Burst firing regulates correlated activity in neurons
Bibliographic record
Abstract
Understanding how sensory information is encoded by populations of neurons is complicated by the fact that neurons display variability in their responses to repeated presentations of the same stimulus. A further complication comes from the fact that these variabilities can be correlated. In fact, the role of correlations in neural variabilities (noise correlations) has been the focus of much debate in recent years as even a small amount of correlation between a pair of neurons can have dramatic effects on information transmission by neural populations [1]. Recent experimental evidence has shown that burst firing can modulate the amount of noise correlations displayed by neural populations [2]. Here we investigate the role of intrinsic bursting dynamics in model neurons receiving correlated input. These model neurons transition from a tonic firing regime to a bursting regime as the amount of depolarizing current is varied. We find that, given an input correlation c, the output correlation R between neural pairs in the network is less when both neurons are in bursting regime than when they are in the tonic regime. Our theoretical results are supported by experimental results obtained from a slice preparation. We show that intrinsic burst dynamics can decorrelate neural populations and therefore regulate their information transmission properties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".