Understanding how the brain learns can be advanced by investigating biologically plausible learning rules -- those that obey known biological constraints, such as locality, to serve as valid brain learning models. Yet, many studies overlook the role of architecture and initial synaptic connectivity in such models. Building on insights from deep learning, where initialization profoundly affects learning dynamics, we ask a key but underexplored neuroscience question: how does initial synaptic connectivity shape learning in neural circuits? To investigate this, we train recurrent neural networks (RNNs), which are widely used for brain modeling, with biologically plausible learning rules. Our findings reveal that initial weight magnitude significantly influences the learning performance of such rules, mirroring effects previously observed in training with backpropagation through time (BPTT). By examining the maximum Lyapunov exponent before and after training, we uncovered the greater demands that certain initialization schemes place on training to achieve desired information propagation properties. Consequently, we extended the recently proposed gradient flossing method, which regularizes the Lyapunov exponents, to biologically plausible learning and observed an improvement in learning performance. To our knowledge, we are the first to examine the impact of initialization on biologically plausible learning rules for RNNs and to subsequently propose a biologically plausible remedy. Such an investigation can lead to neuroscientific predictions about the influence of initial connectivity on learning dynamics and performance, as well as guide neuromorphic design.

翻译：理解大脑如何学习可以通过研究生物合理性学习规则——那些遵守已知生物学约束（如局部性）从而作为有效大脑学习模型的规则——来推进。然而，许多研究忽视了此类模型中架构和初始突触连接性的作用。基于深度学习的见解（其中初始化对学习动态有深远影响），我们提出了一个关键但在神经科学中尚未充分探索的问题：初始突触连接性如何塑造神经回路中的学习？为探究此问题，我们使用生物合理性学习规则训练循环神经网络（RNNs），该网络被广泛用于大脑建模。我们的研究结果表明，初始权重幅度显著影响此类规则的学习性能，这反映了先前在使用时间反向传播（BPTT）训练中观察到的效应。通过检查训练前后的最大李雅普诺夫指数，我们揭示了某些初始化方案为实现期望的信息传播特性而对训练提出的更高要求。因此，我们扩展了最近提出的梯度约束方法（该方法正则化李雅普诺夫指数），将其应用于生物合理性学习，并观察到学习性能的提升。据我们所知，我们是首个研究初始化对RNNs生物合理性学习规则影响并随后提出生物合理性补救措施的工作。此类研究可得出关于初始连接性对学习动态和性能影响的神经科学预测，并指导神经形态设计。