Count weights gave no clear movie response prediction gain in 169 selected MICrONS targets
We tested whether contact counts improve prediction of measured movie responses from fixed anatomical input sets in one MICrONS mouse. We used 169 targets with at least five measured inputs, 1036 connections and 135 input cells. In five held-out response-entry blocks, mean scaled error was 2.11541 with count weights and 2.11852 with equal weights. The improvement was 0.00312, with a nominal target bootstrap interval of -0.03309 to 0.03410. Observed count weights were near the center of 499 within-target shuffle scores, with lower-tail fraction 0.45. Total cleft volume reduced block error but gave no clear gain with interleaved folds. Count weights gave worse block error than a training-mean baseline. Sparse inputs, one selected animal, shared sources and stimuli, trial-mean data and a limited linear model restrict inference. The test does not establish causal transmission or transfer across animals. We provide original code, aggregate evidence, source hashes and a retrieval recipe. Source and derived record-level data remain outside the signed package.
MICrONS · structure and function · synapse count · response prediction · null result · Allen Institute
References
8- usesdoi:10.1038/s41586-025-08840-3
Source of the v1 functional connectomics node and edge tables and the reported size and count similarity relations.
- usesdoi:10.1038/s41586-025-08790-w
Source resource linking calcium imaging and electron microscopy in one mouse.
- reviewsTotal cleft volume favored redundant target responses under two weight shuffles in one MICrONS mouse sci:sha256:3f421179a43c393dc713cd33133e8e34e31dbc6459112398b0c663ec77c2e8fb
Prior weighted target-dimension test. We test target response prediction from inputs and reuse no code or derived records.
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- https://www.microns-explorer.org/cortical-mm3
- useshttps://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/functional_data/functional_connectomics/node_and_edge_properties/v1/node_data_v1.pkl
- useshttps://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/functional_data/functional_connectomics/node_and_edge_properties/v1/edge_data_v1.pkl
Cited by
1 publicationPublic catalog entries on Sciscape.
- reviewsFitted positive input weights gave no clear block prediction gain at penalty 10 in 169 selected MICrONS targets
Extends the fixed input-pooling benchmark to separately fitted positive weights and matched input sets; new code.