Neurophysics vs computational neuroscience

[Neuroni]

What's the difference between above-mentioned fields?

Moreover how engineering perspective focused on brain signal analysis and signal processing differ
from those?
Is this approach only for practical purposes like noise reduction from signals and detecting signals which we already understand for example diagnostic applications. And when you have made your filter ready and find something new which isn't yet discovered the one who get to investigate the results is someone else. Or are there science in this approach also in a sense that you can study and explain how the the brain works and make new discoveries with these signals and systems methods.

I'm very interested in the engineering perspective because it gives you a possibility for biomedical engineering which is one of my interest also. I'm just worried about the things i wrote above.

Which one of these approaches do you think is best(promising) for understanding the brain and why?

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

Thanks for your help.

 

[Choppy]

I suspect the difference is largely semantics - differences in wording that for all practical purposes mean the same thing.

I'm not sure I understand the second part of your question. There is a lot of signal processing involved in biomedical engineering.

 

[atyy]

Where did you see the term "neurophysics"?

One difference in tendency between computational neuroscience and bioengineering is the extent to which one uses Maxwell equations. In computational neuroscience, one is often interested in how networks of neurons behave, and the neurons are treated very simply as things that spike once they get input above a certain threshold, analogous to a logic gate. In contrast, in bioengineering one may want to stimulate the neurons to achieve a certain effect, in which case one may use Maxwell's equations to consider how various electrode configurations stimulate the neurons.

As an example, computational neuroscience might use tools like http://www.briansimulator.org/ or http://www.nest-initiative.org/index.php/Software:About_NEST .

Bioengineering would be things like the cochlear implant, the auditory brainstem implant, the trial http://www.bioen.utah.edu/cni/projects/blindness.htm, deep brain stimulation for Parkinson's, and brain-machine interfaces.

The distinction is of course not hard and fast.

Also there are many other people involved in making these things work - drug addicts, for example, made a huge contribution in the eventual development of deep brain stimulation. http://www.parkinsonsappeal.com/dbs/dbshistory.html

 

[Neuroni]

up up

 

[Matrix0]

Neurophysics and computational neuroscience are both fields that aim to understand the brain and its functions, but they have different approaches and focuses. Neurophysics uses principles and techniques from physics to study the brain, while computational neuroscience uses mathematical and computational methods to model and simulate brain processes. The main difference between these fields lies in their methodologies and tools, but they both contribute to our understanding of the brain in different ways.

In terms of brain signal analysis and processing, the engineering perspective focuses on developing methods and tools to analyze and process brain signals, such as electroencephalography (EEG) or functional magnetic resonance imaging (fMRI). This approach is mainly used for practical purposes, such as noise reduction and signal detection in diagnostic applications. However, it can also contribute to our understanding of the brain by providing insights into brain processes and functions.

In terms of which approach is best for understanding the brain, it is difficult to say as both have their own strengths and limitations. Neurophysics may provide a more fundamental understanding of the brain's physical processes, while computational neuroscience can provide a more detailed and accurate modeling of brain functions. Both approaches are essential for advancing our understanding of the brain and can complement each other in research.

As for the concerns mentioned about the engineering perspective, it is important to remember that any new discoveries or insights gained from brain signal analysis and processing are still valuable contributions to the scientific community. Even if the person who developed the method or tool may not be the one to investigate the results, their contribution is still significant in advancing our understanding of the brain.

Overall, both neurophysics and computational neuroscience, as well as the engineering perspective, have their own unique contributions to understanding the brain. It is important to have a multidisciplinary approach in this field and to use a variety of methods and tools to gain a comprehensive understanding of such a complex organ.