Energy-based Autoregressive Generation for Neural Population Dynamics

• URL: http://arxiv.org/abs/2511.17606v1
• Date: Tue, 18 Nov 2025 07:11:29 GMT
• Title: Energy-based Autoregressive Generation for Neural Population Dynamics
• Authors: Ningling Ge, Sicheng Dai, Yu Zhu, Shan Yu,
• Abstract summary: We introduce a novel Energy-based Autoregressive Generation framework that employs an energy-based transformer learning temporal dynamics in latent space.<n>We show that EAG achieves state-of-the-art generation quality with substantial computational efficiency improvements.<n>These results demonstrate the effectiveness of energy-based modeling for neural population dynamics with applications in neuroscience research and neural engineering.
• Score: 12.867288040044501
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. Code is available at https://github.com/NinglingGe/Energy-based-Autoregressive-Generation-for-Neural-Population-Dynamics.

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