Education & Certifications
B. S., School of Physics, Peking University, Physics (2018)
Current Research and Scholarly Interests
I am an Applied Physics PhD candidate in Baccus lab co-advised by Surya Ganguli. My research focuses on building encoding models of the retina with various biophysical properties especially for natural scenes and answering scientific questions based on computational models. I believe that the core problem in the field of sensory systems is to understand the representation manifold and I am achieving this goal with methods of differential geometry, deep learning, statistical physics, etc.
Information Geometry of the Retinal Representation Manifold.
bioRxiv : the preprint server for biology
The ability to discriminate visual stimuli is constrained by their retinal representations. Previous studies of visual discriminability were limited to either low-dimensional artificial stimuli or theoretical considerations without a realistic model. Here we propose a novel framework for understanding stimulus discriminability achieved by retinal representations of naturalistic stimuli with the method of information geometry. To model the joint probability distribution of neural responses conditioned on the stimulus, we created a stochastic encoding model of a population of salamander retinal ganglion cells based on a three-layer convolutional neural network model. This model not only accurately captured the mean response to natural scenes but also a variety of second-order statistics. With the model and the proposed theory, we are able to compute the Fisher information metric over stimuli and study the most discriminable stimulus directions. We found that the most discriminable stimulus varied substantially, allowing an examination of the relationship between the most discriminable stimulus and the current stimulus. We found that the most discriminative response mode is often aligned with the most stochastic mode. This finding carries the important implication that under natural scenes noise correlations in the retina are information-limiting rather than aiding in increasing information transmission as has been previously speculated. We observed that sensitivity saturates less in the population than for single cells and also that Fisher information varies less than sensitivity as a function of firing rate. We conclude that under natural scenes, population coding benefits from complementary coding and helps to equalize the information carried by different firing rates, which may facilitate decoding of the stimulus under principles of information maximization.
View details for DOI 10.1101/2023.05.17.541206
View details for PubMedID 37292703
View details for PubMedCentralID PMC10245665
A mechanistically interpretable model of the retinal neural code for natural scenes with multiscale adaptive dynamics.
Conference record. Asilomar Conference on Signals, Systems & Computers
2021; 2021: 287-291
The visual system processes stimuli over a wide range of spatiotemporal scales, with individual neurons receiving input from tens of thousands of neurons whose dynamics range from milliseconds to tens of seconds. This poses a challenge to create models that both accurately capture visual computations and are mechanistically interpretable. Here we present a model of salamander retinal ganglion cell spiking responses recorded with a multielectrode array that captures natural scene responses and slow adaptive dynamics. The model consists of a three-layer convolutional neural network (CNN) modified to include local recurrent synaptic dynamics taken from a linear-nonlinear-kinetic (LNK) model . We presented alternating natural scenes and uniform field white noise stimuli designed to engage slow contrast adaptation. To overcome difficulties fitting slow and fast dynamics together, we first optimized all fast spatiotemporal parameters, then separately optimized recurrent slow synaptic parameters. The resulting full model reproduces a wide range of retinal computations and is mechanistically interpretable, having internal units that correspond to retinal interneurons with biophysically modeled synapses. This model allows us to study the contribution of model units to any retinal computation, and examine how long-term adaptation changes the retinal neural code for natural scenes through selective adaptation of retinal pathways.
View details for DOI 10.1109/ieeeconf53345.2021.9723187
View details for PubMedID 38013729
View details for PubMedCentralID PMC10680971
A mechanistically interpretable model of the retinal neural code for natural scenes with multiscale adaptive dynamics
2021 55th Asilomar Conference on Signals, Systems, and Computers
View details for DOI 10.1109/IEEECONF53345.2021.9723187
- Measurement-driven single temperature engine PHYSICAL REVIEW E 2018; 98 (4)