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I am trying to find any recent study on pattern recognition in nerve signals. It does not really matter the part of the body where the research is focused on. It can be anything from studying activity of one group of nerves to signals coming from an entire limb.

I was only able to find old (1996) research, which could be much more complete with the modern advancements in computing and manufacturing of micro-instruments. Here is an example of an old paper.

P.S. I am generally looking for information on how nerve signals differ within multiple subjects of same specie. For example, how would nerve response to a stimuli differ for two mice from same offspring and how will they differ from it's parents.

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what kind of a pattern do you want to find; what do you mean by signal ? –  WYSIWYG Apr 30 at 9:45
    
@WYSIWYG some kind of logical sequence of neural activity caused by the stimulus. For example I would want to know if applying hot/cold object to skin of both right and left limbs would cause the sensory neuron to send the same or similar signal (electrical impulse) to the spinal cord. Would it be possible to identify what happened with the limb, having access to part or all of the electrical activity in central and peripheral neural systems, even theoretically. Is it possible to learn what each impulse is about and if it is, should you learn anew for every new body or signals are universal? –  Xeos May 1 at 20:00
    
I meant what will you capture: Voltage changes ? –  WYSIWYG May 2 at 3:33
    
@WYSIWYG Yes, it would be synaptic potential. –  Xeos May 2 at 12:58

1 Answer 1

up vote 1 down vote accepted

You might want to read about event related potentials (recorded by EEG) or event related fields (recorder by MEG). The idea is simple:

1) Pick some stimulus, e.g. a person touching the hand of a subject. Pick another stimulus, e.g. the subject seeing a person touching the hand of another subject. Record EEG/MEG.

2) Repeat each condition for at least few hundred times. Throw away bad data, and average the repetitions. You do the averaging because the signal-to-noise ratio is low in all non-invasive electrical measurements, and the signals are always riddled with movement, cardiac, and eye-blink artefacts.

3) Compare the conditions in each sensor. Rest is statistics, though not very easy.

Actually, the experiment above would be about mirror neurons, a relatively hot topic in neuroscience. (See e.g. recent issue from Nature)

The more general situation is that using pattern recognition/machine learning algorithms to study brain signals is already a standard thing to do. There are for example graduate/undergraduate programs in computational neuroscience.

If you would study some other signal (e.g. measure from a hand, leg), they are going to dull. Very dull. Mostly because then you have 1 time-series, instead of several hundreds of time-series to analyse.

For example I would want to know if applying hot/cold object to skin of both right and left limbs would cause the sensory neuron to send the same or similar signal (electrical impulse) to the spinal cord.

This you could too study with the event related fields/potentials.

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