Fig. 5: Connectome-based model captures functional characteristics of the oculomotor system.
From: Predicting modular functions and neural coding of behavior from a synaptic wiring diagram
a, Schematic illustrating gross organization of synaptic connections (triangles, excitatory; bars, inhibitory) between the three optically imaged populations; Ve2, vestibular; ABD, combined ABDM (green) + ABDI (pink) populations. b, Eye position (top) and calcium fluorescence activity (bottom) of an ABD neuron ipsilateral to the shown eye (green: raw fluorescence; dotted black: neural activity estimate from deconvolved fluorescence); AU, arbitrary units. c, Top, for the neuron in b, deconvolved fluorescence versus eye position (gray) and best-fit relationship (red) used to determine the relative eye position sensitivity \(\tilde{k}\) (see Methods). Bottom, for an example VPNI neuron, saccade-triggered average (STA) of deconvolved fluorescence (gray) and best fit to a sum of exponential functions with fixed time constants derived approximately from the principal components of the population firing rates (red; see Methods). d, Relative eye position sensitivities from the connectome-based model (gray) and imaging of real cells (green). e, Same as d except that the model uses potential synapses instead of the actual connectome. f, Cumulative variance explained for the leading principal components of the STA of the firing rates of the model (gray) or deconvolved fluorescence of imaged cells (green) for the period between 2 and 6 s after a saccade. g, For double exponential fits as in c, best-fit amplitudes of the exponentials for each cell in the model (gray) and each imaged cell (green). Note in d that VPNI cells were defined experimentally by having a sufficiently large correlation with eye position; thus, although included for completeness, the lowest sensitivity simulated VPNI cells would not have been counted if they occurred in a functional imaging dataset.
