AB062. Cortical state contribution to neuronal response variability
Bibliographic record
Abstract
Background: Visual cortex neurons often respond to stimuli very differently on repeated trials. This trial-by-trial variability is known to be correlated among nearby neurons. Our long-term goal is to quantitatively estimate neuronal response variability, using multi-channel local field potential (LFP) data from single trials. Methods: Acute experiments were performed with anesthetized (Remifentanil, Propofol, nitrous oxide) and paralyzed (Gallamine Triethiodide) cats. Computer-controlled visual stimuli were displayed on a gamma-corrected CRT monitor. For the principal experiment, two kinds of visual stimuli were used: drifting sine-wave gratings, and a uniform mean-luminance gray screen. These two stimuli were each delivered monocularly for 100 sec in a random order, for 10 trials. Multi-unit activity (MUA) and LFP signals were extracted from broadband raw data acquired from Area 17 and 18 using A1X32 linear arrays (NeuroNexus) and the OpenEphys recording system. LFP signal processing was performed using Chronux, an open-source MATLAB toolbox. Current source density (CSD) analysis was performed on responses to briefly flashed full-field stimuli using the MATLAB toolbox, CSDplotter. The common response variability (global noise) of MUA was estimated using the model proposed by Scholvinck et al. [2015]. Results: On different trials, a given neuron responded with different firing to the same visual stimuli. Within one trial, a neuron’s firing rate also fluctuated across successive cycles of a drifting grating. When the animal was given extra anesthesia, neurons fired in a desynchronized pattern; with lighter levels of anesthesia, neuronal firing because more synchronized. By examining the cross-correlations of LFP signals recorded from different cortical layers, we found LFP signals could be divided to two groups: those recorded in layer IV and above, and those from layers V and VI. Within each group, LFP signals recorded by different channels are highly correlated. These two groups were observed in lighter and deeper anesthetized animals, also in sine-wave and uniform gray stimulus conditions. We also investigated correlations between LFP signals and global noise. Power in the LFP beta band was highly correlated with global noise, when animals were in deeper anesthesia. Conclusions: Brain states contribute to variations in neuronal responses. Raw LFP correlation results suggest that we should analyze LFP data according to their laminar organization. Correlation of low-frequency LFP under deeper anesthesia with global noise gives us some insight to predict noise from single-trial data, and we hope to extend this analysis to lighter anesthesia in the future.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".