At Argonne National Laboratory, researchers are developing the next generation of computer neural networks. By more rationally modeling neuron activity, it is hoped that the elusive explanation of how epileptic seizures develop can be found.
Older neural networks treated each neuron as a fixed entity that could exist only in one of two states: firing or inactive. The more sophisticated models devised by Hereld [Argonne computer scientist Mark Hereld] and his colleagues treat each neuron as a pathway unto itself; they trace the route of an electrical signal from the fibrous dendrites into the cell body and out through the axon to other neurons. Rather than conceiving of each neuron as a single entity, Hereld’s model treats it as a data chain, where each link represents a different physical site on the cell.
Hereld’s model also offers another advantage over older neural networks. The “neurons” in the network are classified into one of six different groups, depending on their actual neurophysical role. The model also sorts the “wiring” of axons and dendrites that connects the cell bodies of different neurons into 32 separate types, each with different electrical and chemical properties.
The model has already produced findings that call into question some commonly held assumptions about how epileptic seizures arise. According to Hereld, conventional wisdom has long linked the onset of seizures to over-excitation of the brain’s network. However, he said, the Argonne model produces more epileptiform activity when the neurons have a lower excitation strength.
According to Hereld, models of neural networks provide a glimpse into epilepsy that complements information obtainable through clinical or laboratory studies. “There are some questions that simply can’t be answered by examining a live patient or looking at a small piece of brain tissue in the lab,” Hereld said. “Computing offers the possibility of changing any parameter to answer highly targeted questions about the fundamental causes of seizures.”
Press release: Neural modeling helps expose epilepsy’s triggers