Using Bidirectional Associative Memory Neural Networks to Solve the N-bit Task
Bibliographic record
Abstract
Nowadays, artificial neural networks can easily solve the N-bit parity problem. However, each time a different level must be learned, the network must be retrained. This, combined with the exponential increase of learning trials required as N grows, make these models too different from how their biological counterpart solves them. This is because humans learn to recognize patterns, count, and determine if numbers are odd or even. Once they have learned these tasks, they can have them interact to solve any level without further training. This behavior is akin to performing multiple associations of different tasks. Therefore, it is proposed that by using bidirectional associative memory neural networks, it would be possible to solve the N-bit parity problem in a similar fashion to humans. To achieve this, two networks interacted; one served as a task Identifier and the other as a memory Extractor, giving the desired behavior influenced by the Identifier. Results showed that the model could solve the 2- to 9-bit in linear time once the associations were learned. Moreover, this was possible with 97% fewer inputs and no retraining. In addition, because of the recurrent nature of the model, it could also solve the tasks even under high noise levels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".