Input-Layer Neuron Implementation Using Delta-Sigma Modulators
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Bibliographic record
Abstract
In Spiking Neural Networks (SNNs), typical spiking neuron models use currents as inputs. In turn, synaptic circuits generate current pulses from the neural voltage spikes. In a neural network, the first layer serves as an interface between the external world and the remainder of the network. In this paper, we propose the use of the well-known ∆Σ encoding scheme as the basis in the design of two input-layer neuron circuits. Their purpose is to convert analog sensor voltages into spike trains with firing rates that are linearly proportional to the input voltage. We use simple available circuits: a 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> -order ∆Σ modulator, D-flipflops, a differential-pair synapse, and an Integrate-and-Fire (IF) neuron. These input-layer neurons can be implemented on the same IC as the rest of the SNN, and are capable of encoding values over a wide range of inputs.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it