Principles of visual cortex excitatory microcircuit organization
Bibliographic record
Abstract
ABSTRACT Microcircuit function is determined by patterns of connectivity and short-term plasticity that vary with synapse type. Elucidating microcircuit function therefore requires synapse-specific investigation. The state of the art for synapse-specific measurements has long been paired recordings. Although powerful, this method is slow, leading to a throughput problem. To improve yield, we therefore created optomapping — an approximately 100-fold faster 2-photon optogenetic method — which we validated with paired-recording data. Using optomapping, we tested 15,433 candidate excitatory inputs to find 1,184 connections onto pyramidal, basket, and Martinotti cells in mouse primary visual cortex, V1. We measured connectivity, synaptic weight, and short-term dynamics across the V1 layers. We found log-normal synaptic strength distributions, even in individual inhibitory cells, which was previously not known. We reproduced the canonical circuit for pyramidal cells but found surprising and differential microcircuit structures, with excitation of basket cells concentrated to layer 5, and excitation of Martinotti cells dominating in layer 2/3. Excitation of inhibitory cells was denser, stronger, and farther-reaching than excitation of excitatory cells, which promotes stability and difference-of-Gaussian connectivity. We gathered an excitatory short-term plasticitome, which revealed that short-term plasticity is simultaneously target-cell specific and dependent on presynaptic cortical layer. Peak depolarization latency in pyramidal cells also emerged as more heterogeneous, suggesting heightened sensitivity to redistribution of synaptic efficacy. Optomapping additionally revealed high-order connectivity patterns including shared-input surplus for interconnected pyramidal cells in layer 6. Optomapping thus offered both resolution to the throughput problem and novel insights into the principles of neocortical excitatory fine structure. HIGHLIGHTS 2-photon optomapping of microcircuits is verified as fast, accurate, and reliable Synaptic weights distribute log-normally even for individual inhibitory neurons Maximal excitation of basket and Martinotti cells in layer 5 and 2/3, respectively Short-term plasticity depends on layer in addition to target cell
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".