His name is Spaun, which stands for Semantic Pointer Architecture Unified Network, and he's a brain -- a simulated, brain. His 2.5 million neurons, organized in subsystems that simulate different brain areas, allow Spaun to perform tasks such as image recognition and recalling sequences, and respond through a motor arm. For example, Spaun can recognize numbers on a screen and write them on a piece of paper.
Spaun is the brain child (pun intended!) of authors Eliasmith et al. . It models three specific brain areas: the prefrontal cortex for memory, the basal ganglia to select actions, and the thalamus. Spaun's functional architecture consists of a working memory that, given a visual input, compresses the information and translates the input into firing patterns. The next step is the action selection step, which results in a motor output through the robotic arm. Spaun's memory doesn't just store information, but it also correlates new information with the old one. A nice feature of the model is that different neuron parameters can be chosen from random distributions in order to simulate different population behaviors. This simulates the human brain so well that Spaun expresses a common human behavior: the tendency to remember best the first and last items in a list.
On the other hand, Spaun exhibits noteworthy deviations from human brains: while it can get better and better at a particular task, it cannot learn a completely new task. Another shortcoming is that Spaun's attention and eye position are fixed, so that, contrary to a real human brain, it cannot control the input.
As the authors explain:
"Anatomically, many areas of the brain are missing from the model. Those that are included have too few neurons and perform only a subset of functions found in their respective areas. Physiologically, the variability of spiking in the model is not always reflective of the variability observed in real brains. However, we believe that, as available computa- tional power increases, many of these limitations can be overcome via the same methods as those used to construct Spaun."
 Eliasmith, C., Stewart, T., Choo, X., Bekolay, T., DeWolf, T., Tang, Y., & Rasmussen, D. (2012). A Large-Scale Model of the Functioning Brain Science, 338 (6111), 1202-1205 DOI: 10.1126/science.1225266