Transient Signals Trigger Synchronous Bursts in an Identified Population of Neurons
Bibliographic record
Abstract
It is an important task in neuroscience to find general principles that relate neural codes to the structure of the signals they encode. The structure of sensory signals can be described in many ways, but one important categorization distinguishes continuous from transient signals. We used the communication signals of the weakly electric fish to reveal how transient signals (chirps) can be easily distinguished from the continuous signal they disrupt. These communication signals-low-frequency sinusoids interrupted by high-frequency transients-were presented to pyramidal cells of the electrosensory lateral line lobe (ELL) during in vivo recordings. We show that a specific population of electrosensory neurons encodes the occurrence of the transient signal by synchronously producing a burst of spikes, whereas bursting was neither common nor synchronous in response to the continuous signal. We also confirmed that burst can be triggered by low-frequency modulations typical of prey signals. However, these bursts are more common in a different segment of the ELL and during spatially localized stimulation. These localized stimuli will elicit synchronized bursting only in a restricted number of cells the receptive fields of which overlap the spatial extent of the stimulus. Therefore the number of cells simultaneously producing a burst and the ELL segment responding most strongly may carry the information required to disambiguate chirps from prey signals. Finally we show that the burst response to chirps is due to a biophysical mechanism previously characterized by in vitro studies of electrosensory neurons. We conclude that bursting and synchrony across cells are important mechanisms used by sensory neurons to carry the information about behaviorally relevant but transient signals.
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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 | 0.000 |
| 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 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".