Damage to the hippocampus at the base of the brain can leave a person unable to form new memories. One solution to the problem that is nearing testing is to build a chip that performs all the functions of the hippocampus.
The world's first brain prosthesis - an artificial hippocampus - is about to be tested in California. Unlike devices like cochlear implants, which merely stimulate brain activity, this silicon chip implant will perform the same processes as the damaged part of the brain it is replacing.
The prosthesis will first be tested on tissue from rats' brains, and then on live animals. If all goes well, it will then be tested as a way to help people who have suffered brain damage due to stroke, epilepsy or Alzheimer's disease.
A team led by Theodore W. Berger of USC spent 10 years to build a mathematical model of the hippocampus and then to program it into a silicon chip.
Slices of rat hippocampus were stimulated with electrical signals millions of times, until scientists could be sure which input produced a corresponding output.
Putting the information from each slide together, the researchers were able to devise a mathematical model of a whole hippocampus.
The model was then programmed on to a chip.
From the University of Southern California web site of team leader Theodore W. Berger:
The research of Dr. T.W. Berger involves the complementary use of experimental and theoretical approaches to developing biologically constrained mathematical models of mammalian neural systems. The focus of the majority of current research is the hippocampus, a neural system essential for learning and memory functions. The goal of this research is to address three general issues: (1) the relation between cellular/molecular processes, systems-level functions, and learned behavior; (2) the extent of which the functional dynamics of neural systems are altered by activity-dependent synaptic plasticity; (3) the extent to which the essential functions of a neural system can be incorporated within a hardware representation (e.g., VLSI circuitry).
Experimental studies involve the use of extracellular, intracellular, and whole-cell electrophysiological recording techniques, applied in vivo using anesthetized and chronically implanted animals, and in vitro using hippocampal slice preparations. A number of neurobiological issues are being investigated, including: (1) quantifying the signal processing capabilities of hippocampal neurons and the extent to which these capabilities reflect regulation due to feedforward and feedback circuitry vs. intrinsic neuronal mechanisms, such as voltage-dependent conductances or second messenger biochemical systems; (2) the spatio-temporal distribution of activity in neural networks and its dependence on input pattern and network connectivity; (3) the cellular mechanisms underlying changes in the strength of connections among neurons, i.e., synaptic plasticity, and the influence of synaptic plasticity on signal processing characteristics of neurons and the spatio-temporal distributions of activity in networks.
These and other experimental studies are used in conjunction with several different theoretical approaches to develop models of: (1) the nonlinear, input/output properties of single hippocampal neurons and circuits composed of several populations of hippocampal neurons (in collaboration with Dr. V. Marmarelis, Biomedical Engineering, USC), (2) the hierarchical relationship between synaptic and neuronal events (in collaboration with Dr. G. Chauvet, Institute for Theoretical Biology, University of Angers, France), (3) the kinetic properties of glutamatergic receptor subtypes, and (4) adaptive properties expressed by the "hippocampal-like" neural networks implemented with analog VLSI technology (in collaboration with Dr. B. Sheu, Electrical Engineering, USC).
Suppose the initial tests on rats are successful and the group wants to move onto trying it in humans. There seems to be a problem with how to get patient consent. People who can't form new long term memories may be unable to have the treatment explained to them well enough to be able to evaluate the risks and potential benefits.
Still, this is weird wild stuff. If a chip can be made to emulate the hippocampus can the chip's algorithms be improved upon to make it better than the hippocampus? Could it be turned up to stimulate learning when one is studying material one needs to remember?
|Share |||Randall Parker, 2003 March 12 08:52 PM Cyborg Tech|