Grid-SD2E: A General Grid-Feedback in a System for Cognitive Learning
This is an incremental theoretical model aimed at neuroscience and cognitive science researchers to explain brain interactions, but it lacks empirical validation.
The paper tackles the challenge of integrating theoretical models of brain-world interaction by proposing Grid-SD2E, a cognitive system combining a grid module with Bayesian reasoning for space-division and exploration-exploitation, but it does not report concrete experimental results or numerical improvements.
Comprehending how the brain interacts with the external world through generated neural data is crucial for determining its working mechanism, treating brain diseases, and understanding intelligence. Although many theoretical models have been proposed, they have thus far been difficult to integrate and develop. In this study, we were inspired in part by grid cells in creating a more general and robust grid module and constructing an interactive and self-reinforcing cognitive system together with Bayesian reasoning, an approach called space-division and exploration-exploitation with grid-feedback (Grid-SD2E). Here, a grid module can be used as an interaction medium between the outside world and a system, as well as a self-reinforcement medium within the system. The space-division and exploration-exploitation (SD2E) receives the 0/1 signals of a grid through its space-division (SD) module. The system described in this paper is also a theoretical model derived from experiments conducted by other researchers and our experience on neural decoding. Herein, we analyse the rationality of the system based on the existing theories in both neuroscience and cognitive science, and attempt to propose special and general rules to explain the different interactions between people and between people and the external world. What's more, based on this framework, the smallest computing unit is extracted, which is analogous to a single neuron in the brain.