FPGA minimal components SKAN model for classical and operant conditioning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We show how a minimal components requirement and very low resource demanding field-programmable gate array (FPGA) implementation of an adapted version of the synapto-dendritic Kernel Adapting Neuron (SKAN) model can be used to underlie two of the most basic learning processes: classical conditioning (CC) and operant conditioning (OC). In the CC architecture, this adapted SKAN model is used in a spiking neural network (SNN) to implement spike-timing-dependant-plasticity (STDP). However, in order to achieve a functioning OC architecture, a new STDP-inspired learning process is introduced. The modified CC architecture, new OC architecture, adapted SKAN model and new STDP-inspired process represent the four contributions presented here, along with simulation results on a FPGA which shows their adequacy in supporting CC and OC learning behaviors.
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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