🤖 AI Summary
Traditional neural networks rely on biologically implausible global backpropagation and inadequately model neuroplasticity mechanisms. To address this, we propose SAPIN—a novel spiking neural architecture that integrates structural plasticity with active inference for the first time. SAPIN jointly optimizes synaptic weights and neuronal spatial positions on a 2D grid, driven solely by local prediction errors: these errors govern Hebbian-like synaptic updates and activity-dependent neuronal migration, enabling co-evolution of network topology and connection strength. The resulting architecture implements a self-stabilizing, predictive coding–oriented computation grounded in error minimization. Evaluated on the CartPole task, SAPIN achieves an 82% average success rate across 100 trials using fixed parameters, demonstrating robust reinforcement learning capability and policy stability. Our core contribution is a biologically plausible, computationally efficient paradigm for dynamic, synergistic optimization—bridging neuroscience realism with machine learning efficacy.
📝 Abstract
Traditional neural networks, while powerful, rely on biologically implausible learning mechanisms such as global backpropagation. This paper introduces the Structurally Adaptive Predictive Inference Network (SAPIN), a novel computational model inspired by the principles of active inference and the morphological plasticity observed in biological neural cultures. SAPIN operates on a 2D grid where processing units, or cells, learn by minimizing local prediction errors. The model features two primary, concurrent learning mechanisms: a local, Hebbian-like synaptic plasticity rule based on the temporal difference between a cell's actual activation and its learned expectation, and a structural plasticity mechanism where cells physically migrate across the grid to optimize their information-receptive fields. This dual approach allows the network to learn both how to process information (synaptic weights) and also where to position its computational resources (network topology). We validated the SAPIN model on the classic Cart Pole reinforcement learning benchmark. Our results demonstrate that the architecture can successfully solve the CartPole task, achieving robust performance. The network's intrinsic drive to minimize prediction error and maintain homeostasis was sufficient to discover a stable balancing policy. We also found that while continual learning led to instability, locking the network's parameters after achieving success resulted in a stable policy. When evaluated for 100 episodes post-locking (repeated over 100 successful agents), the locked networks maintained an average 82% success rate.