Our perception of a visual scene changes rapidly in time, even when the scene itself does
not. It is increasingly clear that understanding how the visual percept changes in time is …

An introduction to a broad range of topics in deep learning, covering mathematical and
conceptual background, deep learning techniques used in industry, and research …

Understanding how brains process sensory signals in natural environments is one of the key
goals of twenty-first century neuroscience. While brain imaging and invasive …

This paper is a survey and an analysis of different ways of using deep learning (deep
artificial neural networks) to generate musical content. We propose a methodology based on …

Inventors have long dreamed of creating machines that think. Ancient Greek myths tell of
intelligent objects, such as animated statues of human beings and tables that arrive full of …

Jean-Pierre Briot Gaëtan Hadjeres François-David Pachet Page 1 Computational Synthesis
and Creative Systems Jean-Pierre Briot Gaëtan Hadjeres François-David Pachet Deep …

How do we model the complexity of social perception? A major methodological problem is
that the space of possible variables driving social perceptions is infinitely large, thus posing …

Aims and Scope This book is both an introductory textbook and a research monograph on
modeling the statistical structure of natural images. In very simple terms,“natural images” are …

Reverse correlation (RC) techniques provide a data-driven approach to model internal
representations in an unconstrained way. Here, we used this approach to model social …

The decoding of selective auditory attention from noninvasive electroencephalogram (EEG)
data is of interest in brain computer interface and auditory perception research. The current …