Signal processing by single neurons : biophysical mechanisms and implications for nociception
Bibliographic record
Abstract
Neurons transform analogue input into digital representations (i.e. spike trains) that can be communicated to other neurons. In the process of incorporating information from different sources, representations are modified. These modifications constitute computations. This is particularly relevant in the pain system where, according to the gate control theory, sensory input from multiple modalities as well as cognitive information influence the relationship between noxious input and pain perception. Much of this control is exerted in the spinal dorsal horn, including lamina I, but the biophysical basis for those computations remains unknown. This thesis aims to elucidate basic computational processes involved in signal processing by single neurons, especially as they relate to nociceptive processing by neurons in lamina I. Intrinsic cellular properties of lamina I neurons were characterized using a spinal slice preparation and whole cell patch clamp recordings. The neuron population is divisible into four classes on the basis of spiking pattern. Neurons from different classes encode information in fundamentally different ways. Subsequent investigation revealed that tonic neurons act as integrators because of voltage-dependent inward currents that prolong subthrehsold depolarizations and allow repetitive spiking. Single spike neurons, on the other hand, act as coincidence detectors because of their predominant outward current. Encoding mode (integration vs. coincidence detection) determines the information transmitted by each cell type. Inhibition is also known to play an important role in nociceptive processing. It has been debated whether shunting inhibition is able to divisively modulate firing rate (i.e. implement gain control). Results presented here demonstrate that divisive modulation by shunting inhibition depends on background synaptic noise and dendritic saturation. Further analysis reveals that effects of noise can be understood o
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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 | 0.001 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".