Amazing stuff, the start of inorganic brain emulation/uploading? Maybe programmers could devise a program format for neuronal programming/uploading, which could be run on libraries for robotic platforms. Now we can truly code the "ghost in the machine".

Going a step further, what if we caused neuronal programs to evolve according to some fitness function, and gave them new kinds of sensors (senses?) and environmental challenges? We could end up with a complex brain, and some very curious emergent properties indeed...it makes me excited and worried all at once.

Digital brain + objective/incentive functions + evolutionary mechanics ==> hybrid natural-artificial life?

Quote from the creator about the roboworm's behaviour upon encountering an obstacle:

Obviously, the robot is a very simplistic form of the worm and there are many sensory inputs that aren't being taken into consideration. The worm's "nose" has sensory neurons for chemo, oxygen, touch, temperature, mechano, etc. I'm only using the "avoidance touch" but we have no idea how these other sensory functions might influence the behavior overall. Like any animal, the worm won't back up each and every time it comes to an obstacle; i.e. it might kind of "sniff" around it for a bit before moving on so we are taking a very narrow approach mostly due to the limitations of robotic sensors. However, I have probably ran this experiment over a hundred times and each time, the results are very consistent. Sometimes. I'll let the robot just wander around for a long period of time and it's like we have a cat checking out the environment.﻿

On memory/plasticity of the simulated brain:

Actually you are the first to ask and I'm surprised no one has asked before. We actually have a mechanism we call the "poison pill" whereby we send the individual neurons of the simulated connectome a weighted value of -99999 and when the neuron reads this weight, it kills itself. So to answer your question, this current research is based purely on the connectome as the means to operate the robot and there is no plasticity other than the recursive nature of the connectome itself. No memory storage and each time you start the connectome, it starts new but we have probably did this experiment a 100 times by now and have shown that the connectome itself gives the wormbot it's ability to maneuver it's environment. Thanks for the question.﻿

On human control of such robots:

One of the things that we want to explore is adding a Deep Learning app to the connectome to be able to guide the robot so we can make some more practical use for the system. The current research gives the robot the ability to do it's own thing like any animal. As I have already stated, it's like a cat whereby you can try to command a cat all day but it will do what it wants to do and the way we get cats to do what we want them to do is to change the environment; e.g. put out food. For example, if we were to create a bot specifically for search and rescue, we might want to give the bot some incentive to go a specific direction and if life is found, we may need it to perform specific tasks to ascertain the situation. My connectome gives the bot the ability to maneuver around in an environment and keep it safe from harm but it would do, like the worm, it's own thing and not pay much attention to what we wanted it to do. By interjecting some control (ala a DNN?), this might give us the best of both worlds: a bot that can fend for itself as well as some control that gives us a means to obtain certain information. So your idea is very much in line with some of our thoughts as well. The only difference would be that you must realize, this is truly an autonomous system and as it is right now, you can't control it any better than you could control a real worm or your cat.﻿