Hebb's Rule
A Hebb's Rule is a learning rule that describes synaptic plasticity.
- AKA: Hebbian Theory, Hebb's Postulate, Cell Assembly Theory.
- Example(s):
- Counter-Example(s):
- See: Hebbian Neural Network, Rule Learning Algorithm, Unsupervised Learning, Neuroscience, Synapse, Presynaptic Cell, Synaptic Plasticity, Neuron, Donald Hebb, The Organization of Behavior, Axon, Siegrid Löwel, Spike-Timing-Dependent Plasticity, Associative Learning.
References
2020
- (Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/Hebbian_theory Retrieved:2020-10-31.
- Hebbian theory is a neuroscientific theory claiming that an increase in synaptic efficacy arises from a presynaptic cell's repeated and persistent stimulation of a postsynaptic cell. It is an attempt to explain synaptic plasticity, the adaptation of brain neurons during the learning process. It was introduced by Donald Hebb in his 1949 book The Organization of Behavior.[1] The theory is also called Hebb's rule, Hebb's postulate, and cell assembly theory. Hebb states it as follows:
Let us assume that the persistence or repetition of a reverberatory activity (or "trace") tends to induce lasting cellular changes that add to its stability. ... When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that As efficiency, as one of the cells firing B, is increased.
The theory is often summarized as "Cells that fire together wire together." [2] However, Hebb emphasized that cell A needs to "take part in firing" cell B, and such causality can occur only if cell A fires just before, not at the same time as, cell B. This aspect of causation in Hebb's work foreshadowed what is now known about spike-timing-dependent plasticity, which requires temporal precedence.
The theory attempts to explain associative or Hebbian learning, in which simultaneous activation of cells leads to pronounced increases in synaptic strength between those cells. It also provides a biological basis for errorless learning methods for education and memory rehabilitation. In the study of neural networks in cognitive function, it is often regarded as the neuronal basis of unsupervised learning.
- Hebbian theory is a neuroscientific theory claiming that an increase in synaptic efficacy arises from a presynaptic cell's repeated and persistent stimulation of a postsynaptic cell. It is an attempt to explain synaptic plasticity, the adaptation of brain neurons during the learning process. It was introduced by Donald Hebb in his 1949 book The Organization of Behavior.[1] The theory is also called Hebb's rule, Hebb's postulate, and cell assembly theory. Hebb states it as follows:
- ↑ Hebb, D.O. (1949). The Organization of Behavior. New York: Wiley & Sons.
- ↑ Siegrid Löwel, Göttingen University; The exact sentence is: "neurons wire together if they fire together" (Löwel, S. and Singer, W. (1992) Science 255 (published January 10, 1992)
2020b
- (Chuang, 2020) ⇒ "Appedix D: Artificial Neural Network". Retrieved:2020-10-31.
- QUOTE: Hebb's rule is a postulate proposed by Donald Hebb in 1949[1]. It is a learning rule that describes how the neuronal activities influence the connection between neurons, i.e., the synaptic plasticity. It provides an algorithm to update weight of neuronal connection within neural network. Hebb's rule provides a simplistic physiology-based model to mimic the activity dependent features of synaptic plasticity and has been widely used in the area of artificial neural network. Different versions of the rule have been proposed to make the updating rule more realistic.
- ↑ Hebb D (1949) The Organization of Behavior. A Neuropsychological Theory. Wiley, New York, NY
2018
- (Wang, 2018) ⇒ Ruye Wang (2018) http://fourier.eng.hmc.edu/e161/lectures/nn/node4.html Last Updated:2018-04-10
- QUOTE: Donald Hebb (Canadian Psychologist) speculated in 1949 thatWhen neuron A repeatedly and persistently takes part in exciting neuron B, the synaptic connection from A to B will be strengthened.
Simultaneous activation of neurons leads to pronounced increases in synaptic strength between them. In other words, "Neurons that fire together, wire together. Neurons that fire out of sync, fail to link". So a Hebbian network can be used as an associator which will establish the association between two sets of patterns $\{{\bf x}_k,\;\;k=1,\cdots,K \}$ and $\{{\bf y}_k,\;\;k=1,\cdots,K\}$.
- QUOTE: Donald Hebb (Canadian Psychologist) speculated in 1949 that
2017
- (Liu et al., 2017) ⇒ Jia Liu, Maoguo Gong, and Qiguang Miao (2017, August). "Modeling Hebb Learning Rule for Unsupervised Learning". In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI 2017). DOI:10.24963/ijcai.2017/322.
- QUOTE: This paper presents to model the Hebb learning rule and proposes a neuron learning machine (NLM). Hebb learning rule describes the plasticity of the connection between presynaptic and postsynaptic neurons and it is unsupervised itself. It formulates the updating gradient of the connecting weight in artificial neural networks. In this paper, we construct an objective function via modeling the Hebb rule.