Reddit mentions of Neuromorphic and Brain-Based Robots

> Basically, just find a way to simulate a couple million cortical columns.

You should not confuse a "column" in HTM with a "cortical column" from a real mammalian cortex. We have reason to believe that a single cortical column is a feature detector. A cluster of columns act over milliseconds to suppress other, similar columns in a competitive manner. Inside real brains, a single cortical column is a highly interconnected group of neurons. (mostly fully connected).

You could take several hundred feature detectors as cortical columns. Then you can create singular cells that index a connection between two of them at a time. That is, the connecting neuron is active only when both of its constituent columns are active. These neurons that "index" two columns are called cortico-cortical neurons or CCNs. After the columns have "Settled down" from re-entry, the remaining active columns indicate the presence of a feature in the perceptive stimulus. The CCNs will be active if-and-only-if both of their indexed columns are still active. Let the number of columns be N. Then the number of CCNs is (N^2 - N)

Now for the tricky part. You index each CCN with a hippocampal neuron. The hippocampal neurons are in a fully-connected hopfield network. Given that a percept is a collection of co-occuring features, then the hopfield network will learn a robust pattern after exposure to it, as a collection of co-firings of the active CCNs. Here is a toy diagram of what you are trying to accomplish:



http://i.imgur.com/MSRwT4C.png


> Reward system : Nearly universally, learning is based on rewarding correct behavior and answers. How well would an infant learn that it needs to eat if it felt no hunger? How well does a neural network learn without some type of fitness system to keep it on track? For AI, you need some type of reward system.

This was already done by Rolf Pfeifer, Gerald Edelman, and again by Jeffrey Krichmar. In all cases, they did not use HTM networks or even "deep belief nets". Instead they modulated connections between various networks. Each network was associated with a modality and it was structured as a SOM, or Kohonen Self-Organizing Map.

• A diagram of Pfeifer's agent http://i.imgur.com/bTTUlAc.png
  
  
• Krichmar's book http://www.amazon.com/Neuromorphic-Brain-Based-Robots-Jeffrey-Krichmar/dp/0521768780
  
  
• Tutorial on SOMs. Make sure to click the barely-visible blue numbers at the button to move to next page. http://www.ai-junkie.com/ann/som/som1.html
  
  
  
• Krichmar's lab website http://www.socsci.uci.edu/~jkrichma/