Signal processing approach to stydy the brain

AI Thread Summary
Different approaches in neuroscience, including neurobiophysics, computational neuroscience, and engineering, offer unique insights into brain function. Engineering, particularly in signal processing, has practical applications such as noise reduction and diagnostics, while also contributing to fundamental research on brain mechanisms. The methods used in these fields are often complementary, making it difficult to determine which is the most promising for understanding the brain. Institutions like Stanford are conducting significant research in brain imaging that could enhance understanding in this area. Overall, the integration of applied and basic research perspectives is crucial for advancing knowledge of brain function.
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I have sought information about different approaches(fields) to neuroscience and how they differ in terms of understanding how the brain works. I'm interested to learn more about neurobiophysics, computational neuroscience and engineering perspectives. In engineering especially about brain signal analysis and brain signal processing.

I understand that engineering perspective contains broad range of application related stuff and for example signal processing can have practical purposes like noise reduction from signals and diagnostic applications. But I'm interested to know if there is any science
in this approach in a sense that you can study and explain how the the brain works and make new discoveries with these signals and systems methods? Because I'm interested in both the applied and the basic research side, this is important question for me.

Lastly which one of these three fields do you think is best(most promising) path for understand the brain?

If someone could help me with this i would really appreciate it.

Thanks for your help.
 
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Engineering perspective would be the best for you
 
There are lots of ways to look at the brain and they all have something to offer. It depends on the level of abstraction you're interested in. Most of the different methods are complementary so one isn't necessarily more promising than another.
 
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