Summary: "Growing up with Lucy"

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This article summarizes a number of key concepts that are found in Steve Grand's book "Growing up with Lucy". This book tells the story of his journey to build an android with "artificial intelligence" in order to try to understand mammal-like brains and intelligence.

• "Synthesis can be more powerful than analysis", especially with emergent properties.
  (page 38,47)


• Recurrent neural networks (i.e. with feedback loops) have more interresting properties than simple feed-forward ones. They have complex dynamic internal states.
  (page 33)


• There are three or four possible architectures in nature.
  1. Ad hoc networks which suffice for very primitive creatures, with the neuron as basic building block.
  2. Repeated circuits of neurons for insect-like intelligence, reconfigurable by evolution.
  3. Arrays of neurons (maps) for mammal-like intelligence, reconfigurable during the creatures own lifetime.
  4. Switching information between existing maps at will for human- and primate-like intelligence.
  (page 45)


• Brains and our sensory and motor systems have co-evolved.
  (page 56)


• Simulating an arm (with a pair of muscles) using a servo attached to the joint by springs and translating the desired muscle tensions in an offset between the position of the arm and the position of the servo.
  (page 59-60)


• The retina 'enhances' edges in the image, because edges are independent of the absolute brightness of the image and lightning conditions. Edges isolate objects from each other and from the background.
  (page 77)
  


• Each eye transmits two 'images' to the brain. One telling how much darker each pixel is than its neighbors, and one telling how much lighter each pixel is compared to its neighbors.
  (page 79)
  


• Brains are predictive and not reactive to the environment, because of signal delays for one reason. To keep the eyes focused on a flying bird the eyes need to be turned to the position where the bird will be after the eye muscles received the the signals from the brain and moved the eyes.
  (page 106)
  


• The six layers (I, II, III, IV, V and VI) in the cerebral cortex can (probably) be simplified in to four layers (A, B, C and D) model, where each layer either receives input from higher cortex maps (layer A), outputs to higher cortex maps (layer B), receives input from senses or lower cortex maps (layer C), or outputs to motor systems or lower cortex maps (layer D). Thus several cortex maps can be chained together and form a tree (or network) structure.
  
  (page 123-125)
  


• Each cortical map could be seen as a servo. One input (to layer C) is the sensory input, one input (to layer A) is the desired set point, and one output (from layer D) is the motor output. The second output (from layer B) is the feedback to the secondary cortical maps.
  (page 136)
  


• In a predominant sensory chain of cortical maps the flow from higher to lower maps (and motor output) represents attention or anticipation, whereas in a predominant motor chain it represents intention.
  (page 137)
  


• In the virtual cortical map V1 the neurons are grouped in semi-regular clusters with each neuron detecting an edge in a different orientation. This is achieved by distant neurons inhibiting eachother, and nearby neighbors exciting eachother.
  (page 150,153)
  


• The cortical map V2 around V1 might turn an object centered image produced by V1 into a size and orientation independed 'time signature' from the shape of the object. The radial stripes of V2 might collapse the projected image to the center, whereby the number of 'pixels' reaching the center at any one time creates the 'time signature'.
  (page 162-163)
  


• The developing retina of a child helps (self-)organizing the neurons in cortical map V1 by generating waves of activity in the absence of light. These waves are generated by each neuron exiting its neighbors, except for the ones recently fired.
  (page 170-171)