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I am looking for 2025fall Theoretical(Computational) Neuroscience Phd position in US.

The notes on this page (including path integral and Koopman theory etc) have not been fully uploaded due to phd application.(2024-08-01)

Research Interests: Theoretical/Computational Neuroscience, exploring general principles of neural computation. Searching for lower-dimensional structure in the brain and finding the biological mechanisms that are essential to support it.

I am now focusing on Maximum Entropy Model and new clustering method to identify neural assembly.

A Metaphorical Analogy for My PhD Neuroscience Research Plan:

As human beings, we aspire to become gods. We live on the ground, while the gods reside in the sky. The mainstream approach is to find clouds in the sky that resemble the gods in some way. We aim for these clouds, striving to elevate ourselves (like wearing taller shoes) in the hope of reaching them. We repeat this process, always seeking higher and higher clouds, believing that the gods dwell at the highest point. However, I do not agree with this approach.

In my view, for humans to become gods, the goal isn’t to make humans stronger or taller, but rather to bring the gods down to our level, stripping away the lofty embellishments until they become reachable by us (at a height where a simple jump can touch them). To realize this vision, my plan is as follows:

Locally observe the gods—this inevitably involves studying the clouds.
Pull the gods down from the sky, as low as possible.
Examine "god-like" beings that already exist among us, to understand what features they possess.
With the insights gained from the previous steps, make a leap towards becoming gods ourselves.

Throughout each of these steps, we must occasionally jump, making our observations and actions more smart.

Note: Due to my ongoing application process, I can't yet share the specific meaning behind this metaphor. However, once the application process is over, I’d be happy to explain it in detail here.

PhD period proposal(outdated):
Stage 1: Describe features of neural design principle with the help of phenomenological models, i.e. Maximum Entropy Model.
Stage 2: Construct Lower-dimensional models with neural design principle and find corresponding brain mechanism to support such coarse-grained computation.

I dream of achieving the following goals by studying and researching neurotheory and believe that such coarse grained theories are as important as some biological experiments.

What is the definition of "understanding" the brain? Theories in different scale? Simulating every details? At least the latter, it is not and should not be. (Unfortunately, much of the research in modern neuroscience(so called mechanism and causal chain study) inadvertently conveys this.)
I offer a new possibility: we can transfer consciousness and we can simulate consciousness. It is with this limitation that I have developed my opinion.

Here are some older views, not yet updated:
I believe there are lower dimensional structures in the brain(at least in some brain regions) relative to the number of neurons (although it would still be high dimensional). Compared to traditional linear-dimensional reduction method(latent factors), there should be some new spaces(not state space!) contain such lower dimensional structure. Here are some related work(based on different context or views): MEM, learnability, Control theory(Perturbation). Considering that these conjectures/conclusions will be closely related to what I consider to be two milestones in neuroscience - the simultaneous recording of all neuronal activity in the whole brain at a certain resolution, and the recording of all the biological components in the CNS - the best place to test the theory, at least at this stage, will be C. elegans and Larval Zebrafish and other small animals.

Mid-term goal: control theory of the nervous system, which currently seems to be best studied in small animals (C. elegans and Larval Zebrafish). This includes, but is not limited to, studying the sensitivity and causal-functional connectivity of neural networks and their relationship to network function. And the discovery of generalized low-dimensional structures in the neural circuits.

Ultimate goal: simulate (transferred from the biological brain) personal consciousness in silico.

While lately I am very interested in some specific topics in theoretical neuroscience: Cerebellum-like Structures, Geometry of Perceptual Manifolds, Clustering Principles of Neural Activity, Bridging Neural Manifolds and Neural Circuits, and how the new neurophysiological phenomena observed at molecular and single cellular level in experiment effects macroscopic neuronal networks dynamics.

Here's a detailed version of my research interest (informal) on July 21st, 2023.

Research Internship University of Washington Supervisor: Adrienne Fairhall 2023.7-present
Visiting Student Shanghai JiaoTong University Supervisor: Songting Li and Douglas Zhou 2022.2-2023.1
Research Assistant Shanghai JiaoTong University Supervisor: Ru-Yuan Zhang 2021.8-2022.1
Research Assistant Xi'an JiaoTong Liverpool University Supervisor: Kaizhu Huang 2020.6-2021.6

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Chongwen Wang

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