Modeling of neural decoder based on binary spiking neurons in DEVS
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Bibliographic record
Abstract
Presented here is the simulation of specific application of reported earlier Binary Spiking Neurons for implementation of the Binary Neural Spiking Decoder, thus attaining next level in hierarchy of the Brain Machine devices based on the binary spiking neurons. This further extends the simulation of selected elements of Brain Machine in DEVS environment employing CD++ toolkit. Targeted applications include development of high throughput communication channels, employing spike encoding -- decoding technique at high frequencies. Neural decoder based on binary spiking neurons is chosen for modeling in DEVS formal definitions as top model, which in turn is using previously reported atomic and coupled model associated with binary spiking neurons. In this application the signal of the encoded in ternary alphabet test messages of spike sequences is employed to verify functionality of the resulting spiking neural decoder. Spike sequences are split between two channels -- one for initiating spikes and another one for terminating ones. Binary spiking neurons, which by definition have a rectangular response function, are considered in the presented model. Firing condition for the binary spiking neuron is reached when two rectangular responses, one for the initiating spike and another one for terminating spike, overlap in time domain, as a result producing 1 at the neuron's output (firing signal) or alternatively 0 (non-firing output signal).
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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