Bibliographic record
Abstract
Sensory stimuli typically activate many receptors at once and therefore should lead to increases in correlated activity among central neurons. Such correlated activity could be a critical feature in the encoding and decoding of information in central circuits. Here we characterize correlated activity in response to two biologically relevant classes of sensory stimuli in the primary electrosensory nuclei, the electrosensory lateral line lobe, of the weakly electric fish Apteronotus leptorhynchus. Our results show that these neurons can display significant correlations in their baseline activities that depend on the amount of receptive field overlap. A detailed analysis of spike trains revealed that correlated activity resulted predominantly from a tendency to fire synchronous or anti-synchronous bursts of spikes. We also explored how different stimulation protocols affected correlated activity: while prey-like stimuli increased correlated activity, conspecific-like stimuli decreased correlated activity. We also computed the correlations between the variabilities of each neuron to repeated presentations of the same stimulus (noise correlations) and found lower amounts of noise correlation for communication stimuli. Therefore the decrease in correlated activity seen with communication stimuli is caused at least in part by reduced noise correlations. This differential modulation in correlated activity occurred because of changes in burst firing at the individual neuron level. Our results show that different categories of behaviorally relevant input will differentially affect correlated activity. In particular, we show that the number of correlated bursts within a given time window could be used by postsynaptic neurons to distinguish between both stimulus categories.
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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 | 0.001 |
| 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".