Regulation of Burst Dynamics Improves Differential Encoding of Stimulus Frequency by Spike Train Segregation
Bibliographic record
Abstract
Distinguishing between different signals conveyed in a single sensory modality presents a significant problem for sensory processing. The weakly electric fish Apteronotus leptorhynchus use electrosensory information to encode both low-frequency signals associated with environmental and prey signals and high-frequency communication signals between conspecifics. We identify a mechanism whereby the GABA(B) component of a feedback pathway to the electrosensory lobe is recruited to regulate the intrinsic burst dynamics and coding properties of pyramidal cells for these behaviorally relevant input signals. Through recordings in an in vitro slice preparation and a reduced model of pyramidal cells, we show that recruitment of dendritic GABA(B) currents can shift the timing of a backpropagating spike and its influence on an intrinsic burst mechanism. This regulation of burst firing alters the coding properties of pyramidal cells by improving the correlation of burst and tonic spikes with respect to low- or high-frequency components of complex stimuli. GABA(B) modulation of spike backpropagation thus improves the segregation of burst and tonic spikes evoked by simulated sensory input, allowing pyramidal cells to parcel the spike train into coding streams for the low- and high-frequency components. As the feedback pathway is predicted to be activated in circumstances where environmental and communication stimuli coexist, these data reveal a novel means by which inhibitory input can regulate spike backpropagation to improve signal segregation.
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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".