Answers.Body: <p>In machine learning, the idea behind <a href="https://en.wikipedia.org/wiki/Hebbian_theory" rel="noreferrer">Hebbian learning</a> is to strengthen (or weaken) the connection (the weight) between the neurons that have similar (or, respectively, dissimilar) outputs, where "similar" can be defined in different ways (e.g. it could be based on the sign of the output of the neurons). </p>
  
  <p>Hebbian learning is a more biologically plausible way of learning than back-propagation, because it is a "local learning strategy" (you locally update the connections and not all the connections of the model at the same time), as opposed to back-propagation, which is a "global learning strategy" (where all connections are usually updated at once, given a "global error" of the network).</p>
  
  <p>There are several neural networks (or models) that can learn in a Hebbian fashion: for example, the <a href="https://en.wikipedia.org/wiki/Hopfield_network" rel="noreferrer">Hopfield network</a> or <a href="https://numenta.com/" rel="noreferrer">Numenta</a>'s <em>temporal memory</em>. </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-04-28T12:46:53.917Z
Answers.LastActivityDate: 2019-04-28T12:57:24.533Z
Answers.LastEditDate: 2019-04-28T12:57:24.533Z
Answers.UserDisplayName: nbro
Body: <p>Can anyone help me in understanding Hebb networking and how different function like AND, OR used to solve by this network.</p>
  <p>I couldn’t understand properly through the google.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-04-26T00:54:34.410Z
Comments.Text: Can you please tell us where you heard of the expression "Hebb network"? Aren't you actually interested in Hebbian learning, as opposed to a particular neural network that learns in an Hebbian fashion?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-04-26T02:53:08.013Z
Comments.Text: @nbro I am confused on the same thing if Hebbian learning and Hebb Network are same thing.
Comments.UserDisplayName: Lexi
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-04-27T06:24:30.813Z
Comments.Text: There is no such thing as a Hebb Network. Perhaps you mean a Hopfield Network? Or its probabilistic version the Boltzmann Machine? There are the models which are trained by Hebbian Learning.
Comments.UserDisplayName: samlaf
Tags: <neural-networks><machine-learning><hebbian-learning>
Title: Understanding Hebb network
UserDisplayName: Lexi
ThreadText: # Understanding Hebb network
  
  (Asked by: username_0 on 2019-04-25)
  
  Can anyone help me in understanding Hebb networking and how different function like AND, OR used to solve by this network.
  
  I couldn’t understand properly through the google.
  
  **Tags:** neural-networks, machine-learning, hebbian-learning
  
  > **Comments:**
  > **username_1**: Can you please tell us where you heard of the expression "Hebb network"? Aren't you actually interested in Hebbian learning, as opposed to a particular neural network that learns in an Hebbian fashion?  
  > **username_0**: @username_1 I am confused on the same thing if Hebbian learning and Hebb Network are same thing.  
  > **username_2**: There is no such thing as a Hebb Network. Perhaps you mean a Hopfield Network? Or its probabilistic version the Boltzmann Machine? There are the models which are trained by Hebbian Learning.  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2019-04-28. Score: 6
  
  In machine learning, the idea behind [Hebbian learning](https://en.wikipedia.org/wiki/Hebbian_theory) is to strengthen (or weaken) the connection (the weight) between the neurons that have similar (or, respectively, dissimilar) outputs, where "similar" can be defined in different ways (e.g. it could be based on the sign of the output of the neurons).
  
  Hebbian learning is a more biologically plausible way of learning than back-propagation, because it is a "local learning strategy" (you locally update the connections and not all the connections of the model at the same time), as opposed to back-propagation, which is a "global learning strategy" (where all connections are usually updated at once, given a "global error" of the network).
  
  There are several neural networks (or models) that can learn in a Hebbian fashion: for example, the [Hopfield network](https://en.wikipedia.org/wiki/Hopfield_network) or [Numenta](https://numenta.com/)'s *temporal memory*.
  
  
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