Hebbian Learning

Posted By on May 16, 2016


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Learning Vector Quantization
Introduction to Soft Computing

Hebbian learning is one of the oldest learning algorithms, and is based in large part on the dynamics of biological systems.

A synapse between two neurons is strengthened when the neurons on either side of the synapse (input and output) have highly correlated outputs.

In essence, when an input neuron fires, if it frequently leads to the firing of the output neuron, the synapse is strengthened.

Following the analogy to an artificial system, the tap weight is increased with high correlation between two sequential neurons.

Mathematical Formulation

Mathematically, we can describe Hebbian learning as:

w_{ij}[n + 1] = w_{ij}[n] + \eta x_i[n]x_j[n]

Here, η is a learning rate coefficient, and x are the outputs of the ith and jth elements.

Learning Vector Quantization
Introduction to Soft Computing

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Posted by Akash Kurup

Founder and C.E.O, World4Engineers Educationist and Entrepreneur by passion. Orator and blogger by hobby

Website: http://world4engineers.com