ENCODING CARRIER AMPLITUDE MODULATIONS VIA STOCHASTIC PHASE SYNCHRONIZATION
Bibliographic record
Abstract
The firings of excitable systems can phase lock to periodic forcing. In many situations however, the firings are separated by a random number of forcing cycles, even though they occur near a preferred phase of the forcing. Also, the associated interspike interval histograms display peaks over a continuous range of integer multiples of the forcing period, and the peak heights are a unimodal function of increasing multiples of the period. This paper focusses on these patterns of stochastic phase synchronization and their alteration by physiologically relevant stimuli that modulate the amplitude of the periodic forcing. Specifically, two regimes of the FitzHugh–Nagumo system exhibiting stochastic phase locking are considered. We discuss how internal noise, originating from e.g. synaptic or conductance fluctuations, must interact with either suprathreshold or subthreshold dynamics, and in some instances with subthreshold chaos, to produce such firing patterns. These responses to constant amplitude "carriers" are then compared to those from carriers with random band-limited amplitude modulations (AM's). The comparison is based on the mean firing rate, as well as phase synchronization computed using a suitably defined input–output phase difference. Further, using the stimulus reconstruction technique to characterize synchrony between random AM's and spikes, the internal noise is shown to help transmit information about random carrier AM's in the subthreshold and slightly suprathreshold cases. This transmission also depends nonmonotonically on the carrier frequency. Our results provide biophysical insight into the dynamics of neural signal encoding that combines a mean rate code on long time scales and a precise temporal code based on phase locking on the shorter time scale of the carrier period.
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.
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) | 0.001 | 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".