Predicting synchrony and asynchrony in basket cell networks coupled by multiple dendritic gap junctions
Bibliographic record
Abstract
Basket cells in hippocampus form mutually inhibitory networks and target perisomatic regions of the output pyramidal cell population. Furthermore, they are major players in producing gamma rhythms both in vitro and in vivo [e.g., [2,3]]. Network models incorporating experimentally derived synaptic characteristics produce robust and coherent gamma oscillations, thus suggesting that synchronous output from basket cell networks are important contributors to gamma rhythms. In addition to inhibitory synapses, PV+ basket cells are electrically coupled with gap junctions at multiple locations between their apical and basal dendrites, several hundred microns from their somata [4]. Given that direct electrical communication between neurons plays an important role in shaping network output, it is important to understand how non-proximally located gap junctions contribute to produce synchronous output in basket cell networks. In a previous study we built compartmental models of basket cells with active dendrites and showed that when gap junctions are located distally, there could be sensitive tuning of network dynamics with changes in gap junction conductances [5]. In the work here we built compartmental models of basket cells with different distributions of ion channels in basal and apical dendrites, and explored the dynamics of two-cell networks coupled at non-proximal locations. We first compared full (372 compartments) and reduced (3 compartments) compartmental models to define synchronous and asynchronous regimes. We then quantified phase response curve characteristics in terms of their skewness to predict the network dynamics (synchronous or asynchronous) of reduced models using weakly coupled oscillatory theory. We found that the predictions from quantified phase response curves also held reasonably well when the full compartmental models were coupled at basal or apical dendrites. We next built two-cell networks that were coupled at two and three locations, and computed the average of each of the phase response curves obtained for the different coupling sites. We found that the quantification applied to the averaged phase response curves correctly predicted the network output. This suggests that quantification of phase response curves can be used to predict the output of networks that are coupled with gap junctions at more than one location.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".