What is Hebbian Learning How was it Used by AI Researchers - Research Paper Example

Topic:  What is Hebbian learning? How was it used by AI researchers? Hebbian learning is an ancient algorithm learning system which is largely basedon the biological system dynamics. In Hebbian learning, the relationship between the nodes is represented by adjusted the weight between them. Here the uncorrelated nodes have a weight of zero. According to (Kempter 4498-513)‘‘Hebbian’’ learning is thought to be an important mechanism for the tuning of neuronal connections during development and thereafter”.

The synaptic plasticity is the basic mechanism of Hebbian learning where in the persistent and repeated stimulation of the postsynaptic cell give rise to increase in synaptic efficacy. This learning theory was introduced by Donald Hebb who is a Canadian neuropsychologist in the year of 1940. Hebbian learning is the common practice of neural network training. which could be explained as an unsupervised learning? This algorithm learning is based on the postulate of Hebb which explains that when repeated firing of one cell contributes to the firing of another cell then there is decrease in the magnitude of contribution over a span of time.

As per(Sen 919-25)“Hebbian models of development and learning require both activity-dependent synaptic plasticity and a mechanism that induces competition between different synapses”. In Hebb learning, the neural circuit development based on correlated activity is depended on two important mechanisms. In his book (Rutkowska 88) writes that “The quite simple algorithm is known as the Hebbian learning rule”. The methods of learning proposed by many researchers are mainly based on the Hebbian rule in some form.

Hebbian rule suggest how much magnitude of connection should be applied between two units in align with the product of activation. Hebbian learning is both incremental and local and has been extensively studied by experts since its introduction. Many researchers concentrated on the Hebbian rule to understand its assessing capacity .Hibbing rule is used to explain the weight aspect related to Hopfield network in research field.. The local and incremental properties of learning rule are significant in attractor neural network.

In research field, the Hebbian learning is used to study the interaction between neurons in the brain functioning. According to (Butts ) “The brain is comprised of an immense number of connections between neurons, and clever strategies are required to achieve the correct wiring during development”. Hence the synaptic plasticity is the basic of Hebbian learning it helps in understanding the synapses of the brain and how it functions in different contexts. It is also used by researchers in understanding the synapses in the retinal wave activity connected to neurons of the brain.

The researchers use Hebbian learning in analyzing the interaction of neurons in the central nervous system. In his journal (Yuko 141-48) states that“ Hebbian learning is a biologically plausible and ecologically valid learning mechanism. In Hibbing learning, "units that fire together, wire together”. The studying of the synaptic strength between sensory units in the brain is the main research issue studied with Hebbian learning system. In his journal (Jiajuan 59-65)mentions that “Hebbian learning capitalizes on this positive correlation and predicts learned performance improvements.

When the training accuracy is low, however, Hebbian learning can be erratic, slow, or even fail altogether”. Hebb, in his learning theory explains how a biological neuron might learn. Researchers use the Hebbian learning theory to understand the interaction between the neurons. The researchers use Hebbian learning system in analyzing the working pattern of neuron networking in central nervous system, Researchers in the field of cognitive science, neuroscience cognitive psychological and artificial intelligence uses this learning system to acquire findings on their study.

As per (McDermott)“Most efforts to show that self-organization can occur in structures resembling the brain thus make use of Hebbian learning. ” Researchers have used Hebbian learning to unearth the functioning and working pattern of brain. Researchers use Hebbian learning to study the influence of environment on nervous system working pattern. In artificial intelligence field, the study is conducted by using artificial neuron networks to understand image analysis, speech recognition and adaptive control.

Hebbian learning applies certain mathematical models to study the biological mechanism of neurons in the brain functioning. In general, the researchers used the algorithmical calculation to understand the firing between the neuron units and the magnitude of their connection weight. The main aim of the scientist is to find the signaling process and networking patterns of nodes in a neuron network. The hebbian learning allows in studying biological working of neurons with the application of statistical and mathematical measures.

The cognitive processing of brain is studied with the application of various neuron models in Hebbian learning. Mostly, researchers apply hebbian learning system on experimental animals. As per (Mac-Phee)“Differential Hebbian Learning adjusts the learning and forgetting by pro portion to the amount of change in weight since last cycle”. Generally, most of the research study and analysis is in psychological field apply Hebbian learning system to understand the basic biological mechanism of neuron in the central nervous system.

Bibliography Butts, Daniel A. "A Burst-Based “Hebbian” Learning Rule at Retinogeniculate Synapses Links Retinal Waves to Activity-Dependent Refinement ." Plos Biology. Internet System Consortium, Mar. 2007. Web. 14 Jan. 2012. . Kempter, Richard. "Hebbian learning and spiking neurons." PHYSICAL REVIEW 59.4 Apr. (1999): 4498-513. Print. Liu, Jiajuan. "Augmented Hebbian reweighting: Interactions between feedback and training accuracy in perceptual learning." Journal of Vision 10.1027 (2010): 59-65. Print. McDermott, Josh.

"The Emergence of Orientation Selectivity in Self-Organizing Neural Networks." The Harvard Brain. Harvard Univerisity, 1996. Web. 14 Jan. 2012. Macphee Cobb, Linda. "Hebbian Learning." Herself's Artifical Intelligence. Word Press, 2007. Web. 14 Jan. 2012. . Rutkowska, Danuta. Neuro-fuzzy architectures and hybrid learning. New York: Physica - Verlag, 2002. 88. Print. Song, Sen. "Competitive Hebbian learning through spike-timing-dependent synaptic plasticity." Nature America Inc. 3.9 (2000): 919-25. Print. Yuko, Munakata A.

"A Burst-Based “Hebbian” Learning Rule at Retinogeniculate Synapses Links Retinal Waves to Activity-Dependent Refinement Article." Developmental Science 7.2 Mar. (2007): 141-48. Print.