We derive a new self-organizing learning algorithm that maximizes the information transferred
in a network of nonlinear units. The algorithm does not assume any knowledge of the …

This paper describes NETtalk, a class of massively-parallel network systems that learn to
convert English text to speech. The memory representations for pronunciations are learned by …

NEOCORTICAL neurons display a wide range of dendritic morphologies, ranging from compact
arborizations to highly elaborate branching patterns 1 . In vitro electrical recordings from …

Human learning is distinguished by the range and complexity of skills that can be learned
and the degree of abstraction that can be achieved compared with those of other species. …

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious
problems for electroencephalographic (EEG) interpretation and analysis when rejecting …

The computational power of massively parallel networks of simple processing elements
resides in the communication bandwidth provided by the hardware connections between …

… By applying the leA algorithm of Bell and Sejnowski [1], we attempt to completely separate
the twin problems of source identification (What) and source localization (Where). The leA …

… Motivated by computational simplicity, we use an information-theoretic algorithm that
preserves the simple architecture in Bell and Sejnowski (1995) and allows an extension to the …

It has previously been suggested that neurons with line and edge selectivities found in
primary visual cortex of cats and monkeys form a sparse, distributed representation of natural …

Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data
require a priori knowledge or specific assumptions about the time courses of processes …