State-dependent filtering as a mechanism toward visual robustness - Yan, Jing; Feng, Yunxuan; Dai, Wei P.; Zhang, Yaoyu (Frontiers in Computational Neuroscience, 2025)
Robustness, defined as a system's ability to maintain functional reliability in the face of perturbations, is achieved through its capacity to filter external disturbances using internal priors encoded in its structure and states. While biophysical neural networks are widely recognized for their robustness, the precise mechanisms underlying this resilience remain poorly understood. In this study, we explore how orientation-selective neurons arranged in a one-dimensional ring network respond to perturbations, with the aim of uncovering insights into the robustness of visual subsystems in the brain. By analyzing the steady-state dynamics of a rate-based network, we characterize how the activation state of neurons influences the network's response to disturbances. Our results demonstrate that the activation state of neurons, rather than their firing rates alone, governs the network's sensitivity to perturbations. We further show that lateral connectivity modulates this effect by shaping the response profile across spatial frequency components. These findings suggest a state-dependent filtering mechanism that contributes to the robustness of visual circuits, offering theoretical insight into how different components of perturbations are selectively modulated within the network.