Talk:Dynamical neuroscience

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Neuroscience

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overview[edit]

Here's an example of what I would see as a chapter for Computational neuroscience. Feel free to do what you want with it and incorporate new things, this is just a model to show you what I meant when I said you shouldn't present a biased view where dynamical neuroscience comes as a kind of "savior" for the hordes of neurobiologists :) English is not my first language so do not hesitate to look for errors. Also correct any foolish thing I might have said about dynamical systems, this is what I could understand from what I've read in the last days. To me the important aspect is not that you were wrong about anything relating to dynamical systems but it's more in the tone that was used toward neuroscience as a whole. Jean-Francois Gariepy (talk) 20:28, 7 August 2010 (UTC) (imported by Xurtio (talk) 01:15, 8 August 2010 (UTC))[reply]

Ok, thank you for pitching in. Your changes didn't seem to remove any of the valuable content. I did take a graduate neurobiology course at my university and Izhikevich's criticisms were definitely valid for our local neuroscience program. The professor did of course, mention that what were learning was wrong every once in a while, but didn't bother us with the details and it might not come as a surprise to you that all my biology friends are afraid of mathematics. But I surely have a skewed view in Alaska, where I had to design my own theoretical neuroscience degree :/

Anyway, regardless, there's no reason that viewpoint should be conveyed in an encyclopedia, I agree. Xurtio (talk) 01:40, 8 August 2010 (UTC)[reply]

intro[edit]

I would say the best part to recuperate here could be the attractors part, considering we remove or clarify the link between attractor types and the behaviors modeled -Jean (snipped and imported by Xurtio (talk) 01:15, 8 August 2010 (UTC))[reply]

for example expand the list into sub-sections and give a little more detail on each attractor? Xurtio (talk) 07:53, 9 August 2010 (UTC)[reply]

excitability[edit]

excitability section sounds too much like discussion. I'll have to do something about that. I might need to initiate an Excitability article. Xurtio (talk) 08:20, 8 August 2010 (UTC)[reply]

history and placement[edit]

History might be the most important aspect of this field that I'm missing, and also might give me an opportunity to understand the field better and find out where it belongs. Computational neuroscience definitely utilizes it, but it also seems a branch of mathematical biology and neurophysics. I'm not really sure where it belongs. In my case, I'm coming at it from a neurophysics perspective based on the neurophysics wiki. —Preceding unsigned comment added by Xurtio (talk • contribs) 06:14, 9 August 2010 (UTC)[reply]

Neurodynamics[edit]

ok, I think I found it. The official name of my field:

Current Opinion in Neurobiology, August 2001, Volume 11, Issue 4. Neurodynamics: nonlinear dynamics and neurobiology: Henry D. I. Abarbanel, a and Michael I. Rabinovich

Abstract "The use of methods from contemporary nonlinear dynamics in studying neurobiology has been rather limited.Yet, nonlinear dynamics has become a practical tool for analyzing data and verifying models. This has led to productive coupling of nonlinear dynamics with experiments in neurobiology in which the neural circuits are forced with constant stimuli, with slowly varying stimuli, with periodic stimuli, and with more complex information-bearing stimuli. Analysis of these more complex stimuli of neural circuits goes to the heart of how one is to understand the encoding and transmission of information by nervous systems."

According to neural oscillations, "neurodynamics" was first coined in the 1940's by a cognitive scientist.

Here's some history: http://resources.metapress.com/pdf-preview.axd?code=g384811610556546&size=largest

from http://www.appliedneuro.com/#neurodynamics

"The word “Neurodynamics” is a term used here in my consulting firm name (Applied Neurodynamics) to represent a conceptual and eclectic methodological approach to understanding neural network activity, and to use this perspective to bridge from neuroscience to cognitive science, conscious experience and behavior. Conventional neural network architectures are often simplistic feed forward or recurrent models where the timing of events is not important to the processing being done. Dynamics studies causal systems where timing is a key consideration [1, 19]. Dynamics underlies all “computation” which is the preeminent (and overworked) paradigm in all areas of science today [31]." Xurtio (talk) 06:31, 9 August 2010 (UTC)[reply]