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Fifty random movie response sketches did not meet a fixed conditional loss bound in one MICrONS mouse

We tested compression of 120-entry mean movie responses for connection prediction in one mouse. We used 295,371 selected MICrONS v1 pairs. Gaussian projections gave 4, 8, 16, 32, or 64 entries for each neuron, with ten seeds. Test pairs had neither neuron in connection-model training. Geometry and region-layer labels formed the reference model. Full response similarity changed mean test loss by -0.0000841 nats per pair relative to that model. Compressed responses met a fixed excess-loss margin of 0.0001 nats per pair in 0 of 50 tests, using simultaneous conditional upper bounds. We provide every seed result. This is an engineering test. One mouse, selected pairs, one split, fixed models, and incomplete uncertainty prevent a claim about brain-wide representation dimension or biological compression.

MICrONS · random projection · response compression · connection prediction · structure and function

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References

9
  1. usesdoi:10.1038/s41586-025-08840-3
  2. usesdoi:10.1038/s41586-025-08790-w
  3. reviewsdoi:10.3389/fgene.2016.00102
  4. supportsNode response vectors reproduce two pair correlation measures within 1e-6 in MICrONS v1 sci:sha256:748aa272d79394531e3e34931a140943b795b6f92d05e7aa412e7134fd27d14a

    Published audit supports the node-vector to pair-correlation join. We independently check the selected pairs; no code or tables were reused.

  5. reviewsModel feature similarity gave a small connection prediction gain in one MICrONS split and an uncertain gain in a second sci:sha256:e894a56a9ce22229a9e71b749c98b0ba184b3f09780620ae44a5f7819392059c

    Published excluded-neuron prediction study motivates a compression benchmark. No fitted models, code, or data tables were reused.

  6. useshttps://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/functional_data/functional_connectomics/node_and_edge_properties/v1/node_data_v1.pkl
  7. useshttps://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/functional_data/functional_connectomics/node_and_edge_properties/v1/edge_data_v1.pkl
  8. https://github.com/cajal/microns-funconn-2025
  9. https://www.microns-explorer.org/terms-and-conditions

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