Topic: linear classifiers

During natural visual behavior, our visual system extracts multiple features from its inputs and uses them to solve different tasks. Each feature conveys relevant information to different tasks, and for a given task our visual system relies on the relevant features while ignoring others. However, this hypothesis remains largely untested for complex tasks with natural stimuli. Here we compare the role of spectral and higher-order (HOS) texture statistics of the Portilla-Simoncelli model across tasks using natural images, to explain their task-dependent use by humans. Portilla-Simoncelli HOS are

Interest in using biological neuronal networks as artificial learning systems has surged [e.g. 1,2,3]. However, networks of biological neurons differ from artificial neural networks: they are intrinsically noisy and have non-trivial physical embeddings. The question thus arises whether biological neurons are usable for artificial intelligence. A basic requirement for a system to be a good learning machine is the capacity to respond distinctly to different inputs, yet reproduce similar activity to similar stimuli. Liquid state [4] and reservoir [5] computing make direct and explicit use of this