Keene Macheene

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...

Got the ConceptMap editor working such that one can create Concept Maps, drag connections and make connections.  Also have the ImageInputDrive (a sub-class of Concept Map) pretty much working. Got stuck for a bit in some WinForm layout issues and scrolling the editing area.

In computer programming Object Oriented Programming (OOP) gave programmers a neat way to compartmentalize programs, rather than having a mess of disorganized code and data.  One can neatly make an object, treat it as a black-box and reduce the cognitive load on the programmer.  Nifty. In reactive neural networks where one is taking world inputs and generating response outputs Subsumption vastly simplifies the work of designing the neural processing. From dictionary.com: verb (used with object),   sub·sumed,   sub·sum·ing. to  consider  or  include  (an  idea,   term,   proposition,   etc.)  as  part  of a  more   comprehensive   one. to  bring  (a  case,   instance,   etc.)   under  a  rule. to  take  up  into  a  more   inclusive   classification. Subsumption in the context of neural networks is where some ...

Current neural network systems have a general paradigm of being given some input, and then producing a resulting output. A true general AI should act like a biological system and be self-sequencing. The neural system should just free-run, responding to inputs and creating outputs and learning. One method I am experimenting with in Cognate and NuTank is having a hard-wired set of concept maps (a N-dimensional set of neurons used to embody a concept, e.g. foveolar edge angles.) that take inputs from the external world and produce hard-wired reactions as outputs to control a real or simulated robot. Such a system also uses subsumption to aid in much easier design. This is the Concept Map System (CMS) for the purposes of this post. For now lets ignore the idea that the hard-wired concept map system might also be adapting and evolving over time simulating neroplasticity. One then has a large 3D array of neurons that are in acting as a pattern matcher on the entire CMS.  What this ...