Connectomic AI

Caenorhabditis elegans (C elegans) Hermaphrodite Wild Type has a fully mapped connectome of 302 Neurons. C elegans is the most studied animal on earth. The connections and connection types (Chemical and Electrical), and the function of each neuron is well known (Sensory, Interneuron and Motor Neurons) Nervous systems are messy, noisy systems, each neuron works independently of one another, and I wanted to recreate that system as close as possible. We have this vision that neurons are nice, neat tubes but in reality, they are like a play-doh snake someone has smashed and squished. I created a single program that can assume the identity of each of the 302 neurons that make up the C elegans connectome. Each of the 302 individual programs message to the other programs via User Datagram Protocol (UDP). UDP is like TCP but doesn’t care about packet loss. UDP is how most video and audio is transmitted over the internet.  To simulate connections, I used the number of connections as a weighted

What is Connectomic AI?   I define Connectomic AI as an Artificial Intelligent system based on connectomic/synaptic modeling. This can be created using actual connectomic maps or using the essence of connectomics. Unfortunately, complete connectomic maps are very scarce. C elegans has been around since 1986 (White et al) and there has been a great surge in mapping the neural connections in recent years. If you are aware of any connectomic data sets, complete or partial, please share in the comments. In another post, I will put together a list of sites.  Practical Connectomic AI   I believe by emulating connectomics, we can realize Artificial General Intelligence (AGI). I emulated the C elegans connectome in 2013-2014 and to my surprise, the robot acted like the biological worm. The C elegans nervous system is a very simple 302 neurons. This emulation has been replicated many times using different robots and different programming languages. It is remarkable that just the connectome, or