, I held a postdoctoral fellowship at Harvard’s John A. Paulson School of Engineering and Applied Sciences where I worked with Cengiz Pehlevan. I earned my PhD in condensed matter physics from Peking University where I was advised by Chao Tang and Yuhai Tu at the Center for Quantitative Biology. Before that, I pursued my undergraduate studies in physics at Central China Normal University in Wuhan.

I will join the Institute of Natural Sciences at Shanghai Jiao Tong University as a tenure-track Associate Professor starting from January 2025.

If you or someone you know is interested in working with me, please drop me an email: qinss.pku(at)gmail.com.


Interests
  • Theoretical and Computational Neuroscience
  • Quantiative and Systems Biology
  • Physics of life
Education
  • Ph.D in Condensed Matter Physics, 2019

    Peking University, Beijing, China

  • BSc in physics, 2012

    Central China Normal University, Wuhan, China

Publications

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Featured Publications (top 5):

(2023). Coordinated drift of receptive fields in Hebbian/anti-Hebbian network models during noisy representation learning. Nature Neuroscience.

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(2022). Drifting neuronal representations: Bug or feature?. Biological Cybernetics.

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(2021). Contrastive Similarity Matching for Supervised Learning. Neural Computation.

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(2019). Optimal compressed sensing strategies for an array of nonlinear olfactory receptor neurons with and without spontaneous activity. Proceedings of the National Academy of Sciences.

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Latest Publications (last 5):

(2024). Subcellular proteomics and iPSC modeling uncover reversible mechanisms of axonal pathology in Alzheimertextquoterights disease. bioRxiv.

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(2023). Coordinated drift of receptive fields in Hebbian/anti-Hebbian network models during noisy representation learning. Nature Neuroscience.

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(2022). Drifting neuronal representations: Bug or feature?. Biological Cybernetics.

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(2022). Cell-to-cell variability in inducible Caspase9-mediated cell death. Cell Death & Disease.

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Projects

Internal state dependent olfactory behavior
Animal exhibit distinct behavior that dependent their internal states even for the same sensory input. Drosophila larva responds different to odors when starved compared with fed. This internal state dependent oflactory behavior is controled by a topdown feedback to antennal lobe circuit.
Biologically plausible supervised learning algorithm
Biologically plausible networks models with noisy synaptic update explain the widely observed representational drift.
Early-warning signals of critical transition - effect of extrinsic noise
We extend previous theory to investigate how the interplay of extrinsic noise and intrinsic noise affects early-warning signals near critical transitions.
Optimal nonlinear compressed sensing in olfactory periphery
To achieve efficient coding of odor information in an array of nonlinear olfactory receptors, the odor-receptor sensitivity matrix must be sparse. This sparsity depends on the statistics of environmental odors. We used analytical calclation and extensive numerical simulation to study the optimal sensitivity matrix for recptors with and without spontaneous (background) activity.
Representational drift
Biologically plausible networks models with noisy synaptic update explain the widely observed representational drift.
Short-term plasticity in early olfactory information processing
We built a circuit model of the first olfactory information processing center of fruit fly, which incorporates key features of neuron-neuron interactions such as short-term plasticity and presynaptic inhibition.
Odor representation in C. elegans
Multineuronal imaging of the odor responses in C. elegans and decoding analysis revealed a distinct organization compared with insects and mamammlians.

Talks

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Latest Talks:

[talk] (2024) "Temporal filters of neurons as low-rank forecasters", Center for Computational Neuroscience Retreat, Flatiron Institute, Mohonk (US), Jun 12, 2024 — Jun 14, 2024

[talk] (2024) "Brain in motion: causes and dynamics of drifting neural representations", Havard University Center for Brain Science Neurolunch Seminar, Cambridge (USA), Mar 22, 2024 —

[invited talk] (2024) "Brain in motion: causes and dynamics of drifting neural representations", Department of Neuroscience Special Seminar, the Scripps Research Institute., CA, La Jolla (USA), Mar 16, 2024 — Mar 17, 2024

[invited webinar] (2024) "Brain in motion: causes and dynamics of drifting neural representations", Institute of Neuroscience Special Seminar, Chinese Academy of Sciences., Shanghai (China), Feb 24, 2024 12:00 AM — Mar 1, 2024 12:00 AM

[invited webinar] (2024) "Dynamics and structure of drifting neural representations", VIB-KU Leuven Center for Brain Science Special Seminar, (Belgium), Jan 16, 2024 12:00 AM — Jan 17, 2024 12:00 AM

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