Keene Macheene

This page is to collect references to papers I find important. My old papers Original SRS Paper. (A copy from a very old Word file into blogger) 1995 https://thunderfist-podium.blogspot.com/2014/08/new-cognition.html Original avalanche pattern recognition article https://thunderfist-podium.blogspot.com/2014/08/avalanche-pattern-recognition-in-neural.html General Papers Classic spiking neurons paper https://www.izhikevich.org/publications/spikes.pdf Brain size, neuron counts, and intelligence. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2776484/ Evolution of architectures in CNNs. https://arxiv.org/pdf/1806.09055.pdf Synaptic Pruning both destructive and constructive. https://www.cs.princeton.edu/sites/default/files/uploads/deborah_sandoval.pdf

What is the purpose of a brain and how does that effect the evolution of design? Survival It is obvious that the reason any animals have neural systems is because the neural system provides a survival advantage. How does a brain provide an advantage? The simplest neural systems are in effect hard-wired with little or no learning. These simple systems provide reactions or reflexes to environment changes that may be harmful or beneficial.  Attraction toward food or mates, and fleeing from predators is very valuable. As neural systems become more complex and have learning on can ask: What is being learned? The answer is often stated as "better behaviors" and such. A better formulation would be: Brain impart a survival advantage by predicting the future state of the  environment  and pre-reacting to the environment. Here are several examples at varying levels of intelligence: A mouse is hungry and predicts a future state that includes food, and pre

In a Subsumptive Regular System (SRS  here )  the  regular array of neurons (cortex) that has the goal of predicting the future state of the subsumptive (cerebrum) system . If the match is strong on the present state of the subsumptive system then the stimulus of the subsumptive system into the predicted state is strong.  In the event of sudden changes in the actual external environment one wants to ignore the predicted state and react to the immediate environment. This has an evolutionary advantage of pre-reacting to the environment and getting a jump on surviving the future state. This document calls out the various axis of selection for algorithms.  Research has yet to show where the decision points are optimal. There are several decision points on the algorithms to select: Connectivity from the Subsumptive to the Cortex The subsumptive part of the SRS is hard-wired to take inputs, compute concept mappings (abstractions) of the external environment and generate outputs or re

Neural models have trade offs with speed, efficiency of computation, and ease of use. There are two major domains for neural models: Pattern recognition and computation, and robot control. Spiking neurons have the following advantages: Spikes that only occur once over many time ticks do not require getting memory into cache for the target neurons, and thus are computationally efficient. Spiking neurons can do bursts. Spiking neurons make triggering behavior in a robot simple to implement. Spiking Neural models are inherently forward additive, imply Neural Hiding, and subsumptive architectures. Subtle timing of spikes can result in information encoding by spike arrival times. Drawbacks to Spiking Neurons: Integration over several time ticks are required to get intermediate levels of activation. The math is more complex; Charge, Activation, Burst and other variables must be tracked. Linear Neuron Advantages: (Neurons that transfer activation levels multiplied by weight

Just a simple thought: The Feathers Problem One of the challenges in the invention of powered flight was deciding what matters most.  If one wants to imitate birds there are many factors: Wing curvature Wing warping or twist Flapping Feathers Jumping into flight from the ground Energy source and many more. Some early pioneers of flight proposed feather covered wings.  After all, that is what birds have.  Flapping was tried also. In the end the Wright Brothers figured out that the salient factors were lift, drag, thrust, and weight. The lift was generated by wing curvature, not the fact there were feathers. So the first working airplane (and first gliders that worked) did not have feathers. The other solutions they came up with were light weight engine, wing warping (flaps came later) for control, no flapping but instead propellers, linear sliding take off rather than a jump, solved the learning cure issue with learning to fly a craft without any craft working yet. In A