New Study Finds A Single Neuron Is A Surprisingly Complex Little Computer

Comparing brains to computers is a long and dearly held analogy in both neuroscience and computer science. Deep learning, a powerful form of artificial intelligence, for example, is loosely modelled on the brain’s vast, layered networks of neurons.

You can think of each “Node” in a deep neural network as an artificial representation of a neuron. Like neurons, nodes receive signals from other nodes connected to them and perform mathematical operations to transform input into output.

Scientists know biological neurons are more complex than the artificial neurons employed in deep learning algorithms, but it’s an open question just how much more complex.

In a fascinating paper published recently in the journal Neuron, a team of researchers from the Hebrew University of Jerusalem tried to get us a little closer to an answer.

While they expected the results would show biological neurons are more complex-they were surprised at just how much more complex they actually are.

In the study, the team found it took a five- to eight-layer neural network, or nearly 1,000 artificial neurons, to mimic the behavior of a single biological neuron from the brain’s cortex. Though the researchers caution the results are an upper bound for complexity-as opposed to an exact measurement of it-they also believe their findings might help scientists further zero in on what exactly makes biological neurons so complex.

To computationally compare biological and artificial neurons, the team asked: How big of an artificial neural network would it take to simulate the behaviour of a single biological neuron?

The model used some 10,000 differential equations to simulate how and when the neuron would translate a series of input signals into a spike of its own.

The sweet spot was at least five layers but no more than eight, or around 1,000 artificial neurons per biological neuron.

“Take away one of those things, and a neuron turns into a simple device,” lead author David Beniaguev tweeted in 2019, describing an earlier version of the work published as a preprint.

Idan Segev, a coauthor on the paper, suggests engineers should try replacing the simple artificial neurons in today’s algorithms with a mini five-layer network simulating a biological neuron.

“We call for the replacement of the deep network technology to make it closer to how the brain works by replacing each simple unit in the deep network today with a unit that represents a neuron, which is already-on its own-deep,” Segev said.