Simultaneous Bayesian Estimation of Excitatory and Inhibitory Synaptic Conductances by Exploiting Multiple Recorded Trials
Bibliographic record
Abstract
Advanced statistical methods have enabled trial-by-trial inference of the underlying excitatory and inhibitory synaptic conductances of the membrane potential recordings. Simultaneous inference of both excitatory and inhibitory conductances sheds light into the neural circuits underlying the neural activity and advances our understanding on neural information processing. Conventional Bayesian methods can infer excitatory and inhibitory synaptic conductances based on a single trial of observed membrane potential. However, if multiple recorded trials are available, this typically leads to suboptimal estimation because they neglect common statistics (of synaptic inputs) across trials. Here, we establish a new expectation maximization (EM) algorithm that improves such single-trial Bayesian methods by exploiting multiple recorded trials to extract common synaptic input statistics across the trials. In this paper, the proposed EM algorithm is embedded in parallel Kalman filters (KFs) and Particle filters (PFs) for multiple recorded trials to integrate their outputs to iteratively update the common synaptic input statistics. These statistics are then used to infer the excitatory and inhibitory synaptic conductances of individual trials. We demonstrate the superior performance of these multiple-trial Kalman Filter (MtKF) and Particle Filter (MtPF) methods relative to the corresponding single-trial methods. While relative estimation error of excitatory and inhibitory conductances is known to depend on the level of current injection into a cell, our numerical simulations using MtKF show that both excitatory and inhibitory condutances are reliably inferred using an optimal level of current injection. Finally, we validate the robustness and applicability of our technique through simulation studies and apply the MtKF algorithm to in-vivo data recorded from rat barrel cortex.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".