A survey of neurophysiological differentiation across mouse visual brain areas and timescales
Bibliographic record
Abstract
Abstract Neurophysiological differentiation (ND), a metric that quantifies the number of distinct activity states that the brain or its part visits over a period of time, has been used as a correlate of meaningfulness or subjective perception of visual stimuli. ND has largely been studied in non-invasive human whole-brain recordings where spatial resolution is limited. However, it is likely that perception is supported by discrete populations of spiking neurons rather than the whole brain. Therefore, in this study, we use Neuropixels recordings from the mouse brain to characterize the ND metric within neural populations recorded at single-cell resolution in localized regions. Using the spiking activity of thousands of simultaneously recorded neurons spanning 6 visual cortical areas as well as the visual thalamus, we show that the ND of stimulus-evoked activity of the entire visual cortex is higher for naturalistic stimuli relative to artificial ones. This finding holds in most individual areas throughout the visual hierarchy as well. For animals performing an image change detection task, ND of the entire visual cortex (though not individual areas) is higher for successful detection compared to failed trials, consistent with the assumed perception of the stimulus. Analysis of spiking activity allows us to characterize the ND metric across a wide range of timescales from 10s of milliseconds to a few seconds. This analysis reveals that although ND of activity of single neurons is often maximized at an optimal timescale around 100 ms, the optimum shifts to under 5 ms for ND of neuronal ensembles. Finally, we find that the ND of activations in convolutional neural networks (CNNs) trained on an image classification task shows distinct trends relative to the mouse visual system: ND is often higher for less naturalistic stimuli and varies by orders of magnitude across the hierarchy, compared to modest variation in the mouse brain. Together, these results suggest that ND computed on cellular-level neural recordings can be a useful tool highlighting cell populations that may be involved in subjective perception. Summary Advances in our understanding on neural coding has revealed that information about visual stimuli is represented across several brain regions. However, availability of information does not imply that it is necessarily utilized by the brain, much less that it is subjectively perceived. Since percepts originate in neural activity, distinct percepts must be associated with distinct ‘states’ of neural activity, at least within the brain region that supports the percepts. Thus, one approach developed in this direction is to quantify the number of distinct ‘states’ that the activity of the brain goes through, called neurophysiological differentiation (ND). ND of the entire brain has been shown to reflect subjective reports of visual stimulus meaningfulness. But what specific subpopulations within the brain could be supporting conscious perception, and what is the correct timescale on which states should be quantified? In this study, we analyze ND of spiking neural activity in the mouse visual cortex recorded using Neuropixels probes, allowing us to characterize the ND metric across a wide range of timescales all the way down from 5 ms to a few seconds. It also allows us to understand the ND of neural activity of different ensembles of neurons, from individual thalamic or cortical ensembles to those spanning across multiple visual areas in the mouse brain.
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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 | 0.001 | 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".