Input-Specific Organization of Intrinsic Excitability Expands Coding Capacity of Fast-Spiking Auditory Neurons
Bibliographic record
Abstract
Heterogeneity of presynaptic input and postsynaptic intrinsic excitability are two major variables that regulate neuronal firing rates and patterns. Yet, little is known about how these variables interplay to diversify the fidelity of excitation-spike coupling. To investigate their reciprocal relationship, we took advantage of the one-to-one innervation of mature calyx of Held-principal neuron synapses at the medial nucleus of the trapezoid body (MNTB) in the auditory brainstem of male and female mice. Given that sustainability of synaptic drive is directly correlated with the morphological complexity of presynaptic calyces, we characterized the intrinsic excitability of postsynaptic neurons with morphologically identified inputs. We discovered that morphologically simple calyces (stalks and ≤10 swellings) providing weaker synaptic drive preferentially innervate principal neurons that exhibit lower stimulation-spike coupling fidelity and display phasic firing patterns, while neurons contacted by complex calyces (stalks and >20 swellings) providing stronger synaptic drive exhibit higher stimulation-spike coupling fidelity and are predominantly associated with tonic firing. Phasic and tonic firing neurons have similar action potential shape and composition of low-threshold Kv1 and high-threshold Kv3 potassium currents but display marked differences in their input resistance and resting potassium conductance. Our results support a model in which a postsynaptic gradient of leak potassium channel density complements the presynaptic morpho-functional continuum to create an extended dynamic range of MNTB outputs. This synergy expands the coding capacity within a single population of neurons and supports multiple streams of auditory 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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".