Terrence J. Sejnowski

Parallel networks that learn to pronounce English text

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 learn...

Dictionary Learning Algorithms for Sparse Representation

Algorithms for data-driven learning of domain-specific overcomplete dictionaries are developed to obtain maximum likelihood and maximum a posteriori dictionary estimates based o...

The predictive brain: temporal coincidence and temporal order in synaptic learning mechanisms.

Some forms of synaptic plasticity depend on the temporal coincidence of presynaptic activity and postsynaptic response. This requirement is consistent with the Hebbian, or corre...

An Information-Maximization Approach to Blind Separation and Blind Deconvolution

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...

Competitive Anti-Hebbian Learning of Invariants

Although the detection of invariant structure in a given set of input patterns is vital to many recognition tasks, connectionist learning rules tend to focus on directions of hi...

Proceedings of the 1988 Connectionist Models Summer School

Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources

An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able blindly to separate mixed signals with sub- and supergaussian source distributions. ...

Learned classification of sonar targets using a massively parallel network

Abstract-We have applied massively parallel learning networks to the classification of sonar returns from two undersea targets and have studied the ability of networks to correc...

NETtalk: a parallel network that learns to read aloud