Robotic implementation of classical and operant conditioning within a single SNN architecture
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
This work presents the implementation of operant conditioning (OC) and classical conditioning (CC) with a single spiking neural network (SNN) architecture, thus suggesting that the two types of leaning may relate to the same cognitive process. Both are achieved by using a modified version of spike-timing-dependent plasticity (STDP), where the connection weight between a cue neuron and an action neuron depends on the temporal relation between their spikes and those of a reward neuron. This reward driven STDP (RD-STDP) was implemented with simple computational resources to form an electronic robot's brain, using an adaptation of the synapto-dendritic kernel adapting neuron (SKAN) model. Then, a robot driven by the new neuronal architecture was tested in a maze with changing features, successfully exhibiting CC and OC. These results and the simple computational resources used make the proposed architecture promising for very large scale time-dependent parallel data analysis, with high capacity of adaptation in a dynamic environment. Moreover, it proposes a theoretic framework to model learning by conditioning.
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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