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The organisation of invertebrate brains: cells, synapses and circuits

2009· article· en· W2082832418 on OpenAlexaff
Ian A. Meinertzhagen

Bibliographic record

VenueActa Zoologica · 2009
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsVertebrateBiologyNeuroscienceInvertebrateSomaPostsynaptic potentialNeuronInterneuronBiological neural networkAnatomyEcologyInhibitory postsynaptic potentialReceptor

Abstract

fetched live from OpenAlex

Abstract Meinertzhagen, I.A. 2010. The organisation of invertebrate brains: cells, synapses and circuits. — Acta Zoologica (Stockholm) 91 : 64–71 Invertebrate brains are structurally diverse. Neuron numbers range from ∼10 2 to 10 8 in different groups, compared with larger numbers in vertebrate brains, ∼10 7 to 10 14 . The underpopulated brains of invertebrates are noted in their extreme cases for having few cells, and neurons that can be identified from animal to animal, many known in great detail. Although few in number, invertebrate neurons nevertheless comprise many classes. Correlated with the paucity of their number they are sparsely connected, many having ∼50 synapses or fewer. Synaptic densities, roughly 1 per μm 3 of neuropile, differ little from those for much larger vertebrate neurons. Invertebrate neurons differ from their vertebrate counterparts in the position of their soma, generally in a cortex surrounding the neuropile that consequently occupies a relatively small volume. Their axons typically lack myelin and, supporting a range of conduction velocities, have diameters that differ over a wide range, from 10 3 to 10 −1 μm. Nerves with thousands of axons differ from neuropile fascicles, which typically have 20 or less. Unlike most vertebrate synapses, but like those of the vertebrate retina, synapses in many invertebrate groups – probably all ecdysozoans and possibly some lophotrochozoans – have synaptic contacts with multiple postsynaptic elements, dyads, triads and so on.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.268
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations37
Published2009
Admission routes1
Has abstractyes

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