neurodudes

Dear Members,
I am a prospective graduate student interested in taking up Neural Engineering under EE or Biomedical Engg for research. But I have a lot of concerns and need help from a person who knows about the field well.
1. I have studied VLSI, DSP, Image Processing, Wireless Communication, Control Systems and Embedded Systems as graduate and undergraduate courses and have some research interest in Neural Networks and Machine Learning(That’s how I got interested in Neural Engg and Prosthetics). Which of these subjects will be of help in Neural Engg/Prosthetics research. Which will be of most relevance. Please list them in the order of relevance(high->low).
2. What are the applications of the research ?
3. What is the research and JOB scope for this field? Are there any companies who recruit people with this specialisation? How is the job scene in academia? How many univs are doing research in this field in US? Please let me know about the career progression in academia, like how much time does it take to get full time academic position after PhD?
4. Especially, what are the applications of this research in Robotics?
5. What are the current problems and research themes in universities?
6. What imaging technologies are used in this research?

Though my queries may seem a bit ameteuristic, it is very important for me to get clarity on these doubts.
Hope my queries will be answered.
Thanking all of you in advance,
sudhi

Neuron : Uncertainty, Neuromodulation, and Attention

Haven’t read this article from Peter Dayan’s lab yet but some interesting Bayesian modeling implicating acetylcholine as a signal of expected uncertainty and norepinephrine as a signal of unexpected uncertainty.

Abstract:

Uncertainty in various forms plagues our interactions with the environment. In a Bayesian statistical framework, optimal inference and prediction, based on unreliable observations in changing contexts, require the representation and manipulation of different forms of uncertainty. We propose that the neuromodulators acetylcholine and norepinephrine play a major role in the brain’s implementation of these uncertainty computations. Acetylcholine signals expected uncertainty, coming from known unreliability of predictive cues within a context. Norepinephrine signals unexpected uncertainty, as when unsignaled context switches produce strongly unexpected observations. These uncertainty signals interact to enable optimal inference and learning in noisy and changeable environments. This formulation is consistent with a wealth of physiological, pharmacological, and behavioral data implicating acetylcholine and norepinephrine in specific aspects of a range of cognitive processes. Moreover, the model suggests a class of attentional cueing tasks that involve both neuromodulators and shows how their interactions may be part-antagonistic, part-synergistic.

PLoS Biology: Attenuation of Self-Generated Tactile Sensations Is Predictive, not Postdictive [open access]

I haven’t gotten a chance to fully digest this article (What is the attenuation phenomena that happens when the taps are delayed?), but it seems like a deep result from a relatively simple haptics experiment. Just thought I’d share it with the crowd.

Also, Happy Birthday to fellow Neurodude Bayle! Congrats, man.

This week’s Science has a nice introductory article (but with some mathematical detail) on using probabilistic graphical models to model cellular networks. Even for those of you who already know the formalisms (Bayesian networks, HMMs, etc.), you might find the recent biological applications discussed interesting.

Also, there are several other mathematical biology articles in the issue, including a review on evolutionary game theory.