Technologies like array tomography also show how the human brain may be best understood as a computer that operates on both electricity and on chemicals. One section of the brain, the cerebral cortex, contains more than 125 trillion synapses. Boyles (2010) source material from the Stanford School of Medicine notes that the number of synapses in the brain is “roughly equal to the number of stars in 1,500 Milky Way galaxies,” (Goldman 2010).
Array tomography as a visualization instrument also reveals advancements in digital imaging as well as nanotechnology. The mouse brain used in initial array tomography experiments was sliced at only 70 nanometers thick (Goldman 2010). Measured at the level of the nanometer,, the layers of the brain that can be cut and then imaged with array tomography are small enough that scientists are able to understand more about how the brain works. Without technologies like array tomography, the brain would remain a complete mystery.
As the stanford scientists who worked on the array tomography project claimed, “The sheer number of synapses makes it nearly impossible to see them — even the best traditional-light microscopes cannot resolve them all,” (cited by Boyle 2010). The increased level of detail available in the extremely high-resolution images of array technology can be used for the development of new pharmaceuticals used to treat brain disorders. Neuroscientists can also use the information to understand more about memory, perception, and other brain functions. The technology can also be used to pinpoint specific problems in the brain and thus can be a powerful diagnostic tool.
Boyle, R. (2010). Video: 3-D Image Shows Brains Circuitry in Highest Resolution Ever. Popular Science. Retrieved online: http://www.popsci.com/science/article/2010-11/video-3-d-brain-image-highlights-neuronal-circuits-highest-resolution-ever
Goldman, B. (2010). New imaging method developed at Stanford reveals stunning details of brain connections. Stanford School of Medicine. Retrieved online: http://med.stanford.edu/ism/2010/november/neuron-imaging.html.