Since at least 1935, vision researchers have used art stimuli to test human response to
complex scenes. This is sensible given the “inherent interestingness” of art and its relation to …

Sparse coding algorithms trained on natural images can accurately predict the features that
excite visual cortical neurons, but it is not known whether such codes can be learned using …

The article presents a Bayesian model of causal learning that incorporates generic priors--
systematic assumptions about abstract properties of a system of cause-effect relations. The …

Sparse coding models of natural scenes can account for several physiological properties of
primary visual cortex (V1), including the shapes of simple cell receptive fields (RFs) and the …

Understanding how populations of neurons encode information is the challenge faced by
researchers in the field of neural coding. Focusing on the many mysteries and marvels of the …

A balance between excitatory and inhibitory synaptic currents is thought to be important for
several aspects of information processing in cortical neurons in vivo, including gain control …

We present a method that estimates the strength of neuronal oscillations at the cellular level,
relying on autocorrelation histograms computed on spike trains. The method delivers a …

Looking at the world often involves not just seeing things, but feeling things. Modern
feedforward machine vision systems that learn to perceive the world in the absence of active …

Unsupervised feature learning refers to the problem of learning useful feature extraction
functions from unlabeled data. Despite the great success of deep learning networks in this …

Inhibition plays a crucial role in neural signal processing, sha** and limiting responses. In
the auditory system, inhibition already modulates second order neurons in the cochlear …