Hardware realization of GALS based cortical column systems
Bibliographic record
Abstract
Many researchers seek for alternatives to traditional computing architectures, often placing emphasis on modeling biological systems that possess intelligence and learning capabilities. Cortical columns have emerged as a high-level unsupervised learning model for extracting independent data features in a hierarchical manner. Previous cortical models simply rely on software based techniques and neglect the actual purpose of investigating these architectures: to diverge from the conventional Von Neumann approach to an actual hardware realization of an intelligent system. This work presents the hardware realization of a Globally Asynchronous Locally Synchronous (GALS) cortical column system, taking hardware based factors into consideration which were previously disregarded by software models. We introduce a Neural Spike Dual-Rail encoding scheme for GALS communication, and a temporal pooling unit capable of detecting data distortions during training and testing. This work concludes with a study on cortical columns and their hierarchical impact on hardware resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".