Reality Switch Technologies

Recently reading the book from Andrew Gallimore and two concepts make absolute sense

• one is the basis of universe as movement of information in a grid like space, where the physical objects as they appear are nothing but place holders for information, some information patters are static, some moving and some are infections or reproducing, some can combine to produce higher order and more complex networks, but at the base of every structure in space and its transference/ movement to any other structure are 6 quantum values - everything that exists is a function of that
• The second concept is of seeing the brain as a very high semi stable wetware of information which models the environment. AG initializes the concept that nothing in the environment is ever viewed directly but it acts as a initialization for the neural networks in the brain to initialize and create a useful model of the environment in a way to maximize the mutual information between the environment and itself - allowing it to find food, evade predators and reproduce. As long as three functions are satisfied, the brain keeps acting as reality mapping machine. It encodes sensory signals in cortical columns and T states (patterns of neuronal activity orchestrated by the thalamus). These T states resonate with something akin to a natural frequency when a familiar stimulus is matched giving a subjective feeling of perception, if the stimulus has novelty, error signals are generated and the reality model is updated to accumulate that novelty. Emotions and memory are simply encodings of these strong stimuli
• Since it’s already known that everything in the universe is a function of information (which includes holding and acquiring information), so this should be included in the first principle thinking - where by whenever we are trying to accomplish something we think of it as sharing / acquiring information to reach that end stage.
• Secondly brain as a reality encoding machine is a very interesting thought experiment, some thinking points:
  • If all we are doing is encoding environment stimuli, and we already have very good models of millions of stimuli, why can we not recreate them at will? Is it just a one way process where only the stimuli can recreate/ activate the model and our perception? Why is imagination not as vivid as getting inputs from the environment, is there anything that we can do to trigger these T states more efficiently?
  • If at the base learning is accurate encoding of neuronal firing patterns, does it make sense to first learn in isolation and then learn as we add more and more stimuli, eg learning the very basics, and then making it more and more complex, or directly jumping into something - starting from the basics gives us strong neuronal connections and then we can start to prune them to accommodate complexity
  • What are the factors that assist in better and more accurate encoding - how can we not create mistaken encodings - where we think the stimulus to be something but actually it’s something else? How can we utilize attention, focus and concentrations to get more true, accurate and vivid encodings?