Answers.Body: <p>Yes, there is a whole field where people compare the emergent representations of biological and artificial systems. It is very often found that the role of individual neurons is similar. For example, in the early layers of the visual pathway (retina, LGN, V1), we see receptive fields that are sensitive to local contrast, edges, center-surround and similar simple patterns. The same holds for early layers of CNNs. Intermediate layers both of the visual cortex and of CNNs are sensitive to increasingly complex stimuli such as grids, circles, complex patterns, etc., up to features like eyes, wheels, dog ears. At the last layer, CNN representations are semantic, i.e. coincide with labels (by construction). Equivalently, you find areas of the brain where there are semantic representations, i.e. cells that are sensitive to the concept of (famously) Jennifer Aniston, independently of how we see/hear/read of her.</p>
  <p>Just a couple of examples of literature I'm familiar with: <a href="https://www.nature.com/articles/s41586-018-0102-6" rel="nofollow noreferrer">Banino et al.</a> found grid cells, similar to those for which the Mosers won the Nobel prize for physiology, in a neural network trained for a spatial navigation task. People like James DiCarlo spent their careers on this kind of thing applied to the visual cortex, a summary can be found <a href="https://www.sciencedirect.com/science/article/pii/S089662732030605X" rel="nofollow noreferrer">in this paper</a>, where they propose a challenge for finding the computer vision model that is most similar to the brain; but all of his work is relevant. In fact, an amazing fact is that CNNs trained to do the same task as an animal (vision), can linearly predict the activity of neurons in the animal's visual cortex <em>better than if the network was directly trained to predict those neurons' activities</em>. People also did similar things for <a href="https://www.biorxiv.org/content/10.1101/2020.06.26.174482v1" rel="nofollow noreferrer">language models</a> and for <a href="https://www.frontiersin.org/articles/10.3389/fnins.2022.843988/full" rel="nofollow noreferrer">audio</a>.</p>
  <p>These are all works that compare single-neuron activations (receptive fields, preferred stimuli) between biological systems and artificial models. Of course other methods of comparison could be devised but it sounds like this is what you were interested in.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-07-15T14:19:50.273Z
Answers.LastActivityDate: 2022-07-15T14:19:50.273Z
Answers.UserDisplayName: Martino
Body: <p>&quot;ImageNet Classification with Deep Convolutional Neural Networks&quot; by Krizhevsky &amp; Sutskever &amp; Hinton describes very interesting emergent behavior of the AlexNet.</p>
  <p>It was trained on 2 GPU's:</p>
  <blockquote>
  <p>specialization exhibited by the two GPUs ... The kernels on GPU 1 are largely color-agnostic, while the kernels on on GPU 2 are largely color-specific. This kind of specialization <strong>occurs during every run and is independent of any particular random weight initialization</strong></p>
  </blockquote>
  <p>Likewise our brain mostly processes color with left side of the brain.</p>
  <p>Are there other examples of emergent behavior in AI models that looks similar to natural neural systems? Any kind from coordination of several neurons to high-level function, usefull or detrimental, like optical illusions?</p>
  <p>So far I found only some articles with <a href="https://scholar.google.com/scholar?hl=en&amp;as_sdt=0%2C5&amp;q=deep%20learning%20net%20optical%20illusions&amp;btnG=" rel="nofollow noreferrer">optical illusion examples</a>.</p>
  
Tags: <training><neuroevolution>
Title: Emergent behavior in AI models that looks similar to natural neural systems
UserDisplayName: Vashu
ThreadText: # Emergent behavior in AI models that looks similar to natural neural systems
  
  (Asked by: username_0 on 2022-07-15)
  
  "ImageNet Classification with Deep Convolutional Neural Networks" by Krizhevsky & Sutskever & Hinton describes very interesting emergent behavior of the AlexNet.
  
  It was trained on 2 GPU's:
  
  > specialization exhibited by the two GPUs ... The kernels on GPU 1 are largely color-agnostic, while the kernels on on GPU 2 are largely color-specific. This kind of specialization **occurs during every run and is independent of any particular random weight initialization**
  
  Likewise our brain mostly processes color with left side of the brain.
  
  Are there other examples of emergent behavior in AI models that looks similar to natural neural systems? Any kind from coordination of several neurons to high-level function, usefull or detrimental, like optical illusions?
  
  So far I found only some articles with [optical illusion examples](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=deep%20learning%20net%20optical%20illusions&btnG=).
  
  **Tags:** training, neuroevolution
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2022-07-15. Score: 1 | Accepted answer
  
  Yes, there is a whole field where people compare the emergent representations of biological and artificial systems. It is very often found that the role of individual neurons is similar. For example, in the early layers of the visual pathway (retina, LGN, V1), we see receptive fields that are sensitive to local contrast, edges, center-surround and similar simple patterns. The same holds for early layers of CNNs. Intermediate layers both of the visual cortex and of CNNs are sensitive to increasingly complex stimuli such as grids, circles, complex patterns, etc., up to features like eyes, wheels, dog ears. At the last layer, CNN representations are semantic, i.e. coincide with labels (by construction). Equivalently, you find areas of the brain where there are semantic representations, i.e. cells that are sensitive to the concept of (famously) Jennifer Aniston, independently of how we see/hear/read of her.
  
  Just a couple of examples of literature I'm familiar with: [Banino et al.](https://www.nature.com/articles/s41586-018-0102-6) found grid cells, similar to those for which the Mosers won the Nobel prize for physiology, in a neural network trained for a spatial navigation task. People like James DiCarlo spent their careers on this kind of thing applied to the visual cortex, a summary can be found [in this paper](https://www.sciencedirect.com/science/article/pii/S089662732030605X), where they propose a challenge for finding the computer vision model that is most similar to the brain; but all of his work is relevant. In fact, an amazing fact is that CNNs trained to do the same task as an animal (vision), can linearly predict the activity of neurons in the animal's visual cortex *better than if the network was directly trained to predict those neurons' activities*. People also did similar things for [language models](https://www.biorxiv.org/content/10.1101/2020.06.26.174482v1) and for [audio](https://www.frontiersin.org/articles/10.3389/fnins.2022.843988/full).
  
  These are all works that compare single-neuron activations (receptive fields, preferred stimuli) between biological systems and artificial models. Of course other methods of comparison could be devised but it sounds like this is what you were interested in.
  
  
  ---
  
  