Gerrity Charles Grimes, Treuting Robert Louis, Peters Richard Alan, Womelsdorf Thilo
Vanderbilt University, Department of Psychology, Nashville, TN 37203 USA.
Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, TN 37203 USA.
bioRxiv. 2025 Aug 25:2025.08.20.671126. doi: 10.1101/2025.08.20.671126.
Recent advances in neuroscience enable recording neuronal signals across hundreds of channels while subjects perform complex tasks involving multiple stimulus dimensions. In this study, we developed a novel encode-decode-classify framework employing a gated recurrent variational autoencoder (VAE) to decode decision-making processes from over 300 simultaneously recorded neuronal channels in the prefrontal cortex and basal ganglia of monkeys performing a multidimensional feature-learning task. Using hierarchical stratified sampling and balanced accuracy, we trained and evaluated the model's ability to predict behavioral choices based on neuronal population dynamics. The results revealed distinct neural coding roles, with anterior cingulate cortex (ACC) channels encoding decision variables collectively and prefrontal cortex (PFC) channels contributing individually to decoding accuracy. This approach demonstrated decoding accuracy for decisions in multi-dimensional feature space that is comparable to single-label decoding accuracy for lower dimensional problems, highlighting the potential of machine learning frameworks to capture complex spatiotemporal neuronal interactions involved in multidimensional cognitive behaviors. The code has been released in https://github.com/cgerrity/Neural-Data-Reading.
神经科学的最新进展使得在受试者执行涉及多个刺激维度的复杂任务时，能够记录数百个通道的神经元信号。在本研究中，我们开发了一种新颖的编码-解码-分类框架，该框架采用门控循环变分自编码器（VAE），从执行多维特征学习任务的猴子的前额叶皮层和基底神经节中同时记录的300多个神经元通道解码决策过程。我们使用分层抽样和平衡准确率，训练并评估了该模型基于神经元群体动态预测行为选择的能力。结果揭示了不同的神经编码作用，前扣带回皮层（ACC）通道共同编码决策变量，前额叶皮层（PFC）通道则分别对解码准确率有贡献。这种方法展示了在多维特征空间中决策的解码准确率，与低维问题的单标签解码准确率相当，突出了机器学习框架捕捉多维认知行为中复杂时空神经元相互作用的潜力。代码已在https://github.com/cgerrity/Neural-Data-Reading上发布。
