Could physically simulating neural structure on a fundamental level yield superior results to machine learning algorithms? My curiosity is merely of whether (in a future where we have computers with the processing power of the human brain) it's possible that actually simulating a neural network's physical behavior on the most fundamental level might be more effective than using algorithms to reach the same result.

I'm hoping for an answer which argues this topic on a purely fundamental level. 



Is it likely (or not) that we might achieve a superior (more efficient, more intelligent, more dynamic) result by simulating the advanced behavior of neural interaction (the neuro-activity patterns the brain experiences to form emotions, develop conclusions, processes sensory data, establish and/or revisit memories, etc) than we can reach by using shortcuts (advanced machine learning algorithms, etc)?
Very interesting question. Although I have not a single little bit of expertise in this area, I do have some references you may want to read. First is a paper by Merz and Fromherz (2005) where they grew snail neurons on a silicon chip. Pfister et al (2007) also tried to grow neurons to allow interfacing between neurons and machine (for neural prosthesis e.g.). There is thus definitely a field of research that believe neural interfacing may be better than mechanical and algorithmic networks.  

There has been 10 years worth of research since these papers so I bet there are some advancements that may give you a more definitive answer than mine.