MapleSim used to create 'largest model of human brain'

29 July 2013

Neuroscientists and software engineers at the University of Waterloo’s Computational Neuroscience Research Group (CNRG) have built what they describe as 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, the output system, is the only motor control system of the model. Researchers used Maplesoft’s simulation and modelling 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 aArchitecture 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, memorising and reproducing sequences, and fluid reasoning.  

Using MapleSim, Travis and the team constructed a nine-muscle, three-link (shoulder, elbow and wrist) arm model, based on the model presented in a paper by Kenji Tahara. The muscles in the arm were constructed in MapleSim based on the Hill muscle model. The controller was modelled in Matlab, and MapleSim’s connectivity to Matlab via the Maple engine provided seamless integration between the two systems.

'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.'

Related internet links

Computational Neuroscience Research Group