Computational neuroscience class

This year didn’t start out so great for my online classes. I signed up and started a bunch, but quit all but one so far. Some were not as interesting as I thought, some didn’t contain enough new stuff or the material covered was too different from what I expected. I just couldn’t motivate myself to invest the time and effort to complete them. Maybe it’s not as exciting as it was for the first few classes or maybe these teachers are just trying out a new channel and are not as determined and enthusiastic about this new form of education. For me personally, the first MOOC that I completed, the introduction to AI is still the best.

Finally I found a class that I was keen enough to complete. That was about computational neuroscience. I read some books about neurology before, and was familiar with the basic structure of neurons and synapses as well as with some neuro transmitters such as GABA. But the details about ion channels and their detailed behaviour was new to me. The calculations with the spike voltages and spike triggered averages were very interesting. They highlighted to me just how simplified the common perceptron neural network models are. The second part of the class that was more about the application of the insight from the biological neuroscience into artificial intelligence and machine learning was more familiar and partly repetition.