Text written by Nicholas Colb
This week we learned about the action potentials and synaptic transmission, and the chemistry behind these phenomena. I found it extremely interesting that the interaction between neurons can be explained thoroughly by examining particles on a molecular level, and that the relatively uncomplicated concept of the flow of ions is what essentially creates our intricate reality of thoughts, feelings and senses as well as all of our voluntary and involuntary bodily functions.
Furthermore, we learned about the general structure and organization of the mammalian brain. It was fascinating to see how much the brains of animals, such as rats or sheep, resemble the human brain. While at first glance the abundance of complicated terms did seem overwhelming, it was nice to realize that the terms were surprisingly easy to internalize. Compared to, for example, learning a new language and memorizing hundreds of words and grammatical structures, the anatomical terms of the brain seem relatively apprehendable (ironically, to the brain itself).
What sparked my imagination in this week’s readings was the introduction of neural activation maps, especially of the olfactory cortex. If it is so that each distinct odor triggers activity in certain subsets of neurons, and that we can map these neurons with modern imaging techniques, would it be possible to utilize these images in machine learning to predict which neuronal combinations are universally related to each odor? If this would indeed be possible, could these sections of neurons be artificially activated in the human brain to create a sense of odor?