Hippocampal‐like circuitry in the pallium of an electric fish: Possible substrates for recursive pattern separation and completion
Bibliographic record
Abstract
Teleost fish are capable of complex behaviors, including social and spatial learning; lesion studies show that these abilities require dorsal telencephalon (pallium). The teleost telencephalon has subpallial and pallial components. The subpallium is well described and highly conserved. In contrast, the teleost pallium is not well understood and its relation to that of other vertebrates remains controversial. Here we analyze the connectivity of the subdivisions of dorsal pallium (DD) of an electric gymnotiform fish, Apteronotus leptorhynchus: superficial (DDs), intermediate (DDi) and magnocellular (DDmg) components. The major pathways are recursive: the dorsolateral pallium (DL) projects strongly to DDi, with lesser inputs to DDs and DDmg. DDi in turn projects strongly to DDmg, which then feeds back diffusely to DL. Our quantitative analysis of DDi connectivity demonstrates that it is a global recurrent network. In addition, we show that the DD subnuclei have complex reciprocal connections with subpallial regions. Specifically, both DDi and DDmg are reciprocally connected to pallial interneurons within the misnamed rostral entopeduncular nucleus (Er). Based on DD connectivity, we illustrate the close similarity, and possible homology, between hippocampal and DD/DL circuitry. We hypothesize that DD/DL circuitry can implement the same pattern separation and completion computations ascribed to the hippocampal dentate gyrus and CA3 fields. We further contend that the DL to DDi to DDmg to DL feedback loop makes the pattern separation/completion operations recursive. We discuss our results with respect to recent studies on fear avoidance conditioning in zebrafish and attention and spatial learning in a pulse gymnotiform fish. J. Comp. Neurol. 525:8-46, 2017. © 2016 Wiley Periodicals, Inc.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".