Dendritic Mechanisms for<i>In Vivo</i>Neural Computations and Behavior
Bibliographic record
Abstract
Dendrites receive the vast majority of a single neuron's inputs, and coordinate the transformation of these signals into neuronal output.Ex vivoand theoretical evidence has shown that dendrites possess powerful processing capabilities, yet little is known about how these mechanisms are engaged in the intact brain or how they influence circuit dynamics. New experimental and computational technologies have led to a surge in interest to unravel and harness their computational potential. This review highlights recent and emerging work that combines established and cutting-edge technologies to identify the role of dendrites in brain function. We discuss active dendritic mediation of sensory perception and learning in neocortical and hippocampal pyramidal neurons. Complementing these physiological findings, we present theoretical work that provides new insights into the underlying computations of single neurons and networks by using biologically plausible implementations of dendritic processes. Finally, we present a novel brain–computer interface task, which assays somatodendritic coupling to study the mechanisms of biological credit assignment. Together, these findings present exciting progress in understanding how dendrites are critical forin vivolearning and behavior, and highlight how subcellular processes can contribute to our understanding of both biological and artificial neural computation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".