Connectomic AI

    J ust when you need it, I created a new acronym = Emulasim. I define it as not a true emulation but a step up from a simulation like halfway between simulate and emulate. I believe AGI falls somewhere in this category. From my own experiments, I emulate the connectome from given datasets and do a halfway job of emulating the neuron. So today I want to discuss Neuron emulasim. In my C elegans experiments and other lower forms like Ciona, I used a simple model of the neuron. The attributes are: The artificial neuron accumulates weights where weights are defined as the number of synapses and gap junctions that are connected between the presynaptic and postsynaptic neurons; e.g. if Neuron A has 3 synaptic connections to Neuron B, when A fires, it sends a message to B of a weight of 3. A specified threshold must be met within a specified time frame in order for the neuron to fire. If the threshold is not met in a specified time frame (e.g. 200ms) then the neuron hyperpolarizes by either

    L et me begin this post by stating that what is contained within is a simplified idea of the actual coding and there are specifics I am leaving out due to the amount of information would be better contained in a book rather than a blog post. Here I am attempting to highlight concepts and not discuss in detail. Maybe later discussions will allow me to do so?  The key with emulations of nervous systems is real time emulation if you are wanting real life AGI The first order of emulation is machine constraint. When I first did connectomic experiments, pretty much every machine I worked with could only support 250 processes running at a given time (16-bit). I had lunch with a colleague one day in 2013 complaining about this limitation when he asked, "Don't you have a 32 bit system now?". I did so he told me that he bet I could have many more processes running under that architecture. When I got home, I created a quick app that started up 302 notepad.exe and there were no