Self-Organized Context Dependent Processing in Neural Networks

Abstract Context dependent processing (CDP) enables flexible responses to stimuli based on varying circumstances. It is a crucial aspect of higher cognition that supports primates’ adaptive behavior in dynamic environments. However, the structural requirements for its emergence remain poorly understood. The current study bridges this gap by investigating what network architectures enable the spontaneous development of CDP in artificial neural networks (ANNs). We demonstrate that networks with two parallel pathways that merge via cross-product self-organize towards CDP (we termed SOCDP), with spontaneously emerging functional modules for processing contextual and sensory information, respectively, mirroring the key aspects of CDP in the primate brain. Further analysis revealed that the pressure to efficiently navigate high-dimensional information space—a challenge akin to overcoming the curse of dimensionality—is the driver behind the SOCDP. Furthermore, we identified crucial factors such as network size, structural asymmetry, and task complexity in influencing the emergence of functional specialization in the system. These results suggest that CDP can arise spontaneously given proper network structural predisposition, providing insights into the computational principles underlying flexible cognition in biological systems and how structural constraints shape functional organization in neural architectures.