Balwani Aishwarya H, Wang Alex Q, Najafi Farzaneh, Choi Hannah
School of Electrical & Computer Engineering, Georgia Institute of Technology.
Computational Science and Engineering Program, Georgia Institute of Technology.
bioRxiv. 2025 Jan 13:2025.01.09.632231. doi: 10.1101/2025.01.09.632231.
Recurrent neural networks (RNNs) have emerged as a prominent tool for modeling cortical function, and yet their conventional architecture is lacking in physiological and anatomical fidelity. In particular, these models often fail to incorporate two crucial biological constraints: i) Dale's law, i.e., sign constraints that preserve the "type" of projections from individual neurons, and ii) Structured connectivity motifs, i.e., highly sparse yet defined connections amongst various neuronal populations. Both constraints are known to impair learning performance in artificial neural networks, especially when trained to perform complicated tasks; but as modern experimental methodologies allow us to record from diverse neuronal populations spanning multiple brain regions, using RNN models to study neuronal interactions without incorporating these fundamental biological properties raises questions regarding the validity of the insights gleaned from them. To address these concerns, our work develops methods that let us train RNNs which respect Dale's law whilst simultaneously maintaining a specific sparse connectivity pattern across the entire network. We provide mathematical grounding and guarantees for our approaches incorporating both types of constraints, and show empirically that our models match the performance of RNNs trained without any constraints. Finally, we demonstrate the utility of our methods for inferring multi-regional interactions by training RNN models of the cortical network to reconstruct 2-photon calcium imaging data during visual behaviour in mice, whilst enforcing data-driven, cell-type specific connectivity constraints between various neuronal populations spread across multiple cortical layers and brain areas. In doing so, we find that the interactions inferred by our model corroborate experimental findings in agreement with the theory of predictive coding, thus validating the applicability of our methods.
循环神经网络（RNNs）已成为一种用于模拟皮层功能的重要工具，然而其传统架构在生理和解剖学逼真度方面存在不足。特别是，这些模型往往未能纳入两个关键的生物学限制：i）戴尔定律，即保留来自单个神经元投射“类型”的符号限制，以及ii）结构化连接模式，即不同神经元群体之间高度稀疏但定义明确的连接。众所周知，这两个限制都会损害人工神经网络的学习性能，尤其是在训练其执行复杂任务时；但是随着现代实验方法使我们能够记录来自跨越多个脑区的不同神经元群体的数据，使用RNN模型来研究神经元相互作用而不纳入这些基本生物学特性，这引发了对从中获得的见解有效性的质疑。为了解决这些问题，我们的工作开发了一些方法，使我们能够训练尊重戴尔定律的RNN，同时在整个网络中保持特定的稀疏连接模式。我们为纳入这两种类型限制的方法提供了数学基础和保证，并通过实验表明我们的模型与无任何限制训练的RNN性能相当。最后，我们通过训练皮层网络的RNN模型来重建小鼠视觉行为期间的双光子钙成像数据，同时在分布于多个皮层层和脑区的不同神经元群体之间强制实施数据驱动的、细胞类型特定的连接限制，以此证明我们的方法在推断多区域相互作用方面的实用性。通过这样做，我们发现我们的模型推断出的相互作用与预测编码理论一致，从而证实了我们方法的适用性。
