The study of neuronal populations through graph and network theory provides powerful insights into brain organizational principles [1]. This half-day tutorial will focus on computational tools and modern learning paradigms for analyzing neuronal activity as networks, uncovering functional organization, and learning transferable representations of neural systems across scales and modalities.
The tutorial will feature two main components:
1. Graph Analysis of Neuronal Populations
Participants will learn to construct and analyze networks from neuronal activity using graph-theoretic approaches. Topics include adjacency matrix construction, modularity detection, and metrics such as clustering coefficients, centrality, and small-worldness to characterize neuronal communication and information flow [2,3,4]. Practical examples will use experimental datasets, including in vitro spike trains from CL1—demonstrating adaptive, task-oriented behavior in living neuronal cultures [5,6]— and example fMRI time series. Common pitfalls in interpreting functional connectivity and network- based analyses will also be discussed [7].
2. Foundation Models for Neural Data and Brain Networks
This component will provide a step-by-step introduction to modern machine learning models for neural data, progressing from sequence-to-sequence formulations to Transformer architectures and contemporary foundation models. Emphasis will be placed on developing intuition and understanding these methods from first principles, with concrete examples drawn from neural time series and network-structured data. A key example will be BrainSymphony [8], illustrating how foundation models can jointly capture neural dynamics and structure for downstream tasks such as classification, prediction, and biomarker discovery.
Beyond fMRI, the tutorial will discuss applications of foundation models across neuroscience, including causal discovery [9], electrophysiology tasks such as spike detection [10] and stimulus– response prediction [11], and representation learning [8,12]. Participants will gain hands-on exposure to Python-based workflows, alongside discussion of scalability, inductive biases, and open challenges in applying foundation models to neural systems.
Attendees will gain practical insight into combining graph-theoretic analysis with foundation models for neural population analysis, appealing to researchers in brain connectivity, neural computation, and representation learning.
References
[1] Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186-198.
[2] Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 52(3), 1059-1069.
[3] Khajehnejad, M., & Habibollahi, F., et al. (2023). On Complex Network Dynamics of an In-Vitro Neuronal System during Rest and Gameplay. In NeurIPS 2023 Workshop on Symmetry and Geometry in Neural Representations.
[4] Khajehnejad, M., Habibollahi, F., Khajehnejad, A., French, C., Kagan, B. J., & Razi, A. (2024). Graph-Based Representation Learning of Neuronal Dynamics and Behavior. arXiv preprint arXiv:2410.00665.
[5] Kagan, B. J., et al. (2022). In vitro neurons learn and exhibit sentience when embodied in a simulated game-world. Neuron, 110(23), 3952–3969.
[6] Cortical Labs: https://corticallabs.com/
[7] Bastos, A. M., & Schoffelen, J. M. (2015). A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Frontiers in Systems Neuroscience, 9, 175.
[8] Khajehnejad, M., Habibollahi, F., & Razi, A. (2025). BrainSymphony: A Transformer-Driven Fusion of fMRI Time Series and Structural Connectivity. arXiv preprint arXiv:2506.18314.
[9] Nag, S., & Uludag, K. (2024). Transformer-aided dynamic causal model for scalable estimation of effective connectivity. Imaging Neuroscience, 2, 1-22.
[10] Wei, F., Mo, J., Zhang, K., Shen, H., Nagarajan, S., & Jiang, F. (2024). Nested deep learning model towards a foundation model for brain signal data. arXiv preprint arXiv:2410.03191.
[11] Wang, E. Y., Fahey, P. G., Ding, Z., Papadopoulos, S., Ponder, K., Weis, M. A., ... & Tolias, A. S. (2025). Foundation model of neural activity predicts response to new stimulus types. Nature, 640(8058), 470-477.
[12] Dong, Z., Li, R., Wu, Y., Nguyen, T. T., Chong, J., Ji, F., ... & Zhou, J. H. (2024). Brain-jepa: Brain dynamics foundation model with gradient positioning and spatiotemporal masking. Advances in Neural Information Processing Systems, 37, 86048-86073.
