Research topics

1. Data driven discovery of computation principles

Historically, scientists have inferred the brain’s plasticity rules and decision making principles (e.g., reinforcement learning) manually from neural recordings and behavioral observations. However, as datasets grow larger and task paradigms become increasingly complex, these traditional approaches may become less feasible. This underscores the need for algorithms that enable data-driven discovery of learning rules. Some of my onging work is guided by this line of thoughts:

Multi-agent inverse reinforcement learning to reveal the value functions, Theory-of-Mind mentalization process of animals. My current work has setup an inference algorithm to achieve that in multiple animals.
Data driven discovery of social learning rules. By accessing value functions at different snapshots of the learning process, we can link their updates to various functional forms of decisions, outcomes, and others’ choices. My ongoing work explores that using reinformence learning constrained state-space modeling.How DeepMind team approaches that using LLMs.
Data driven discovery of neural plasticity rules. After inferring behavioral-level learning rules, the next step is to uncover how the brain implements them. Recent advances in meta-learning provide promising tools for this endeavor. [Ref1] [Ref2]

2. Revealing network learning from representations

Where we are now can provide important clues about where we came from. By examining the neural representations that emerge during a given behavioral paradigm, it is possible—at least in artificial neural networks (ANNs)—to infer the underlying learning trajectories. In recent years, there has been a surge of theoretical work in machine learning aimed at opening the “black box” of learning dynamics and representations in ANNs. These advances offer powerful tools that we can also leverage to illuminate the black box of brain networks.

What is the unifying objective function of the brain that accounts for the wide range of findings observed in neuroscience experiments? My previous work focused on temporal predictive coding in hippocampus. Future work would be focusing on extending the temporal predictive coding framework to cortical column computation in neural processing hierachy.
What unique inductive biases and constraints of the brain contribute to its flexible and adaptive learning? My onging work explored the emergence of cognitive maps underlying different network learning rules and initialization.

3. Revealing the brain’s functional connectivity

While many statistical methods can reveal correlational structures, evaluating the brain’s functional connectivity requires accounting for its intrinsic dynamics. My previous work was the first to integrate neural dynamics into the evaluation of functional connectivity, opening opportunities for many extensions-for example, incorporating specific nonlinearities or modeling the influence of external inputs.

Now with the increasing amount of shared modality data recording functional activities and structural connectivity (e.g. MiCRONs and V1 Deep Dive from Allen Institute), this method might come handy while handling these functional connectivities.

Selected Publications

Chen, Y., Zhang, H., Cameron, M. & Sejnowski, T.J. (2024) Predictive sequence learning in the hippocampal formation. Neuron 112, 1-14. [paper]
Chen, Y., Rosen, B. Q. & Sejnowski, T. J. (2022) Dynamical differential covariance recovers directional network structure in multiscale neural systems. Proceedings of the National Academy of Sciences (2022). [paper][code] [5-min video]

Preprints

Cheng, Y^*, Chen, Y^*, … & Wu, Z. (2025) Asymmetric Social Representations in the Prefrontal Cortex for Cooperative Behavior. bioRxiv [paper]
Chen, Y., Radulescu, A. & Wu, Z. (2024) Unveiling the latent dynamics in social cognition with multi-agent inverse reinforcement learning. bioRxiv [paper]
Cameron, M., Chen, Y. & Sejnowski, T. A biologically-plausible alternative to backpropagation using pseudoinverse feedback connections. Accepted to Cosyne 2025
Chen, Y., Recanatesi, S., Jiang, P., Rao, R., Mihalas, S., Fairhall, A., & Shea-Brown, E. How learning regimes shape the emergence of cognitive maps. in prep
Chen, Y., Recanatesi, S., Liu, S., Cohen, J., Shea-Brown, E. Reinforcement learning constrained state space modeling of neural decisions. in prep

Service

Reviewer for Nature, Proceedings of the National Academy of Sciences, ICLR, Neural Computation
TA for Allen Summer Workshop on the Dynamics Brain
Graduate instructor assistant for system computational neuroscience, UCSD
Graduate instructor assistant for bioinformatics, UCSD

Awards

Swartz Postdoctoral Fellowship (2023)
Kavli-Helinski Fellowship (2021, 2022)
National Scholarship of China (2016)

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