# Grid-SD2E: A General Grid-Feedback in a System for Cognitive Learning

arXiv: https://arxiv.org/abs/2304.01844 · HTML: https://mlsanity.com/paper/2304.01844/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 86 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 9 of 9)

- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [The Forward-Forward Algorithm: Some Preliminary Investigations](https://mlsanity.com/paper/2212.13345/) (2022)
- [Active inference on discrete state-spaces: a synthesis](https://mlsanity.com/paper/2001.07203/) (2020)
- [Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution](https://mlsanity.com/paper/2210.08340/) (2022)
- [Active Inference in Robotics and Artificial Agents: Survey and Challenges](https://mlsanity.com/paper/2112.01871/) (2021)
- [The Quest for a Common Model of the Intelligent Decision Maker](https://mlsanity.com/paper/2202.13252/) (2022)
- [Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI](https://mlsanity.com/paper/2005.09687/) (2020)
- [What we are is more than what we do](https://mlsanity.com/paper/2102.04219/) (2021)
- [ViF-SD2E: A Robust Weakly-Supervised Method for Neural Decoding](https://mlsanity.com/paper/2112.01261/) (2021)

## Built on by (most cited first, 1 of 1)

- [An Algorithm Board in Neural Decoding](https://mlsanity.com/paper/2502.12536/) (2025)

## Abstract (arXiv)

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.