Recording Large-scale Neuronal Ensembles with Silicon Probes in the Anesthetized Rat
Bibliographic record
Abstract
Large scale electrophysiological recordings from neuronal ensembles offer the opportunity to investigate how the brain orchestrates the wide variety of behaviors from the spiking activity of its neurons. One of the most effective methods to monitor spiking activity from a large number of neurons in multiple local neuronal circuits simultaneously is by using silicon electrode arrays. Action potentials produce large transmembrane voltage changes in the vicinity of cell somata. These output signals can be measured by placing a conductor in close proximity of a neuron. If there are many active (spiking) neurons in the vicinity of the tip, the electrode records combined signal from all of them, where contribution of a single neuron is weighted by its 'electrical distance'. Silicon probes are ideal recording electrodes to monitor multiple neurons because of a large number of recording sites (+64) and a small volume. Furthermore, multiple sites can be arranged over a distance of millimeters, thus allowing for the simultaneous recordings of neuronal activity in the various cortical layers or in multiple cortical columns (Fig. 1). Importantly, the geometrically precise distribution of the recording sites also allows for the determination of the spatial relationship of the isolated single neurons. Here, we describe an acute, large-scale neuronal recording from the left and right forelimb somatosensory cortex simultaneously in an anesthetized rat with silicon probes (Fig. 2).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".