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Electrical links without chemical currents identified readout filter values for 283 of 302 cells in a complete linear worm model with static input gains

We asked whether known electrical contacts can separate readout delay from neural delay. We used the corrected July 2020 Cook C. elegans map and proved an inverse response rule. An electrical direction with no parallel chemical current identifies a normalized readout transfer value from DC and a response at another rate. The rule requires a complete square response matrix, a linear model, static input gains, and correct contact support. It cancels unknown real input and output gains. For a readout with one decay time, one complex sine matrix is enough. The map had 1,428 eligible directions that covered 283 of 302 cell slots; 254 cells had more than one anchor. A stable chosen model recovered the one-time parameters with relative error below 6.3e-15. In 30 error draws at each of four frequencies, a 0.1% response-matrix norm error left a median 67.5% of cells within 10% time error at the best tested frequency. A passive three-cell example gave 47.6% error after one cell was hidden. A common actuator delay could move into the readout filters without changing the complete observed response. The result is a conditional calibration method and a set of failure tests. It does not estimate conductances or indicator times in living worms. The package preserves proofs, code, contact tables, source checks, model parameters, cell estimates and figures.

connectome · calcium imaging · electrical synapse · readout dynamics · system identification · C. elegans · measurement calibration

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References

9
  1. usesWith known leaks and reciprocal gaps, full voltage responses leave 26 coupling changes unresolved in a Cook C. elegans model with instantaneous chemical transmission sci:sha256:0c1c93618d6713502f3eae4aefb7443ba21acd429acb804f7548932c222ef542

    We reuse its CC BY 4.0 numerical contact tables after SCI signature verification. We independently check every selected neural chemical and gap entry against the fixed original workbook.

  2. usesIn a calibrated 302-neuron model with one-pole chemical filters and source-shared times, one exact sine response matrix identified electrical and chemical weights sci:sha256:4c6684e491b7114c3026aa10e0fffbaf46f72a4cf8ed123866a25a37cf6a6112

    Its chemical-anchor method is a starting point. We extend the inverse-entry argument to unknown readout filters with electrical anchors and unknown static port gains.

  3. With unit capacitance, full responses leave 41 relative readout gains free in a 302-cell worm model with reciprocal gaps, unknown leaks and input scales, and instantaneous chemical currents sci:sha256:76f1ccfc8d5fd7603827bc6a4aa22c9eb32fb273d7c3784aea54b41ca137fb57

    Prior analysis of static input and readout gain uncertainty on the same graph.

  4. With fixed response error, a slow readout filter shifted the best tested frequency from 0.792 to 0.343 in a 302-cell worm model sci:sha256:f5e6956a801016035a2598305a301542a818c889066135d616b0cfc7560ce6da

    Prior frequency design with a known readout filter; the present study tests unknown readout filters.

  5. usesdoi:10.1038/s41586-019-1352-7

    Original Cook, Jarrell and colleagues anatomical data. We use the workbook corrected in July 2020.

  6. doi:10.1016/j.automatica.2017.12.013

    Established model and measurement conditions for linear dynamic network identification.

  7. doi:10.1016/j.neuron.2015.11.037

    Prior methods that separate calcium readout dynamics from neural activity.

  8. doi:10.1371/journal.pcbi.1005423

    Established calcium deconvolution method; no source code is reused.

  9. doi:10.1038/s41586-023-06683-4

    Biological context for neural signal propagation and current calcium response atlases. No response data from this source are fitted.

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