Implementation of the Neural Engineering Framework on the TrueNorth Neurosynaptic System
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Bibliographic record
Abstract
The Neural Engineering Framework (NEF) provides a methodology for implementing algorithms and models using spiking neurons. Although it is possible to run simulations based on the NEF on Von Neumann hardware, neuromorphic hardware holds the promise of increased computational efficiency and lower power implementation. This work describes an implementation of the NEF on IBM's TrueNorth Neurosynaptic system. Using one TrueNorth chip, a NEF neural population of 629 neurons representing five dimensions is demonstrated on hardware. However, the crossbar array architecture itself, utilized in the TrueNorth hardware, can be used to compute the basic NEF calculations for any sized neural population, representing any dimensionality. The computation time is a function of the maximum values used in the computations.
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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