Selective Input Sparsity in Spiking Neural Networks for Pattern Classification
Why this work is in the frame
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Bibliographic record
Abstract
The concept of input sparsity in Spiking Neural Networks for pattern recognition is introduced and explored with the goals of reductions in network inference time and size, leading to lower resource requirements in hardware implementations. A method is proposed by which selective input sparsity can be inferred from the training set to reduce the size of the network before training and decrease the network inference time. This method also requires no additional pre-processing steps during the testing phase, making it an excellent candidate for edge applications. For a basic fully connected spiking neural network trained to solve the MNIST handwritten digits, selective input sparsity is applied and the network size is reduced by 58.16% and a 41.07% decrease in the network's inference time is observed without notable accuracy hinderance. In the case of the Fashion MNIST dataset, selective input sparsity reduced the network size by 55.99% and reduced the network's inference time by 59.05%.
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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 it