Dual exploration strategies using artificial spiking neural networks in a robotic learning task
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
Spatial information can be valuable, but new environments may be perceived as risky and thus often evoke fear responses and risk-averse exploration strategies such as thigmotaxis or wall-following behavior. Individual differences in risk-taking (boldness) and thigmotaxis have been reported in natural taxa, which may benefit their survival. In neurorobotic, the common approach is to reproduce cognitive phenomena with multiple levels of bio-inspiration into robotic scenarios. Since autonomous robots may benefit from these different behaviors in exploration tasks, this study aims at simulating two exploration strategies in a virtual robot controlled by a spiking neural network. The experimental context consists in a visual learning task solved through an operant conditioning procedure. Results suggest that the proposed neural architecture sustains both behaviors, switching from one to the other by external cues. This original bio-inspired model could be used as a first step toward further investigations of neurorobotic personality modulated by learning and complex exploration contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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