Computer Simulations Reveal Deep Mysteries of the Brain

Researchers at Cedars-Sinai Medical Center have developed complex bio-realistic computer models of individual neurons. They are also suitable for the simulation of large networks of neurons.

"These models capture the shape, timing and speed of the electrical signals that neurons fire in order to communicate with each other, which is considered the basis of brain function," says research leader Costas Anastassiou in a press release issued by Cedars-Sinai Medical Center. "This lets us replicate brain activity at the single-cell level."

A paper is published in Cell Reports. The researchers report that, to develop the models, they used two different sets of data on the mouse primary visual cortex. This is the area of the brain that processes information coming from the eyes.

The first data set presented complete genetic pictures of tens of thousands of single cells. The second linked the electrical responses and physical characteristics of 230 cells from the same brain region. Machine learning methods have been used to integrate these two datasets.

The researchers emphasize that, contrary to previously developed computer models of brain cells, the new models are suitable for computational optimizations that allow for the simulation of thousands of brain cells. The researchers report that they created bio-realistic models of 9,200 single neurons and their electrical activity.

"This work represents a significant advancement in high-performance computing related to single-cell biophysics," says Keith Black, chair of the Department of Neurosurgery at Cedars-Sinai. "It also gives researchers the ability to search for relationships within and between cell types and to glean a deeper understanding of the function of cell types in the brain."

These models of brain cells are both bio-realistic and suitable for high-performance computer simulation of brain networks. This combination is expected to be a key complement to laboratory experiments.

"In laboratory experiments, the researcher doesn't control everything," explains Anastassiou. "Biology controls a lot. But in a computational simulation, all the parameters are under the creator's control. In a model, I can change one parameter and see how it affects another, something that is very hard to do in a biological experiment."

The researchers are persuaded that this work brings together mathematics, statistics, and computer science to address all the important questions in biomedical research and healthcare. The new models could one day allow scientists to answer questions about neurological disorders, and even human intellect, that aren't possible to explore through biological experiments. 

"Ultimately, this computational direction will help us understand the deepest mysteries of the human brain,” concludes Jason Moore, chair of the Department of Computational Biomedicine at Cedars-Sinai.