Abstracting the Brain: A Simpler Emulation?

Researchers at Washington University in St. Louis have taken a three-neuron feedback microcircuit model and abstracted the microcircuit to a single "neuron" that behaved in an identical way to the more complex microcircuit. In other words, without losing any complexity of behaviour, they have simplified their model. This is an important concept for cognitive scientists who wish to simulate brain behaviour with hardware. Being able to conceptualise simplified models exhibiting an equal behavioural richness, will allow scientists to design elegant new hardware that should be both faster and use far less energy to operate.

Looking for a way to explain to the students in his Physics of the Brain class how delayed feedback produces complexity in a circuit, Ralf Wessel, Ph.D., associate professor of physics in Arts & Sciences, came up with a toy neural circuit simple enough to be stepped through time iterations at the blackboard. He used it to show his students that if there was feedback among the neurons, simple constant inputs could produce a long-period oscillation in their outputs.

Wessel then asked Matthew S. Caudill, Ph.D., graduate research assistant in physics, to create a computer model of the circuit so that it could be explored more thoroughly. As they worked with three-neuron microcircuit they realized it was very like one students in Wessel's neurophysiology lab were studying.

That circuit, which consists of three neurons and their feedback projections, has a simple task: to detect motion in the chicken's field of view. One neuron in the area called the optic tectum because it sits on the "roof" of the brain, sends axons to others in a knob of tissue called the nucleus isthmi. The neurons in the isthmi send projections back to the optic tectum, either directly to the neuron from which they got their input or back to the rest of the tectum (the crucial feedback loops).

There are similar microcircuits in the optical processing areas of reptilian and mammalian brains.

The microcircuit's behavior could be captured mathematically by three equations, each of which describes one neuron's output in terms of its inputs and parameters called synaptic weights, the standard way of expressing the strength of the connection between two neurons. Looking at the equations, Wessel and Caudill recognized that they could be reduced by algebraic substitution to one equation with two parameters (derived from the original five synaptic weights).

"It is as if," says Caudill, "the system of three neurons was reduced to one abstract neuron that does the same thing, follows the same rule, as the more complicated circuit. " _Physorg

This is a long way from a cortical lobe -- or even a cortical column -- but it is a conceptual foundation that may allow for further abstraction.

Most people who understand both the brain and computers, will understand that new hardware and software is absolutely necessary to better approach the brain's level of complex (but remarkably stable) behaviours. But understanding how to build the architecture for the new hardware has been slow in coming.

It is the opinion of Al Fin cognitive scientists that much of the brain's complexity is a red herring, a distraction from what the brain is actually doing. We will have to approach this "essence of braininess" from both the top-down, and the bottom-up.

Henry Markram's "Blue Brain" approach includes all the complexity of neuronal processes and ion channels because it is meant to simulate brain function well enough to demonstrate actual brain pathology -- such as Parkisonism or Alzheimer's. Most cognitive scientists just want to design a machine that can intentionally initiate learning, thinking, and acting much like a human does.