AN IMPROVED ARCHITECTURE FOR COMPETITIVE AND COOPERATIVE NEURONS (CCNS) IN NEURAL NETWORKS
Bibliographic record
Abstract
In neural networks, the associative memory is one in which applying some input pattern leads to the response of a corresponding stored pattern. During the learning phase the memory is fed with a number of input vectors and in the recall phase when some known input is presented to it, the network recalls and reproduces the output vector. Here, we improve and increase the storing ability of the memory model proposed in [1]. We show that there are certain instances where their algorithm can not produce the desired performance by retrieving exactly the correct vector. That is, in their algorithm, a number of output vectors can become activated from the stimulus of an input vector while the desired output is just a single vector. Our proposed solution overcomes this and uniquely determines the output vector as some input vector is applied. Thus we provide a more general scenario of this neural network memory model consisting of Competitive Cooperative Neurons (CCNs).
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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 | 0.001 | 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".