Bibliographic record
Abstract
We create a Spiking Neural Network (SNN) architecture based on transforming the dynamics – Unstable Periodic Orbits (UPOs) – of a chaotic spiking neuron model to Neuronal Groups composed from Resonant Neurons. An input fed to the SNN will activate one of its neuronal groups. An activated neuronal group represents ‘memory’ or a neural state of the SNN. By exploiting a fundamental principle in chaos theory, which is Chaotic Sensitivity upon Initial Conditions, in conjunction with chaos control, we show that similar inputs, when fed separately to the SNN, will always activate the same neuronal group and different inputs will activate different neuronal groups. In addition, we show that differences between the system responses (i.e. neuronal groups) are proportional to differences between inputs. These features make the system suitable for input discrimination; we give an example of discerning human physical actions. More importantly, we study the capacity of the SNN. We show that the number of neuronal groups that can be reached is extremely large; it grows exponentially with the increase of the network size (i.e. number of neurons). This is due to neurons mixing, which allows the same resonant neuron to belong to other neuronal groups and due to the theoretically infinite number of UPOs available in a chaotic system that can be stabilized through chaos control. Also, our work competes with Izhikevich's polychronous groups, so we compare our results to his. We discuss the relevance of the work in the nonlinear sciences and its relation to chaotic neuro-dynamics, cognitive science, neural computation, machine learning and memory modeling including future considerations and open problems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".