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Recent technological advances have enabled the recording of neurons in intactcircuits with a high spatial and temporal resolution, creating the need formodeling with the same precision. In particular, the development of ultra-fasttwo-photon microscopy combined with fluorescence-based genetically-encodedCa2+-indicators allows capture of full-dendritic arbor and somatic responsesassociated with synaptic input and action potential output. The complexity ofdendritic arbor structures and distributed patterns of activity over timeresults in the generation of incredibly rich 4D datasets that are challengingto analyze (Sakaki, 2020). Interpreting neural activity from fluorescence-basedCa2+ biosensors is challenging due to non-linear interactions between severalfactors influencing intracellular calcium ion concentration and its binding tosensors, including the ionic dynamics driven by diffusion, electrical gradientsand voltage-gated conductance.To investigate those dynamics, we designed amodel based on a Cable-like equation coupled to the Nernst-Planck equations forionic fluxes in electrolytes. We employ this model to simulate signalpropagation and ionic electrodiffusion across a dendritic arbor. Using thesesimulation results, we then designed an algorithm to detect synapses from Ca2+imaging datasets. We finally apply this algorithm to experimentalCa2+-indicator datasets from neurons expressing jGCaMP7s (Dana et al., 2019),using full-dendritic arbor sampling in vivo in the Xenopus laevis optic tectumusing fast random-access two-photon microscopy.Our model reproduces thedynamics of visual stimulus-evoked jGCaMP7s-mediated calcium signals observedexperimentally, and the resulting algorithm allows prediction of the locationof synapses across the dendritic arbor.Our study provides a way to predictsynaptic activity and location on dendritic arbors, from fluorescence data inthe full dendritic arbor of a neuron recorded in the intact and awakedeveloping vertebrate brain.

An Algorithm Based on a Cable-Nernst Planck Model Predicting Synaptic Activity throughout the Dendritic Arbor with Micron Specificity