<p>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 <a href="https://www.researchgate.net/publication/229444024_Silicon_Chip_Interfaced_with_a_Geometrically_Defined_Net_of_Snail_Neurons" rel="nofollow">Merz and Fromherz (2005)</a> where they grew snail neurons on a silicon chip. <a href="http://europepmc.org/articles/PMC3979335" rel="nofollow">Pfister et al (2007)</a> 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.  </p>

<p>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. </p>

CC BY-SA 3.0
2016-05-24T12:13:40.293Z
2016-05-24T12:13:40.293Z
Robin Kramer-ten Have
<p>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 <em>behavior</em> on the most fundamental level might be more effective than using algorithms to reach the same result.</p>

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

<hr>

<p>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)? </p>

<artificial-intelligence>
Could physically simulating neural structure on a fundamental level yield superior results to machine learning algorithms?
J.Todd
# Could physically simulating neural structure on a fundamental level yield superior results to machine learning algorithms?

(Asked by: username_0 on 2016-05-24)

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.

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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)?

**Tags:** artificial-intelligence


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## 1 answers

### Answer by username_1 on 2016-05-24. Score: 3

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)](https://www.researchgate.net/publication/229444024_Silicon_Chip_Interfaced_with_a_Geometrically_Defined_Net_of_Snail_Neurons) where they grew snail neurons on a silicon chip. [Pfister et al (2007)](http://europepmc.org/articles/PMC3979335) 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.


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