CMOS implementation of analog Hebbian synaptic learning circuits
Why this work is in the frame
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Bibliographic record
Abstract
CMOS VLSI circuits for the implementation of analog Hebbian synapses with in situ learning have been designed, fabricated, and tested. Synaptic weights are stored as analog voltages on integrated linear capacitors located at each synapse. These analog synaptic circuits are more area-efficient than their digital equivalents, resulting in enormous information processing potential. Investigations show that neural network architectures, such as networks using Hebbian and contrastive Hebbian learning, can tolerate highly imperfect analog computational components. These networks can use their learning capability to compensate for component variations, making it possible to implement them using simple, silicon area-efficient circuits. The synaptic circuits described have been incorporated into a fully analog 600-synapse, 28000-transistor neural network to investigate their behavior in a medium-sized system.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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) | 0.001 | 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