A Dendritic Voltage Surrogate-Based Synaptic Learning Framework for Biophysically Detailed Neurons and Networks

A key challenge in biophysically detailed modeling is determining functionally effective synaptic weights. Here, we introduce a dendritic voltage surrogate that couples passive dendritic transfer impedances with the exact active currents computed from the original model, yielding explicit, differentiable approximations of membrane voltage. The resulting gradients can achieve effective synaptic learning in active dendrites under transient conditions. In detailed pyramidal neuron models with somatic bursting and dendritic calcium plateau, the surrogate enables reconstruction of subthreshold and bursting membrane potentials and supports supervised fitting of target responses. At network scale, we train a detailed 136-neuron model of Caenorhabditis elegans to match an experimentally measured whole-brain correlation matrix using a multi-GPU framework. Our approach links interpretable dendritic computation with scalable optimization, providing an effective synaptic learning framework for detailed neurons and networks.