A group of neuroscientists and software engineers at the University of Waterloo’s Computational Neuroscience Research Group (CNRG) have built the world’s largest functional model of the human brain. Named Spaun, the simulated brain has a digital eye which it uses for visual input, and a robotic arm that it uses to draw its responses.

The robotic arm is the only motor control system of the model. Researchers used Maplesoft’s simulation and modeling platform, MapleSim, to create the arm. Travis DeWolf, the University of Waterloo researcher who built the arm, attributes the success of the complex arm model to MapleSim’s symbolic computation power and model simplification capabilities. 

Spaun (Semantic Pointer Architecture Unified Network) consists of 2.5 million simulated neurons, allowing it to perform eight different tasks. Spaun has a 28×28 (784-pixel) digital eye, and a robotic arm which can write on paper. The researchers show it a group of numbers and letters, which Spaun reads into memory, and then another letter or symbol acts as the command, telling Spaun what function to perform. The output of the task is then inscribed by the simulated arm. Using the arm, the brain demonstrates tasks such as copy drawing, counting, memorizing and reproducing sequences, and fluid reasoning.  

Using MapleSim, Travis and the team constructed a 9-muscle, 3-link (shoulder, elbow and wrist) arm model. The muscles in the arm were constructed in MapleSim based on the Hill muscle model. “We were able to gradually, and very smoothly, increase the complexity of the model using MapleSim,” says Travis. “MapleSim allowed us to easily add in another muscle/link as we progressed, without losing any fidelity. This helped keep the overhead low, and allowed us to focus on developing the control system.”

While Travis considered other similar modeling and simulation tools, it was MapleSim’s symbolic computation capabilities that won him over. “In the other modeling software that we looked at, the underlying equations just weren’t accessible for analysis,” continues Travis. “With MapleSim, we had access to the symbolic equations driving the system, which meant we could get very accurate descriptions and do extensive analysis of the model. And the equations were automatically simplified in MapleSim, giving us a highly efficient simulation.”

The research goal for Spaun was to evaluate how different scenarios affected the output of the brain system. Other research using this same MapleSim arm model have examined modeling the effects of damage to the brain, caused by blunt trauma, Huntington’s disease, and cerebellar abnormalities. Results from this research can be applied to modeling new patient treatments. For example, the effects of deep brain stimulation, i.e., the process of inserting a wire through the brain to send electricity for treating Parkinson’s disease, can be modeled in this manner. Having a model such as Spaun will help in more in-depth and accurate investigation before treatment begins.


For more information on the use of MapleSim in robotic systems, download this whitepaper: