Overcoming the space clamp effect: Reliable recovery of local and effective synaptic conductances of neurons
Bibliographic record
Abstract
Neurons process information by integrating thousands of synaptic inputs along their dendrites. Understanding the computational principles underlying neuronal information processing requires a reliable measure of synaptic conductance dynamics that accurately represents the input sources before signal integration and processing. Prevailing approaches to measuring synaptic conductances typically employ a voltage clamp at the soma of a neuron and assume the neuron as an isopotential point when processing electrical signals. However, owing to the presence of the well-known space clamp effect, the measurement of synaptic conductances through these methods often leads to significant errors, impeding the elucidation of dendritic signal features and subsequent signal integration processes. To address this issue, here we first develop a two-step clamp method at the soma that separately recovers the mean and time constant information of local synaptic conductance on the dendrite with high accuracy when a neuron receives a single synaptic input. Furthermore, under in vivo conditions of multiple synaptic inputs, we propose an intercept method to extract effective net excitatory and inhibitory synaptic conductances from measurements of synaptic currents at the soma. Both methods are grounded in mathematical perturbation analyses of a conductance-based passive cable model and are validated across multiple biologically detailed multicompartment neuron models with active channels, including Purkinje neuron, pyramidal neuron, and fast-spiking interneuron. Results demonstrate that our methods effectively circumvent the space clamp effect, offering reliable means to assess the role of measured conductances and synaptic activity in neuronal information processing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".