Neuronal Spatial Arrangement Shapes Effective Connectivity Traits of <i>in vitro</i> Cortical Networks
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Bibliographic record
Abstract
We studied effective connectivity in rat cortical cultures with various degrees of spatial aggregation, ranging from homogeneous networks to highly aggregated ones. We considered small cultures 3 mm in diameter and that contained about 2;000 neurons. Spatial inhomogeneity favored an increase of metric correlations and connectivity among neighboring neurons. Effective connectivity was determined from spontaneous activity recordings using calcium fluorescence imaging. We used generalized transfer entropy as tool to infer the effective connectivity. We carried out numerical simulations to build networks that mimicked the experimental ones and to test the reliability of the connectivity-inference algorithm. Effective connectivity traits were investigated during the development of the cultures over two weeks, and along the gradual blockade of excitatory connections through CNQX. We observed that the average effective connectivity rapidly increased during culture development. At day in vitro (DIV) 15 the average excitatory in-degree was measured as ≃k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</sup> 50 for homogeneous and semi aggregated networks, and ≃k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</sup> 120 for aggregated ones, and with 20 percent inhibition. Aggregated cultures exhibited assortative traits and a high resilience to chemical damage, while the other cultures were dissassortative or neutral, and less resilient. Our work illustrates the role of metric correlations in spatially embedded networks in shaping connectivity and activity traits in living neuronal networks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it