Coding Method Based on Fuzzy C-Means Clustering for Spiking Neural Network With Triangular Spike Response Function
Bibliographic record
Abstract
Although spiking neural network (SNN) has the advantages of strong brain-likeness and low energy consumption due to the use of discrete spikes for information representation and transmission, its performance still needs to be improved. This article improves SNN in terms of the coding process and the spike response function by invoking fuzzy sets. In terms of coding, a new fuzzy C-means coding (FCMC) method is proposed, which breaks the limitation of uniformly distributed receptive fields of existing coding methods and automatically determines suitable receptive fields that reflect the density distribution of the input data for encoding through the fuzzy C-means clustering. In terms of spike response function, triangular fuzzy numbers instead of the commonly used alpha-type function are used as the spike response function. Different from other functions of fixed shape, width parameters of the proposed function are learnt in the iterative way like weights of synapses do. Experimental results obtained on seven benchmark datasets and two real-world datasets with eleven approaches demonstrate that SNN with triangular spike response functions (abbreviated as T-SNN) combining FCMC can achieve improved performance in terms of accuracy, F-measure, AUC, required epochs, running time, and stability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".