Complete channel loss covered all sampled recall failures in three selected fly calyx models
We tested whether complete glomerular channel loss finds odor recall failures seen under partial suppression. We linked verified FlyWire v783 and hemibrain v1.2 circuit models to 23 receptor channels for 110 odors. FlyWire left and right are one fly; hemibrain is another. The model stored baseline top-10-percent Kenyon cell codes and used fixed nearest-template recall. It tested 21 suppression levels for each odor and channel. No primary pair failed at a sampled partial level and then recalled correctly at complete loss. Complete loss gave 180, 191 and 199 failures among 2530 pairs per calyx, with ties counted as failures. Thus, equal-weight recall was above 92 percent in each model. An added targeted-loss diagnostic found at least one damaging channel for 102, 106 and 107 of 110 odors. Code change and recall gave different scores. A synthetic nonnegative model failed at partial loss and recovered at complete loss, so the empirical grid result is not a general theorem. Incomplete input profiles, two anatomical animals, an ideal activity threshold, exact stored templates, no trial noise and a finite grid limit the result. Code, aggregate evidence, source hashes and verified input retrieval support reproduction. Source-derived matrices and per-odor records remain outside the baseline publication.
Drosophila · connectomics · mushroom body · odor recall · input loss · sparse coding · representation reliability
References
13- usesWeight shuffles gave less mean code change after one input loss in three fly calyx models sci:sha256:41cb795c03857c14b06ee329ab03ec570fdc506399c39e4e7a0cdc8434b4dcc7
We reuse three verified model archives outside the signed publication and extend the active-set loss test to fixed-template recall across graded suppression.
- reviewsIn models from three fly mushroom body connectomes, glomerulus input share gave a community input advantage, Kenyon cell co-convergence gave none, and real wiring gave no gain for 110 odors sci:sha256:10b3bf2ee10c18102a0cee66d472b5be5bec41f9d264f8c8b7cc584f42d3d835
Prior odor identification test with noise and wiring controls; the current test uses fixed baseline templates and graded input suppression.
- reviewsProjection neuron co-convergence onto Kenyon cells repeats in three fly calyces, but in a rate model balanced input totals per Kenyon cell, not co-convergence, lower odor pattern overlap sci:sha256:d47d61cecf35201e4dfa315a4550acd1f6f7038e2cc7d15a7828e387c956b142
Structural selection lineage for the model archives reused through the input-loss publication.
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- usesdoi:10.1038/s41586-024-07558-y
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- usesdoi:10.7554/eLife.57443
- reviewsdoi:10.1523/JNEUROSCI.1099-11.2011
- reviewsdoi:10.1523/JNEUROSCI.0682-12.2013
- https://home.flywire.ai/guidelines
External data terms were checked. No source tables or derivative connectivity matrices are bundled.
- https://zenodo.org/records/10676866
- useshttps://raw.githubusercontent.com/tom-f-oconnell/drosolf/2ff359109ed9d4c05e2f4c296111ca186cb0e892/drosolf/data/hc_data.csv
Pinned receptor export for the independent rate rebuild audit only; kept outside the signed publication.
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