Selective consistency of recurrent neural networks induced by plasticity as a mechanism of unsupervised perceptual learning.

Understanding the mechanism by which the brain achieves relatively consistent information processing contrary to its inherent inconsistency in activity is one of the major challenges in neuroscience. Recently, it has been reported that the consistency of neural responses to stimuli that are presented repeatedly is enhanced implicitly in an unsupervised way, and results in improved perceptual consistency. Here, we propose the term "selective consistency" to describe this input-dependent consistency and hypothesize that it will be acquired in a self-organizing manner by plasticity within the neural system. To test this, we investigated whether a reservoir-based plastic model could acquire selective consistency to repeated stimuli. We used white noise sequences randomly generated in each trial and referenced white noise sequences presented multiple times. The results showed that the plastic network was capable of acquiring selective consistency rapidly, with as little as five exposures to stimuli, even for white noise. The acquisition of selective consistency could occur independently of performance optimization, as the network's time-series prediction accuracy for referenced stimuli did not improve with repeated exposure and optimization. Furthermore, the network could only achieve selective consistency when in the region between order and chaos. These findings suggest that the neural system can acquire selective consistency in a self-organizing manner and that this may serve as a mechanism for certain types of learning. Author summary: This study explores how the brain can achieve stable information processing despite its inherent variability. Here we introduced the concept of "selective consistency"—the brain's ability to enhance response consistency to stimuli experienced multiple times—and investigated how it is achieved in an unsupervised and self-organizing manner. Using a reservoir-based neural network model with Hebbian plasticity, we tested how it acquires selective consistency by exposing it to repeated white noise stimuli multiple times. Remarkably, our results demonstrate that the neural network can acquire this consistency quickly, with just five exposures to the stimuli. Additionally, the plastic neural network only achieved selective consistency when operating near the edge of chaos—a critical state maintaining a delicate balance between order and chaos. These findings suggest that a brain near criticality can self-organize its structure with plasticity to improve perceptual consistency without explicit supervision. This study underscores the potential for neural plasticity to drive selective consistency and suggests that such mechanisms may be fundamental to how the brain develops to achieve consistent information processing from experiences. [ABSTRACT FROM AUTHOR]