State-dependent Filtering of the Ring Model

TL;DR

This paper investigates how orientation-selective neurons in a ring network respond to perturbations, revealing that activation states influence robustness and that certain sinusoidal patterns can induce maximal responses, akin to adversarial attacks.

Contribution

It demonstrates that neuron activation states, not firing rates, determine responses to perturbations and links these findings to visual robustness and adversarial vulnerabilities.

Findings

Activation states, not firing rates, govern response to perturbations.

Sinusoidal perturbations induce maximal responses.

Optimal perturbations resemble adversarial attacks in deep learning.

Abstract

Robustness is a measure of functional reliability of a system against perturbations. To achieve a good and robust performance, a system must filter out external perturbations by its internal priors. These priors are usually distilled in the structure and the states of the system. Biophysical neural network are known to be robust but the exact mechanisms are still elusive. In this paper, we probe how orientation-selective neurons organized on a 1-D ring network respond to perturbations in the hope of gaining some insights on the robustness of visual system in brain. We analyze the steady-state of the rate-based network and prove that the activation state of neurons, rather than their firing rates, determines how the model respond to perturbations. We then identify specific perturbation patterns that induce the largest responses for different configurations of activation states, and find them to be sinusoidal or sinusoidal-like while other patterns are largely attenuated. Similar results are observed in a spiking ring model. Finally, we remap the perturbations in orientation back into the 2-D image space using Gabor functions. The resulted optimal perturbation patterns mirror adversarial attacks in deep learning that exploit the priors of the system. Our results suggest that based on different state configurations, these priors could underlie some of the illusionary experiences as the cost of visual robustness.