Shanshan Qin

Shanshan Qin

Associated Research Scientist

Flatiron Institution

About me

I am an Associated Research Scientist at the Center for Computational Neuroscience at Flatiron Institute, Simons Foundation. 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, 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.

Interests

  • Theoretical and Computational Neuroscience
  • Quantiative and Systems Biology
  • Physics of life

Education

  • PhD in Condensed Matter Physics, 2019

    Peking University, Beijing, China

  • BS in physics, 2012

    Central China Normal University, Wuhan, China

Projects

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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.

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.

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.

Publications

Coordinated drift of receptive fields in Hebbian/anti-Hebbian network models during noisy representation learning

Recent experiments have revealed that neural population codes in many brain areas continuously change even when animals have fully …

Drifting neuronal representations: Bug or feature?

The brain displays a remarkable ability to sustain stable memories, allowing animals to execute precise behaviors or recall stimulus …

Functional imaging and quantification of multi-neuronal olfactory responses in C. elegans

Many animals perceive odorant molecules by collecting information from ensembles of olfactory neurons. Each neuron employs receptors …

Cell-to-cell variability in inducible Caspase9-mediated cell death

iCasp9 suicide gene has been widely used as a promising killing strategy in various cell therapies. However, different cells show …

Short-Term Plasticity Regulates Both Divisive Normalization and Adaptive Responses in Drosophila Olfactory System

In Drosophila, olfactory information received by olfactory receptor neurons (ORNs) is first processed by an incoherent feed forward …

Contrastive Similarity Matching for Supervised Learning

We propose a novel biologically plausible solution to the credit assignment problem motivated by observations in the ventral visual …

Internal state configures olfactory behavior and early sensory processing in Drosophila larvae

The first olfactory processing center in the larval Drosophila brain uses information about feeding state to shape behavior. Animals …

Optimal compressed sensing strategies for an array of nonlinear olfactory receptor neurons with and without spontaneous activity

There are numerous different odorant molecules in nature but only a relatively small number of olfactory receptor neurons (ORNs) in …

A systematic study of the determinants of protein abundance memory in cell lineage

Proteins are essential players of life activities. Intracellular protein levels directly affect cellular functions and cell fate. Upon …

Early-warning signals of critical transition: Effect of extrinsic noise

Complex dynamical systems often have tipping points and exhibit catastrophic regime shift. Despite the notorious difficulty of …

Network Motifs Capable of Decoding Transcription Factor Dynamics

Transcription factors (TFs) can encode the information of upstream signal in terms of its temporal activation dynamics. However, it …

Odor-evoked inhibition of olfactory sensory neurons drives olfactory perception in Drosophila

Inhibitory response occurs throughout the nervous system, including the peripheral olfactory system. While odor-evoked excitation in …

Large-Scale Porous Hematite Nanorod Arrays: Direct Growth on Titanium Foil and Reversible Lithium Storage

Porous single-crystalline hematite (α-Fe2O3) nanorod array has been synthesized on large-area Ti foil via a facile hydrothermal method …

Experience

 
 
 
 
 

Associated Research Scientist

Flatiron Institute

Feb 2024 – Present New York, NY

Research projects:

  • Developing neural circuit model to understand olfactory information processing
 
 
 
 
 

Postdoctoral researcher

Harvard University

Aug 2019 – Dec 2023 Cambridge, MA

Research projects:

  • Developed biologically plausible learning algorithms.
  • Unraveled causes and dynamics of drifting neural representations.
  • Characterized ensemble-level odor coding properties in C. elegans, olfactory information processing in fly larvae
 
 
 
 
 

Visiting student

University of California, Berkeley

Jun 2016 – Sep 2016 Berkeley, CA
Visiting scholar at the Hernan Garcia Lab at the Department of Molecular and Cell Biology. Designed and performed experimental study on the temperature-dependent speed of embryogenesis in Drosophila Melanogaster
 
 
 
 
 

Visiting student

University of California, Santa Barbara

Aug 2015 – Sep 2016 Santa Barbara, CA
As as summer school student, I Investigated embryogesis process of insect embryos using quantitative fluorescence microscopy.
 
 
 
 
 

Ph.D Student

Peking University

Sep 2012 – Jul 2019 Beijing, China

I was advised by Dr. Chao Tang at the Center for Quantitative Biology, Peking University. My research was focused on quantitative and systems biology, biophysics and computational neuroscience. Research projects:

  • Established a theoretical framework for nonlinear compressed sensing problems and applied it to the peripheral olfactory systems.
  • Investigated the effect of extrinsic noise in early warning signals in nonlinear complex systems.
 
 
 
 
 

Undergraduate research assistant

Central China Normal University

Jun 2009 – Nov 2010 Wuhan, China
Successfully prepared the hematite nanorods array, and studied its electrochemical properties as an anode material and reversible lithium storage

Recent Posts

Cosyne2024

I will be presenting a poster on input-dependent drift at the CoSyNe conference at Lisbon from Feb 29 to March 6.

Accomplish­ments

Distinction in Teaching

Been recognized for excellence in teaching during the Fall semester of 2020 in the course Neural Computation.

Recent & Upcoming Talks

Skills

MATLAB

100%

Python

100%

R

80%

C/C++

30%

LaTex

100%

Contact

  • 160 5th Ave, New York, NY 10010