With fixed known responses and linear sums, 14 of 36 incomplete mouse V1 maps required more than 5% layer share change at 30 Hz with no upper limit on missing responses
We tested whether missing second responses can remove layer share changes in mouse V1 input maps at 30 Hz. We used public optical input maps and fixed the stored first amplitudes. Equal activation and linear voltage sums defined the layer shares. The selected panel had 55 maps, with 887 known and 64 unknown second responses for positive first amplitudes. A source check added 37 connected inputs with a stored zero first response. This gave 36 incomplete maps. Every unknown second response could take any nonnegative value, with no upper limit. With known responses fixed, 14 maps had a sharp minimum total variation above 0.05. A relative allowance of 10% for known second estimates left 11 maps above this value. A 20% allowance left eight. These allowances are assumed sets, not measured errors or confidence intervals. Seven maps had simple two-layer lower certificates above 0.05. Missing inputs cannot add excitation outside their recorded source layers, which explains why a bound can remain without a gain limit. Independent linear programs, source reads and an offline repeat checked the calculations. The package preserves original data, code, provenance, results, figures and repeat instructions. These are limits for recorded estimates under a linear model. Missing trial errors, uncertain stored zeros and incomplete input coverage prevent a biological effect claim.
functional connectome · mouse visual cortex · short-term plasticity · missing data · cortical layers · partial identification · response bounds
References
7- usesdoi:10.5061/dryad.5x69p8dc7
We use the unchanged CC0 source data from Dryad version ID 338458, metadata version 4, released 2025-02-08.
- usesdoi:10.1016/j.xinn.2024.100735
This study measured the optical input maps and short-term responses used here.
- usesFor equal activation and linear sums, a layer rule gave no clear prediction gain for 30 Hz input shares in 54 mouse V1 maps sci:sha256:eedcc1ec7efaf32d23918e4cc8657386489fc542ee82b10e965cab1ac79c026a
We extend its test of layer shares by calculating sharp bounds for missing response estimates. We verified its signed archive.
- usesAt 30 Hz, fitting the strength slope cut squared log error by 16.6% for 438 mouse V1 basket-cell inputs with test dates excluded sci:sha256:af0f4eba8ee407e3bc249ac0f622471e8c1dad05766f85ac62f11d65dff50455
We use its source ratio audit rule and compare our independently extracted known input set with its 991 retained connections. We verified its signed archive.
- For equal input activation, strength weighting lowered 30 Hz gain estimates by 0.13 in 69 mouse V1 input maps sci:sha256:52a91f2ae872b8b907b7e3d7807bcbbbc666694090caaec31677c7d907c28d68
This earlier study examined weighted total gain in the same source maps.
- doi:10.1016/j.neuron.2023.07.003
This experiment links input-specific depression to temporal integration in mouse visual cortex.
- usesdoi:10.1002/nav.3800090303
We use the standard positive-denominator transformation to check normalized response bounds with linear programs.
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