Robotic implementation of classical and Operant Conditioning as a single STDP learning process
Bibliographic record
Abstract
A robot is presented whose behavior is based on two fundamental types of learning in the animal world: Classical Conditioning (CC) and Operant Conditioning (OC). It is shown how both share Spike-Timing-Dependent-Plasticity (STDP) as learning process for a Spiking Neural Network (SNN). STDP was implemented on a Field-Programmable Gate Array (FPGA) with very low-demanding resources, using an adaptation of the Synapto-dendritic Kernel Adapting Neuron (SKAN) model. Moreover, it is shown how a 3-way version of STDP is needed to allow for OC. The robot was designed to use the CC and OC neuronal architectures proposed in this paper and was tested in a dynamic environment, which consisted of a maze with changing features. It was successful in presenting both types of learning. This paper thus validates an architecture with an important potential for very large scale time-dependent parallel data analysis, with high capacity of adaptation in a dynamic environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".