How Initial Connectivity Shapes Biologically Plausible Learning in Recurrent Neural Networks

The impact of initial connectivity on learning has been extensively studied in the context of backpropagation-based gradient descent, but it remains largely underexplored in biologically plausible learning settings. Focusing on recurrent neural networks (RNNs), we found that the initial weight magnitude significantly influences the learning performance of biologically plausible learning rules in a similar manner to its previously observed effect on training via 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 could lead to predictions about the influence of initial connectivity on learning dynamics and performance, as well as guide neuromorphic design.