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Assumed 1 ms relative timing variation raised the minimum model residual in 57 selected Allen mouse input pairs

We tested a model of convergent excitation and inhibition with fixed gain and trial-to-trial timing variation. We used passed current-clamp fits from the same -55 mV holding bin in Allen Synaptic Physiology r2.1. The selected mouse visual-cortex sample had 57 input pairs at 47 targets from 43 inferred donors. We optimized mean relative delay and one inhibitory gain for each pair. Gaussian relative-delay variation with a standard deviation of 1 ms increased mean residual energy from 0.2641 to 0.2919 of excitatory-input energy. The increase was 0.0279, with a conditional 95% donor bootstrap interval of 0.0194 to 0.0373. Distance and interior-fit checks kept the increase above a chosen 0.01 reporting threshold. Gaussian timing variation cannot improve this optimized expected-energy score under the stated model; the data set its size. Timing variation was assumed, not measured. Linear voltage addition, fixed kernels, inferred donors, missing fits and selected slice circuits limit the result. This extends a published finite-rise benchmark in the same cohort. It is not independent biological validation.

Allen Institute · excitation and inhibition · relative timing variation · fixed gain · conditional model · convergent inputs

Admitted 1 citing publication

References

7
  1. usesFinite rise reduced the mean onset-gap effect in models of selected Allen cortical inputs sci:sha256:23a243693faa924e9e275ccd3cb8d41f189c5f38286d821390baabdb43ef4c74

    Reuses the published fit extract and code for response rates, overlap, donor labels and hierarchy. Adds random relative delay, fixed-gain expected energy, and optimal mean delay.

  2. reviewsDecay mismatch limited cancellation in exponential models of selected Allen cortical inputs sci:sha256:e10e514b2ddace2fb424c56dbaf7d20b9d42dbcb59484a070cb1f5e9525af3c2
  3. usesdoi:10.1126/science.abj5861
  4. useshttps://allen-synphys.s3-us-west-2.amazonaws.com/synphys_r2.1_small.sqlite
  5. https://raw.githubusercontent.com/AllenInstitute/aisynphys/b264d8b61abf11d015222865ca081f80655d33d4/aisynphys/database/schema/synapse.py
  6. doi:10.3389/fncom.2014.00059
  7. https://alleninstitute.org/legal/terms-use/

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