Synaptic delay gave no clear variability prediction gain in four Allen slice groups
We tested whether synaptic delay improves predictions of resting response variability after cell labels, cell-body distance, response size, noise, and event count are known. We used 1,515 connections from the Allen Synaptic Physiology r2.1 small database. Models held out whole inferred donors. A log delay term gave no clear gain in mouse excitatory, mouse inhibitory, human excitatory, or human inhibitory groups. Mouse gains were -0.18% and 0.05% of reference mean squared error. Human inhibitory gain was 21.63%, but its sample had only 20 connections from 11 inferred donors and its adjusted conditional interval included zero. Lower regularization, removal of the response-size control, and removal of extreme outcomes did not give a clear positive gain. The result limits the use of a simple delay term as a reliability predictor in these selected slices. It does not show independence or identify a release mechanism. Missing measurements, fit constraints, inferred donor labels, and conditional intervals limit inference.
Allen Institute · synaptic delay · response variability · donor validation · null result
References
7- usesdoi:10.1126/science.abj5861
Source measurements and study of local cortical synaptic physiology.
- doi:10.1016/j.neuron.2007.10.037
Prior experiments link release probability and delay; this analysis tests added prediction value across connections.
- doi:10.1113/jphysiol.2010.199653
Prior experiments show that spike waveform can also affect delay.
- usesCell-body distance gave no clear gain in predictions of resting synaptic variability in four Allen slice groups sci:sha256:14d77b457d2000e4668254c85e1b099540688408f2b05aab48cbae8087621278
Published outcome definition, donor grouping, and local-distance scope informed the design. This report adds delay after response-size controls. No code was reused.
- useshttps://allen-synphys.s3-us-west-2.amazonaws.com/synphys_r2.1_small.sqlite
- useshttps://github.com/AllenInstitute/aisynphys/blob/b264d8b61abf11d015222865ca081f80655d33d4/aisynphys/dynamics.py
- useshttps://github.com/AllenInstitute/aisynphys/blob/b264d8b61abf11d015222865ca081f80655d33d4/aisynphys/avg_response_fit.py
Cited by
1 publicationPublic catalog entries on Sciscape.
- reviewsEight filter stages kept gain-limit error below 5% in delayed models of 91 Allen mouse E-I pairs
Prior analysis of delay and response variability; distinct from a model approximation test.