For the next few years and possibly decades, the development of brain emulation technology will occur alongside the development of neuromorphic technology.
Some teams will be primarily focused on achieving extremely accurate renditions of sections of actual brain tissue, as well as increasingly accurate neural maps which are sometimes based on high-throughput scans of actual brain tissue. These teams will wish to based their work on individual neurons and glia that are very much like actual cells.
However, Henry Markram, director of Europe’s Human Brain Initiative, has asserted that we need not model anything like the full complexity of gene expression and protein formation in human neurons in order to accurately represent firing patterns. Those pursuing the path toward WBEs will be willing to compromise on the issue of the level of detail at which individual cells are modeled, to varying degrees. Perhaps some will discover ways to measure whether these simplifications generate a statistically significant difference in how the simulated brain might react to stimulus.
Other teams will be more concerned with using models of groups of neurons as a calculation tool may be less concerned with whether they accurately represent individual neurons. “Neural net” technology was not intended to accurately model the brain, and the individual elements in a neural net are nothing like cells.
Nonetheless, these teams will learn everything they can from those who are trying to simulate actual brain function, and some of the same people will work in both sub-disciplines and different points in their careers.
If people from the simulation and human connectome camps develop an understanding of some new aspect of brain function, those who are just trying to find new AI methods to build into software will be able to take advantage of the results. However, they may be able to shave a lot of compute cycles out by utilizing abstractions of the newly-realized insight about structure and function to idealized neurons and glia that do not really try to approximate the function of living tissue much at all.
We cannot entirely predict whether extremely detailed models of individual cells are necessary for neuromorphic AI. However, I am interested in whatever evidence is available.
For the next few years and possibly decades, the development of brain emulation technology will occur alongside the development of neuromorphic technology.
Some teams will be primarily focused on achieving extremely accurate renditions of sections of actual brain tissue, as well as increasingly accurate neural maps which are sometimes based on high-throughput scans of actual brain tissue. These teams will wish to based their work on individual neurons and glia that are very much like actual cells.
However, Henry Markram, director of Europe’s Human Brain Initiative, has asserted that we need not model anything like the full complexity of gene expression and protein formation in human neurons in order to accurately represent firing patterns. Those pursuing the path toward WBEs will be willing to compromise on the issue of the level of detail at which individual cells are modeled, to varying degrees. Perhaps some will discover ways to measure whether these simplifications generate a statistically significant difference in how the simulated brain might react to stimulus.
Other teams will be more concerned with using models of groups of neurons as a calculation tool may be less concerned with whether they accurately represent individual neurons. “Neural net” technology was not intended to accurately model the brain, and the individual elements in a neural net are nothing like cells.
Nonetheless, these teams will learn everything they can from those who are trying to simulate actual brain function, and some of the same people will work in both sub-disciplines and different points in their careers.
If people from the simulation and human connectome camps develop an understanding of some new aspect of brain function, those who are just trying to find new AI methods to build into software will be able to take advantage of the results. However, they may be able to shave a lot of compute cycles out by utilizing abstractions of the newly-realized insight about structure and function to idealized neurons and glia that do not really try to approximate the function of living tissue much at all.
We cannot entirely predict whether extremely detailed models of individual cells are necessary for neuromorphic AI. However, I am interested in whatever evidence is available.