EDEN: Emergent Dynamics in Evolutionary Neural-networks for Robust Continuous Control

Biological neural systems leverage neuronal heterogeneity and diverse firing patterns to generate rich nonlinear dynamics, yet these mechanisms are largely abstracted away in conventional artificial neural networks (ANNs) and simplified spiking neural networks (SNNs). Detailed differential-equation neural models are biologically expressive, but spike discontinuities and complex state evolution pose challenges for gradient-based optimization in complex functional tasks. To address this challenge, we introduce EDEN (Emergent Dynamics in Evolutionary Neural-Networks), a neural dynamic model training framework inspired by natural evolutionary processes. Built upon a continuous-time network of heterogeneous Izhikevich neurons, EDEN integrates efficient parallel neural dynamics simulation with advanced evolutionary strategies, enabling the joint optimization of intrinsic single-neuron-level parameters and network-level synaptic connections without backpropagation. Comprehensive evaluations across multiple MuJoCo continuous motor control tasks indicate that EDEN achieves robust control performance comparable to mainstream deep reinforcement learning baselines. Furthermore, neurodynamic analysis reveals that the trained EDEN models exhibit strong biological plausibility, characterized by functional differentiation across individual neurons and emergent low-dimensional latent manifolds governing population dynamics. EDEN demonstrates that the combination of gradient-free evolutionary strategies and biological neurodynamic networks provides an alternative yet efficient paradigm alongside mainstream reinforcement learning, thereby offering a highly promising pathway towards truly biologically-inspired intelligence.