The Virtual Brain

Technology certainly seems smarter than it was a decade ago. Most smartphones can now listen and talk. Computers are getting much better at interpreting images and video. Facebook, for instance, can recognize your face if you’re tagged in enough photos. These advances are largely thanks to machine learning, the technique of writing algorithms that can be “trained” to recognize images or sounds by analyzing many examples.

Let’s start at the bottom. Much is known about the brain’s connections at the cellular level. Each neuron gathers electrical signals from those to which it is connected. When the total incoming current is high enough, it sends out an electrical pulse. That pulse is the neuron firing, also called spiking.

When a neuron fires, it provides input to the neurons connected to its outgoing “wires.” In terms of computers, that’s a processing behaviour – the neurons filter their incoming signals and decide when to send one out. But the connections between the neurons, called synapses, change depending on the pulses that came before. Some pathways get stronger while others get weaker. And that, in computer terms, is memory. Simple enough, right? Except the best estimates for the number of neurons in an adult human brain are in the tens of billions, with thousands of synapses per neuron.

The virtual brain, called Spaun, can turn a handwritten image into the idea of the number, Spaun knows that it’s a number 2 and that 2 comes before 3 and after 1. All that kind of conceptual information is brought online. Spaun can then use that idea to answer questions, some of which you might find on an IQ test. For instance, in one of the challenges highlighted in the team’s videos, Spaun is presented with 4:44:444, 5:55:? Spaun figures out the pattern in the digits – an abstract pattern that only humans can recognize.

The neurons were divided among 21 individual networks representing different parts of the brain, and these networks were linked up to reflect how the equivalent sections of the brain are connected. These include the different levels of visual processing in the brain, portions that turn an image of a number into the idea of a number, parts that manipulate that idea and areas responsible for moving the virtual arm. Intriguingly, Spaun is human-like in the way it makes mistakes. For instance, when recalling a long sequence of numbers, Spaun remembers the first and last numbers best, and gets a little fuzzy with those in the middle.

Spaun also consists of spiking neural networks, creating a two-tier system. While the higher tier is focused on learning the goal, the lower tier is concerned with the steps needed to reach that goal. The hierarchy makes it possible to apply previously learned skills to new tasks.

This virtual brain is designed by Chris Eliasmith of the University of Waterloo in Ontario, Canada.