Modulation of Hippocampal and Amygdalar-Evoked Activity of Nucleus Accumbens Neurons by Dopamine: Cellular Mechanisms of Input Selection
Bibliographic record
Abstract
Inputs from multiple sites in the telencephalon, including the hippocampus and basolateral amygdala (BLA), converge on neurons in the nucleus accumbens (NAc), and dopamine (DA) is believed to play an essential role in the amplification and gating of these different limbic inputs. The present study used extracellular single-unit recordings of NAc neurons in combination with chronoamperometric sampling of mesoaccumbens DA efflux to assess the importance of DA in the integration of different limbic inputs to the NAc. Tetanic stimulation of the fimbria potentiated hippocampal-evoked firing activity of NAc neurons and increased DA extracellular levels. Systemic administration of the D(1) receptor antagonist SCH23390 or the NMDA receptor antagonist CPP abolished the potentiation of hippocampal-evoked activity and produced a D(2) receptor-mediated suppression of evoked firing. In neurons that received converging input from the hippocampus and BLA, fimbria tetanus potentiated hippocampal-evoked firing activity and suppressed BLA-evoked activity in the same neurons. Both D(1) and NMDA receptors participated in the potentiation of fimbria-evoked activity, whereas the suppression of BLA-evoked activity was blocked by either D(1) receptor antagonism with SCH23390 or the adenosine A(1) antagonist 8-cyclopentyl-1,2-dimethylxanthine. Coincidental tetanus of both the fimbria and BLA resulted in potentiation of both inputs, indicating that DA and adenosine-mediated suppression of BLA-evoked firing was activity-dependent. These data suggest that increases in mesoaccumbens DA efflux by hippocampal afferents to the NAc play a critical role in an input selection mechanism, which can ensure preferential responding to the information conveyed from the hippocampus to the ventral striatum.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".