Distal Gap Junctions and Active Dendrites Can Tune Network Dynamics
Bibliographic record
Abstract
Gap junctions allow direct electrical communication between CNS neurons. From theoretical and modeling studies, it is well known that although gap junctions can act to synchronize network output, they can also give rise to many other dynamic patterns including antiphase and other phase-locked states. The particular network pattern that arises depends on cellular, intrinsic properties that affect firing frequencies as well as the strength and location of the gap junctions. Interneurons or GABAergic neurons in hippocampus are diverse in their cellular characteristics and have been shown to have active dendrites. Furthermore, parvalbumin-positive GABAergic neurons, also known as basket cells, can contact one another via gap junctions on their distal dendrites. Using two-cell network models, we explore how distal electrical connections affect network output. We build multi-compartment models of hippocampal basket cells using NEURON and endow them with varying amounts of active dendrites. Two-cell networks of these model cells as well as reduced versions are explored. The relationship between intrinsic frequency and the level of active dendrites allows us to define three regions based on what sort of network dynamics occur with distal gap junction coupling. Weak coupling theory is used to predict the delineation of these regions as well as examination of phase response curves and distal dendritic polarization levels. We find that a nonmonotonic dependence of network dynamic characteristics (phase lags) on gap junction conductance occurs. This suggests that distal electrical coupling and active dendrite levels can control how sensitive network dynamics are to gap junction modulation. With the extended geometry, gap junctions located at more distal locations must have larger conductances for pure synchrony to occur. Furthermore, based on simulations with heterogeneous networks, it may be that one requires active dendrites if phase-locking is to occur in networks formed with distal gap junctions.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".