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

I put together a list of some computational neuroscience courses which have substantial material (handouts or reading lists) publically available on the web.

If you know of other such courses which are not listed there, feel free to add them.

This list drew heavily from two other lists (links to them are at the bottom of that page, in the section “other lists”).

The October 6th issue of Science is a special issue devoted to computational neuroscience. From the introduction to the special issue:

Computational neuroscience is now a mature field of research. In areas ranging from molecules to the highest brain functions, scientists use mathematical models and computer simulations to study and predict the behavior of the nervous system. Simulations are essential because the present experimental systems are too complex to allow collection of all the data. Modeling has become so powerful these days that there is no longer a one-way flow of scientific information. There is considerable intellectual exchange between modelers and experimentalists. The results produced in the simulation lab often lead to testable predictions and thus challenge other researchers to design new experiments or reanalyze their data as they try to confirm or falsify the hypotheses put forward. For this issue of Science, we invited leading computational neuroscientists, each of whom works at a different organizational level, to review the latest attempts of mathematical and computational modeling and to give us an outlook on what the future might hold in store.

Of particular interest is a review article by Randall O’Reilly on biologically based computational models. He focuses on models of the pre-frontal cortex.

UCSD’s Computational Neurobiology program just released a new website to herald a new year of research and graduate admissions. It seems to put a lot of emphasis on the students.

If anyone has any additional questions that they think would be good to address at the workshop, leave it as a comment below.

NIPS 2006 Workshop Announcement and Call for Abstracts
Decoding the Neural Code

There is great interest in sensory coding. Studies of sensory coding typically involve recording from sensory neurons during stimulus presentation, and the investigators determine which aspects of the neuronal response are most informative about the stimulus. These studies are left with a decoding problem: are the discovered codes, sometimes quite exotic, ultimately used by the nervous system to guide behavior? In our one-day workshop, researchers with many different backgrounds will evaluate what we know about neuronal decoders and suggest new strategies, both experimental and computational, for addressing the decoding problem.

Each hour, five to six researchers will address a particular question for five minutes, followed by a half-hour discussion. We will also set aside time for a poster session.

We tentatively plan to include the following questions, and are soliciting additional questions from our speakers:
1. Which variables that encode stimuli are actually used to guide behavior?
2. What mechanisms do nervous systems use to decode encoded information?
3. Are motor systems better than sensory systems for experimentally addressing decoding?
4. What computational and experimental techniques are needed to address decoding? For instance, should information theory be used to address decoding as well as encoding?

For information on abstract submission, go to the workshop web site at http://science.ethomson.net/NIPS_workshop.html.

Transcranial magnetic stimulation (TMS) is a popular technology for stimulating human cortical neurons, due to its safety, noninvasiveness, and efficacy. A TMS device is just a little coil of wire, through which 10,000 Amps of current is cranked during a period of only a few hundred microseconds; the resultant rapidly-changing magnetic field induces eddy currents in the brain. Depending on the protocol used, TMS can drive/inhibit a region of cortex corresponding to roughly a cubic centimeter or two, and is being explored for the treatment of depression, the reduction of auditory hallucinations during schizophrenia, and the alleviation of tinnitus and migraines. Thousands of papers on medicine and psychology have been written using this tool.

Yet the device itself is expensive and rare — they can run from $20,000 to $50,000 or even more, despite the fact that they are, in essence, a coil, a switch, a bank of capacitors, and a power supply. Much of the art lies in making the devices safe and fail-proof. Is it possible to hack/engineer a system that is safe, fault-tolerant, efficacious, and inexpensive? And furthermore, can we facilitate a community that will devise such devices, and share information about protocols and approaches to brain hacking?

This past August at Foo Camp, a hackers’ conference in Northern California, a group of people got together and set out to do just that. We are designing a safe, noninvasive, modular, and “open source” brain stimulator that will open up the field of circuit modulation to a wider audience. Members of the group include therapists and mental health professionals, engineers, programmers, and others interested in either the development of such devices, or the sharing of information on this front. Key to the design is safety — we want to make sure that the devices we create are as safe as devices on the market. Also, all the information is released under the Creative Commons “Attribution and Sharealike” license. This is a new model for “open source” medical device development — which may move it beyond the domain of simply creating “cool toys,” and to creating real devices.

