Connectome Research Timothy Busbice, Updated: 03/10/2014 History For the past 20+ years, I have tried to create a connectome using individual programs to represent neurons whereby each program exhibits the common but unique characteristics of the individual neurons. In each iteration of this research, I have discovered the machine limitations of the type of systems that I was using, mainly the restriction of the number of processes that are allowed to run simultaneously on a given processor. In addition, until I worked on the OpenWorm project, I did not have a true connectome to work with and my previous attempts were basically made up neural networks that did not yield any meaningful results. Talking to a colleague a in December 2012, made me realize that using 64 bit technology may have changed the limitation of the desktop PC. After doing a quick test, I found that indeed it has and that I could create a system of 302 neurons running on my Personal Computer.

Connectome Research The Process In the simplest terms, there are three components: Sensory Input, the Neurons that make up the connectome, and the Output which is muscle activation/movement.

Connectome Research InterProcess Communication UDP = User Datagram Protocol To talk between the apps, I use UDP (a cousin to TCP). ”With UDP, computer applications can send messages, in this case referred to as datagrams, to other hosts on an Internet Protocol (IP) network without prior communications to set up special transmission channels or data paths.”datagramsInternet Protocol In other words, UDP is Asynchronous! *

Connectome Research The EV3 Robot is used to test the Connectome Using the Lego Mindstorms EV3, we can connect Robotic Sensors to the Connectome and use muscle output from the Connectome to drive the robotic motors.

Connectome Research Sensory Input An Input App that reads the robot sensors and then stimulates sensory neurons with weighted values. I use touch sensors for anterior and posterior body touch, sonar for nose touch and a sound sensor to simulate food or chemosensory presence.

Connectome Research Individual Neurons Each Neuron is spawned by reading an MS SQL Data Base table that defines which Neuron it is and what Neurons it links to by weight. App Elements: Socket (Port) Number is unique for each Neuron Neuron Name Receive IP Address Weight Received Linking Neurons : Port # : Weight

Connectome Research The Connectome Engine The connectome engine is highly integrated and recursive based upon the biological model. The image below displays how 5 of the 302 neurons link to one another.

Connectome Research Muscle Output Neurons have defined links to specific Muscles which in the prototype system terminate at a motor out app. The Motor app is designated as UDP Port The accumulated left and right totals are used to drive the two robot motors. Each of the muscles is defined in a matrix and incoming weights are accumulated and displayed

Connectome Research Insights When the robot is activated and the connectome engine is employed, we observe repeated predictable behaviors displayed in the biological C Elegans, mainly: Food sensing drives the robot forward Nose touch sensing causes the robot to reverse and change course Body touch sensations cause changes in behaviors The greatest insight is the fact that the robot is driven by the connectome alone. There is no program that tells the robot to stop, back up and change direction when the sonar (nose touch) stimulation occurs – the connectome is driving this behavior. Challenges Running the Input/Output programs on a 64 bit Laptop, and the entire connectome engine on a Dell desktop, causes latency issues due to the tremendous amount of UDP traffic going on simultaneously. This skews observation of behavior over time due to sensory input not being acted upon for several seconds later. To remedy this issue, I am looking to distribute the neurons and Input/Output programs over a larger array of computers and using at least a gigabit network.

Connectome Research Next Steps? Distribution of processes as indicated on the previous slide to reduce the workload of any given processor and achieve true parallelism. The best case scenario would be 302 machines, each with one neuron but we may start with 4 to 10 machines for the connectome engine so that 50 to 30 neurons are running on each system respectively. Refine the Neuron app to match closer to timing and internal processes (e.g. Neuropeptides) of actual C Elegan neurons. I have already stated to colleagues that I can see at least two mirror programs for each neuron, one for chemical synapses and one for electrical (gap junction) connections. I have created a Python version that I call the Simple Connectome Model and have shown it to work with the EV3 Robot. I am working on a “Web Service” model that is based upon the Python model so anyone can plug into and use the C Elegans Connectome.

Connectome Research Connectome Engine Project: Youtube: Several Videos that display the robot using the connectome Contact: Timothy Busbice,