Synchrony-Based State Representation for Classification by Liquid State Machines
Bibliographic record
Abstract
The Liquid State Machine (LSM) models usually ignore the influence of the liquid state representation on performance, with the assumption that the latter depends only on the readout circuit. The typical decoding of the liquid’s spike trains is achieved with spike rate-based vectors that are input into the readout circuit. This occults the spike timing, a central aspect of biological neural coding, with potentially detrimental consequences on performance. We propose a model of liquid state representation that builds the feature vectors from the temporal information about the spike trains, hence using spike synchrony instead of rate. Using Poisson-distributed spike trains in noisy conditions, we show that such model outperforms a rate-only model in distinguishing spike train pairs, regardless of the frequency chosen to sample the liquid state or the noise level. In the same vein, we suggest a synchrony-based measure of the Separation Property (SP), a core feature of LSMs regarding classification performance, for a more robust and biologically plausible interpretation.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".