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Proceedings of the AAAI Conference on Artificial Intelligence

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Energy-based Autoregressive Generation for Neural Populat...
Ningling Ge, · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Ningling Ge Institute of automation, Chinese academy of science School of Artifcial Intelligence, University of Chinese Academy of Sciences State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology
  • Sicheng Dai Institute of automation, Chinese academy of science School of Artifcial Intelligence, University of Chinese Academy of Sciences State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Beijing Academy of Artificial Intelligence
  • Yu Zhu Institute of automation, Chinese academy of science School of Artifcial Intelligence, University of Chinese Academy of Sciences State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Beijing Academy of Artificial Intelligence
  • Shan Yu Institute of automation, Chinese academy of science State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology

DOI:

https://doi.org/10.1609/aaai.v40i1.36992

Abstract

Understanding brain function represents a fundamental goal in neuroscience, with critical implications for therapeutic interventions and neural engineering applications. Computational modeling provides a quantitative framework for accelerating this understanding, but faces a fundamental trade-off between computational efficiency and high-fidelity modeling. To address this limitation, we introduce a novel Energy-based Autoregressive Generation (EAG) framework that employs an energy-based transformer learning temporal dynamics in latent space through strictly proper scoring rules, enabling efficient generation with realistic population and single-neuron spiking statistics. Evaluation on synthetic Lorenz datasets and two Neural Latents Benchmark datasets (MC_Maze and Area2_bump) demonstrates that EAG achieves state-of-the-art generation quality with substantial computational efficiency improvements, particularly over diffusion-based methods. Beyond optimal performance, conditional generation applications show two capabilities: generalizing to unseen behavioral contexts and improving motor brain-computer interface decoding accuracy using synthetic neural data. These results demonstrate the effectiveness of energy-based modeling for neural population dynamics with applications in neuroscience research and neural engineering.

How to Cite

Ge, N., Dai, S., Zhu, Y., & Yu, S. (2026). Energy-based Autoregressive Generation for Neural Population Dynamics. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 309–317. https://doi.org/10.1609/aaai.v40i1.36992

Issue

Section

AAAI Technical Track on Application Domains I

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