Retina ribbon synapses are the first synapses in the visual system. Unlike the conventional synapses in the central nervous system triggered by action potentials, ribbon synapses are uniquely driven by graded membrane potentials and are thought to transfer early sensory information faithfully. However, how ribbon synapses compress the visual signals and contribute to visual adaptation in retina circuits is less understood. To this end, we introduce a physiologically constrained module for the ribbon synapse, termed Ribbon Adaptive Block (RAB), and an extended "hierarchical Linear-Nonlinear-Synapse" (hLNS) framework for the retina circuit. Our models can elegantly reproduce a wide range of experimental recordings on synaptic and circuit-level adaptive behaviors across different cell types and species. In particular, it shows strong robustness to unseen stimulus protocols. Intriguingly, when using the hLNS framework to fit intra-cellular recordings from the retina circuit under stimuli similar to natural conditions, we revealed rich and diverse adaptive time constants of ribbon synapses. Furthermore, we predicted a frequency-sensitive gain-control strategy for the synapse between the photoreceptor and the CX bipolar cell, which differ from the classic contrast-based strategy in retina circuits. Overall, our framework provides a powerful analytical tool for exploring synaptic adaptation mechanisms in early sensory coding.

Biophysically detailed multi-compartment models are powerful tools to explore computational principles of the brain and also serve as a theoretical framework to generate algorithms for artificial intelligence (AI) systems. However, the expensive computational cost severely limits the applications in both the neuroscience and AI fields. The major bottleneck during simulating detailed compartment models is the ability of a simulator to solve large systems of linear equations. Here, we present a novel Dendritic Hierarchical Scheduling (DHS) method to markedly accelerate such process. We theoretically prove that the DHS implementation is computationally optimal and accurate. This GPU-based method performs at 2-3 orders of magnitude higher speed than that of the classic serial Hines method in the conventional CPU platform. We build a DeepDendrite framework, which integrates the DHS method and the GPU computing engine of the NEURON simulator and demonstrate applications of DeepDendrite in neuroscience and AI tasks. We investigated how spatial patterns of spine inputs affects neuronal excitability in a detailed human pyramidal neuron model with 25,000 spines; and examined how dendrites protect morphologically detailed neural networks against adversarial attacks in typical image classification tasks.