A Unified Approach to Building and Controlling Spiking Attractor Networks
Why this work is in the frame
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Bibliographic record
Abstract
Extending work in Eliasmith and Anderson (2003), we employ a general framework to construct biologically plausible simulations of the three classes of attractor networks relevant for biological systems: static (point, line, ring, and plane) attractors, cyclic attractors, and chaotic attractors. We discuss these attractors in the context of the neural systems that they have been posited to help explain: eye control, working memory, and head direction; locomotion (specifically swimming); and olfaction, respectively. We then demonstrate how to introduce control into these models. The addition of control shows how attractor networks can be used as subsystems in larger neural systems, demonstrates how a much larger class of networks can be related to attractor networks, and makes it clear how attractor networks can be exploited for various information processing tasks in neurobiological systems.
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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