Evidence for rich posterior representations from discrete encoding models of V1
Yidi Ke, Filippos Panagiotou, Jorg Lucke, Dmytro Velychko · COSYNE 2025
Understanding how cortical circuits encode sensory information remains a key challenge. Given a stimulus, the central question from the prespecitive of probablistic theories is how posterior probablities are neurally encoded. In recent years, standard approaches like sampling-based and variational approximations have been extended by deep neural networks (DNNs). For variational approaches, DNNs are frequently used to define encoders in variational autoencoders (VAE) settings. It remains unclear, however, what aspects of posterior approximations are important to explain neural encoding . Unlik
ePoster · Neuroscience