  Recent technological advances have enabled the recording of neurons in intact
circuits with a high spatial and temporal resolution, creating the need for
modeling with the same precision. In particular, the development of ultra-fast
two-photon microscopy combined with fluorescence-based genetically-encoded
Ca2+-indicators allows capture of full-dendritic arbor and somatic responses
associated with synaptic input and action potential output. The complexity of
dendritic arbor structures and distributed patterns of activity over time
results in the generation of incredibly rich 4D datasets that are challenging
to analyze (Sakaki, 2020). Interpreting neural activity from fluorescence-based
Ca2+ biosensors is challenging due to non-linear interactions between several
factors influencing intracellular calcium ion concentration and its binding to
sensors, including the ionic dynamics driven by diffusion, electrical gradients
and voltage-gated conductance.To investigate those dynamics, we designed a
model based on a Cable-like equation coupled to the Nernst-Planck equations for
ionic fluxes in electrolytes. We employ this model to simulate signal
propagation and ionic electrodiffusion across a dendritic arbor. Using these
simulation results, we then designed an algorithm to detect synapses from Ca2+
imaging datasets. We finally apply this algorithm to experimental
Ca2+-indicator datasets from neurons expressing jGCaMP7s (Dana et al., 2019),
using full-dendritic arbor sampling in vivo in the Xenopus laevis optic tectum
using fast random-access two-photon microscopy.Our model reproduces the
dynamics of visual stimulus-evoked jGCaMP7s-mediated calcium signals observed
experimentally, and the resulting algorithm allows prediction of the location
of synapses across the dendritic arbor.Our study provides a way to predict
synaptic activity and location on dendritic arbors, from fluorescence data in
the full dendritic arbor of a neuron recorded in the intact and awake
developing vertebrate brain.
