| Abstract |
This talk explores the connection between biological neuronal computation and the design of its inspired novel artificial neural networks. We begin by examining how single neurons integrate spatiotemporal inputs through their dendritic structures. Using mathematical modeling, theoretical analysis, and numerical simulations, we uncover a bilinear integration rule that captures essential features of dendritic information processing. Building on this principle, we introduce a compact and interpretable architecture termed the Dendritic Bilinear Neural Network (DBNN). This model accurately predicts subthreshold membrane potentials and spike timing across diverse neuron types, while exhibiting strong generalization and robustness. Beyond prediction, DBNN reproduces key dendritic computations, including direction selectivity, coincidence detection, and logical operations, with parameters that closely align with biologically observed mechanisms. As a novel neural network architecture, DBNN ... |
