Improved Spiking Neural Networks with multiple neurons for digit recognition
Bibliographic record
Abstract
For more than a decade Deep Learning, a subset of machine learning have been using for many applications such as forecasting, data visualization, classification etc. However, it consumes more energy and also takes longer training periods for computation, when compared to human brain. In most cases, it is difficult to reach human level performance. With the recent technological improvements in neuroscience and thanks to neuromorphic computing, we now can achieve higher classification efficacy for producing the desired outputs with considerably lower power consumption. Latest advancements in brain simulation technologies has given a breakthrough for analysing and modelling brain functions. Despite its advancements, this research remains undiscovered due to lack of coordination between neuroscientists, electronics engineers and computer scientists. Recent progress in Spiking Neural Networks(SNN) led towards integration different fields under one single roof. Biological neurons inside human brain communicate with each other through synapses. Similarly, bio-inspired synapses in the neuromorphic model mimic the biological neuro synapses for computing. In this novel research, we have modelled a supervised Spiking Neural Network algorithm using Leaky Integrate and Fire (LIF), Izhikevich and rectified linear neurons and tested its spike latency under different conditions. Furthermore, these SNN models are tested on the MNIST dataset to classify the handwritten digits, and the results are compared with the results of the Convolutional Neural Network (CNN).
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".