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An online Hebbian learning rule that performs Independent Component Analysis

Publications associées (60)

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Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning

Wulfram Gerstner

We study analytically a model of long-term synaptic plasticity where synaptic changes are triggered by presynaptic spikes, postsynaptic spikes, and the time differences between pre- and postsynaptic spikes. We show that plasticity can lead to an intrinsic ...

Massachusetts Institute of Technology Press2001

An algorithm for modifying neurotransmitter release probability based on pre- and postsynaptic spike timing

Henry Markram

The precise times of occurrence of individual pre- and postsynaptic action potentials are known to play a key role in the modification of synaptic efficacy. Based on stimulation protocols of two synaptically connected neurons, we infer an algorithm that re ...

2001

Mixture models for unsupervised and supervised learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of al1 this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

EPFL2000

Mixture Models for Unsupervised and Supervised Learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of all this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

IDIAP2000

Mixture Models for Unsupervised and Supervised Learning

École Polytechnique Fédérale de Lausanne, Computer Science Department2000

Hebbian learning and spiking neurons

Wulfram Gerstner

A correlation-based (

Hebbian'') learning rule at the spike level is formulated, mathematically analyzed, and compared with learning in a firing-rate description. As for spike coding, we take advantage of a

learning window'' that describes the effect of ...

1999

The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability

Henry Markram

Although signaling between neurons is central to the functioning of the brain, we still do not understand how the code used in signaling depends on the properties of synaptic transmission. Theoretical analysis combined with patch clamp recordings from pair ...

1997

A neuronal learning rule for sub-millisecond temporal coding

Wulfram Gerstner

An unresolved paradox exists in auditory and electrosensory neural systems {Carr93,Heiligenberg91}: they encode behaviourally relevant signals in the range of a few microseconds with neurons that are at least one order of magnitude slower. We take the barn ...

1996

Speaker identification by lipreading

This paper describes a new approach for speaker identification based on lipreading. Visual features are extracted from image sequences of the talking face and consist of shape parameters which describe the lip boundary and intensity parameters which descri ...

1996

Intercomparison Of Atomic Models For Computing Stopping Parameters From The Bethe Theory - Atomic-Hydrogen

Anders Meibom

The stopping cross section, the straggling parameter, and three higher moments over the energy-loss cross section have been evaluated for a moving point charge interacting with atomic hydrogen. The aim was to estimate the accuracy of predictions based on f ...

1992