Low throughput screening in neuroscience: using light to study central synapses one at a time
Bibliographic record
Abstract
Neurophotonic approaches have fostered substantial progress in our understanding of the brain by providing an assortment of means to either monitor or manipulate neural processes. Among these approaches, the development of two-photon uncaging provides a useful and flexible approach to manipulate the activity of individual synapses. In this short piece, we explore how this technique has emerged at the intersection of chemistry, optics, and electrophysiology to enable spatially and temporally precise photoactivation for studying functional aspects of synaptic transmission and dendritic integration. We discuss advantages and limitations of this approach, focusing on our efforts to study several functional aspects of glutamate receptors using uncaging of glutamate. Among other advancements, this approach has contributed to further our understanding of the subcellular regulation, trafficking, and biophysical features of glutamate receptors (e.g., desensitization and silent synapse regulation), the dynamics of spine calcium, and the integrative features of dendrites, and how these functions are altered by several forms of plasticity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".