邹立诚 Licheng Zou

Doctoral Researcher

Göttingen Campus Institut für Dynamik biologischer Netzwerke

Biography

Hi, welcome! I’m Licheng Zou, currently a PhD student at Göttingen Campus Institute for Biological Networks/ Max-Planck Institute for Dynamics and Self-organization, receiving supervision from Prof. Dr. Fred Wolf. Trained in applied mathematics and biomedical sciences, my academic career focuses on computational neuroscience. Since 2024, I’ve been investigating the primary visual cortex and aiming at understand visual neuron dynamics through multi-scale computational approaches.

Interests

Mean-field Analysis
Synaptic Plasticity
Memory Attractor
Evolutionary Optimization
Turing Instability
Large-scale Simulation

Education

PhD in Theoretical Neurophysics, 2028
Georg August University of Göttingen
MSc in Neurobiology, 2025
University of Amsterdam
BSc in Mathematics, 2023
Shanghai Jiao Tong University

Skills

Technical

Python

Matlab

Applied Mathematics

Hobbies

Movies

Fitness

Hiking

Experience

Visiting Scientist (summer)

Department of Neural Science, NYU Shanghai

June 2025 – August 2025 Shanghai

Together with Dr. Zhuocheng Xiao, we investigate the network mechanisms underlying the emergence of V1 functional receptive fields through large-scale simulations and multi-scale analysis.

Graduate Research Internship (hybrid)

University of Amsterdam, University of Chicago, German Primate Center

September 2024 – June 2025 Amsterdam, Goettingen

An interdisciplinary collaboration across continents, aiming at investigating the neural mechanisms underlying macaque perceptual decision-making via pulvinar-cortex interactions. My role is to build up a bi-hemisphere thalamocortical circuit model to reproduce the collected neuronal spiking data under different scenarios – control, lesion, and micro-stimulation.

Graduate Research Internship

Computational Neuroscience Group, Centre de Recerca Matematica

July 2023 – September 2024 Barcelona

Funded by the Erasmus+ internship scholarship, I performed two research projects on representational drift under supervision of Dr. Alex Roxin. In the meantime, I actively participated in the academic events taking place within the Great Barcelona neuroscience community, including one conference and two workshops.

Undergraduate Research Internship

Cognitive and Systems Neuroscience Group, University of Amsterdam

September 2022 – June 2023 Amsterdam

Funded by the generous Zhiyuan overseas internship scholarship, I performed the research project on distributed models underlying perceptual decision-making under supervision of Dr. Jorge Mejias. And my bachelor thesis was produced as part of the output of this internship.

Undergraduate Research Internship

Lab of computational neuroscience, Shanghai Jiao Tong University

November 2021 – June 2022 Shanghai

Thanks for Dr. Douglas Zhou’s admission, I joined his lab co-directed by Dr. Songting Li and started my journey into computational neuroscience (CNS). During this internship, I got knowledge of foundation and different research topics in CNS, and presented interested literatures during weekly journal clubs. In the meantime, I attended two CNS winter schools hosted by the same institution and one online conference.

Projects

A distributed neocortical model on decision-making (DM).

Recent electrophysiology experiments report distributed cortical responses to a random-dot discrimination task, and we built a biologically plausible neocortical model incorporating with different datasets to capture this phenomenon.

Large-scale simulation of rodent V1 across laminae.

With the emergence of large-scale recording data in mouse, we wondered whether a data-constrained model can replicate the functional receptive field properties across rodent V1 neuron types.

Network mechanisms underlying representational drift (RD)

We tested the hypothesis treating RD as the inevitable consequence of synaptic turnover by fitting a network model to data from long-term chronic calcium imaging experiments in mouse hippocampus. Synaptic turnover in the model inputs, consistent with the ongoing encoding of new activity patterns, accounted for the observed statistics of RD.