Connectomic AI

     I 've long conjectured that the reason we have an inhibitory network in our brains is to prevent an epileptic burst and it seems that this thought has some truth. In my early simulations of neurons, I found quickly that excitatory networks would get to a tipping point where they would just start firing uncontrollably; i.e. every neuron (node) would be firing as fast as it could and the output would end up being a jumbled mess of motor neurons all firing, all the time. I even continue to experience this sometimes in more true emulations due to the timing or the number of neurons aren't in a correct balance so that the inhibitory neurons aren't in a great enough strength to overcome the excitation.  In AI/Deep Learning (et al), inhibitory networks don't exist. The excitatory network is refined by the error factors and outcomes are created by numeric voting. DL tunes an excitatory network by weighted factors.  There is a delicate balance between the excitatory...

     I feel bad that I didn't write down the scientist that said "AGI must start with the stomach" but he is so right. 99% of AI (ANN/GOFAI/DL/Neurosymbolic/ et al) starts with sensory input and 99.99% attaches the AI to motor activity if it has something to do with robotics, where that motor activity is a direct result of everything sensory. In current AI, motor activity is always secondary to sensory input and most often, AI doesn't even put motor activity as a requisite.  Let me flip the script and instead of starting with sensory input, let me start with motor activity. The reason AGI starts with the stomach is because when we are hungry, we need to move. As an infant, we begin sucking on anything that represents a nipple. As we get older, we grab anything that we know we can eat and put it in our mouths. Even older, we go to the kitchen and make us something to eat. The drive to squash the hunger pain is the reason we learn. To eat in any instance, requires us ...

One of the surprising revelations that I discovered while preforming emulations of animal connectomes is the huge amount of recurrence. The brain literally folds back on itself over and over again.  To illustrate this idea, let's look at C elegans again. Keep in mind that we are working with 302 neurons. Neurons connect to a set of other neurons. This connected set has a number of axons that link back to the originating neuron. If we look at the Interneuron AVAL, AVAL connects to 77 other neurons. These 77 neurons connect to another set of neurons, so on and so on. Each neuron can have multiple connections to other neurons. For example, AVAL has 11 synaptic connections to the neuron DA6. From the first set of neurons that AVAL connects too, there are 83 total synaptic and gap junction connections from those 77 neurons back to AVAL. If we take those 77 neurons and follow what neurons they connect too, we find from that second set (Layer 2), there are 573 neuronal connections back to...

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 ...