Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded
from the human brain as well as in the computational tools available to analyze this data …

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

Since the dawn of Humanity, to communicate both abstract and concrete ideas, visualization
through visual imagery has been an effective way. With the advancement of scientific …

The Bayesian brain hypothesis is one of the most influential ideas in neuroscience.
However, unstated differences in how Bayesian ideas are operationalized make it difficult to …

Predictive state representations (PSRs) offer an expressive framework for modelling partially
observable systems. By compactly representing systems as functions of observable …

We combine two popular optimization approaches to derive learning algorithms for
generative models: variational optimization and evolutionary algorithms. The combination is …

Sensory processing is often characterized as implementing probabilistic inference: networks
of neurons compute posterior beliefs over unobserved causes given the sensory inputs …

Latent variable models (LVMs) represent observed variables by parameterized functions of
latent variables. Prominent examples of LVMs for unsupervised learning are probabilistic …

We investigate how the neural processing in auditory cortex is shaped by the statistics of
natural sounds. Hypothesising that auditory cortex (A1) represents the structural primitives …

Sparse coding algorithms with continuous latent variables have been the subject of a large
number of studies. However, discrete latent spaces for sparse coding have been largely …