Thursday, March 16, 2017

Thalamus Prediction

Concurrent Pattern Hierarchy

This is just a short post to make a quick prediction about the internal organization of the thalamus, a relatively small but complex area of the brain that is thought to serve primarily as a relay center between various sensors and the sensory cortex. Given my current understanding of the brain and intelligence, I predict that the parts of the thalamus that process sensory signals (e.g., the lateral and medial geniculate nuclei) will be found to be hierarchically organized. The function of the hierarchy is to discover small concurrent patterns in the sensory space. These are commonly called "spatial patterns" in neuroscience. I personally don't like the use of the word "spatial" to refer to patterns because I think it is misleading. All patterns are temporal in my view, even if they refer to visual patterns. Here are some of the characteristics of the thalamic pattern hierarchy as predicted by my current model:
  • The hierarchy consists of a huge number of pattern detectors organized as binary trees.
  • The bottom level of the hierarchy receives signals from sensors.
  • The hierarchy has precisely 10 levels. This means that the most complex pattern has 1024 inputs.
  • Every level in the hierarchy makes reciprocal connections with the first level of the cerebral cortex.
  • Every pattern detector receive recognition feedback signals from the first level of the cerebral cortex.
The cerebral cortex (sequence memory) can instantly stitch these elementary patterns to form much bigger entities of arbitrary complexity. A number of researchers in artificial general intelligence (AGI), such as Jeff Hawkins and Subutai Ahmad of Numenta, assume (incorrectly in my view) that both concurrent and sequential patterns are learned and detected in the cortical columns of the cerebral cortex. In my model of the cortex, the cortical columns are used exclusively for sequence learning and detection while concurrent patterns are learned and recognized by the thalamus.

Stay tuned.

Edit 3/16/2017, 2:42 PM:

I should have elaborated further on the binary tree analogy. I prefer to call it an inverse or upside-down binary tree. That is to say, each node (pattern detector) in the tree receives only two inputs from lower level nodes. Each node may send output signals to any number of higher level nodes. It is a binary tree in the sense that the number of inputs doubles every time one climbs up one level in the hierarchy.