You can find out more information, or contribute to the project, or learn from the project, at
http://transcenmentalism.org/OpenStim/

-Ed

Today MIT’s Technology Review magazine released its annual list of innovators under the age of 35 who were nominated for recognition. Interestingly, almost a full quarter are doing work relating to or impacting the field of neuroengineering — including ways to tag synapses with quantum dots, activate neurons remotely, improve machine vision, classify whole-brain states for prosthetic purposes, and make nanowire arrays.

http://www.technologyreview.com/TR35/

This past May, the Almaden Research center, part of IBM research, invited some provocative speakers on the topic of “Cognitive Computing” to come and speak. Since IBM recently invested a lot of money into understanding the brain with the Blue Brain project, it seems like this meeting was a way to figure out the next step along this path.

Powerpoint presentations and videos of the event are available online.

From the synopsis:

The 2006 Almaden Institute will focus on the theme of “Cognitive Computing” and will examine scientific and technological issues around the quest to understand how the human brain works. We will examine approaches to understanding cognition that unify neurological, biological, psychological, mathematical, computational, and information-theoretic insights. We focus on the search for global, top-down theories of cognition that are consistent with known bottom-up, neurobiological facts and serve to explain a broad range of observed cognitive phenomena. The ultimate goal is to understand how and when can we mechanize cognition.

Confirmed speakers include Toby Berger (Cornell), Gerald Edelman (The Neurosciences Institute), Joaquin Fuster (UCLA), Jeff Hawkins (Palm/Numenta), Robert Hecht-Nielsen (UCSD), Christof Koch (CalTech), Henry Markram (EPFL/BlueBrain), V. S. Ramachandran (UCSD), John Searle (UC Berkeley) and Leslie Valiant (Harvard). Confirmed panelists include: James Albus (NIST), Theodore Berger (USC), Kwabena Boahen (Stanford), Ralph Linsker (IBM), and Jerry Swartz (The Swartz Foundation).

A very nice neuroecon expt. in the newest Nature:

Daw et al. find that humans choose between multiple slot machines (with different payoff probabilities) based on expected value (versus just going with the highest probability one most of the time and then randomly choosing another one every so often). Then, with fMRI, they find brain areas correlated with different value predictions.

News & Views (Daeyol Lee)

Cortical substrates for exploratory decisions in humans (Daw, Dayan)

Abstract:

Decision making in an uncertain environment poses a conflict between the opposing demands of gathering and exploiting information. In a classic illustration of this ‘exploration-exploitation’ dilemma, a gambler choosing between multiple slot machines balances the desire to select what seems, on the basis of accumulated experience, the richest option, against the desire to choose a less familiar option that might turn out more advantageous (and thereby provide information for improving future decisions). Far from representing idle curiosity, such exploration is often critical for organisms to discover how best to harvest resources such as food and water. In appetitive choice, substantial experimental evidence, underpinned by computational reinforcement learning (RL) theory, indicates that a dopaminergic, striatal and medial prefrontal network mediates learning to exploit. In contrast, although exploration has been well studied from both theoretical and ethological perspectives, its neural substrates are much less clear. Here we show, in a gambling task, that human subjects’ choices can be characterized by a computationally well-regarded strategy for addressing the explore/exploit dilemma. Furthermore, using this characterization to classify decisions as exploratory or exploitative, we employ functional magnetic resonance imaging to show that the frontopolar cortex and intraparietal sulcus are preferentially active during exploratory decisions. In contrast, regions of striatum and ventromedial prefrontal cortex exhibit activity characteristic of an involvement in value-based exploitative decision making. The results suggest a model of action selection under uncertainty that involves switching between exploratory and exploitative behavioural modes, and provide a computationally precise characterization of the contribution of key decision-related brain systems to each of these functions.

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.