Body: <p>In spike-based neural networks, there is a learning rule called <strong>STDP</strong> <em>(Spike-Timing-Dependent Plasticity)</em>.
  It's a completely unsupervised learning rule that works continuously when data is fed into the network.</p>
  <p>I've been trying to find learning rules that work like STDP, but can be applied to a numerical-based network with multiple layers, and complex connections <em>(e.g., multi-layer perceptron)</em>.
  I found some interesting techniques such as: <em>Principal Component Analysis, Kohonen's Self-Organizing Map, Competitive Learning, Oja's Rule, and the Hebbian rule</em>.</p>
  <p>But none of these can be applied to a deeper neural network with multiple layers and more complex connections.</p>
  <p>I'm looking for a learning rule that works unsupervised and online, similar to STDP, but that can be applied to traditional neural networks.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-08-04T19:40:37.827Z
Comments.Text: Have you considered auto encoders?
Comments.UserDisplayName: Gaslight Deceive Subvert
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-08-08T12:36:29.243Z
Comments.Text: What kind of auto encoders do you mean? Because the simple idea of auto encoders is to learn using supervised backpropagation to convert the input information to a smaller input space and then decode it back as best as possible.
Comments.UserDisplayName: aarong
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-08-08T16:28:24.340Z
Comments.Text: Autoencoding is a form of unsupervised machine learning. So it's unclear what you're looking for. Unsupervised and online is extremely broad is very broad.
Comments.UserDisplayName: Gaslight Deceive Subvert
Tags: <unsupervised-learning><online-learning><principal-component-analysis><spiking-neural-networks><hebbian-learning>
Title: Is there any unsupervised online learning rule for classical neural networks?
UserDisplayName: aarong
ThreadText: # Is there any unsupervised online learning rule for classical neural networks?
  
  (Asked by: username_0 on 2024-07-24)
  
  In spike-based neural networks, there is a learning rule called **STDP** *(Spike-Timing-Dependent Plasticity)*.
  It's a completely unsupervised learning rule that works continuously when data is fed into the network.
  
  I've been trying to find learning rules that work like STDP, but can be applied to a numerical-based network with multiple layers, and complex connections *(e.g., multi-layer perceptron)*.
  I found some interesting techniques such as: *Principal Component Analysis, Kohonen's Self-Organizing Map, Competitive Learning, Oja's Rule, and the Hebbian rule*.
  
  But none of these can be applied to a deeper neural network with multiple layers and more complex connections.
  
  I'm looking for a learning rule that works unsupervised and online, similar to STDP, but that can be applied to traditional neural networks.
  
  **Tags:** unsupervised-learning, online-learning, principal-component-analysis, spiking-neural-networks, hebbian-learning
  > **Comments:**
  > **username_1**: Have you considered auto encoders?  
  > **username_0**: What kind of auto encoders do you mean? Because the simple idea of auto encoders is to learn using supervised backpropagation to convert the input information to a smaller input space and then decode it back as best as possible.  
  > **username_1**: Autoencoding is a form of unsupervised machine learning. So it's unclear what you're looking for. Unsupervised and online is extremely broad is very broad.  
  
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  ## 0 answers
  
  