neurodudes

A controversial paper proposed that sodium channels are not statistically independent when they open and close. This may have implications for the speed of neural computation.

(more…)

Computational neuroscience is a field where many successful researchers have a strong physics background. So far, the physics approach has provided a strong foundation from which to understand the brain. Recently, however, the influence of a computer science perspective has become more prominent. How can we understand the different perspectives that these disciplines bring to the field? Can we observe the influence of physics methodologies on the modern study of the brain? And if so, what is the consequence of our understanding of the brain through the lens of physics versus the lens of computer science?

One consequence may be the way that computational neuroscience models time in the brain. The study of physics generally conceptualizes time as continuous. Time is something to be plotted on the x-axis of a graph where some other quantity of interest is plotted on the y-axis.

In computer science, on the other hand, real time is rarely conceptualized explicitly. Computer scientists do not plot quantities against time unless they are profiling software for performance purposes, and even then, time is more generally thought of as number of operations. Thinking about operations generally leads computer scientists to think about time as discrete events.

I posit that the distinction between continuous and discrete time creates a foundational difference between the physics approach and the computer science approach to understanding how the brain works. Due to the discrete time conceptualization, computer scientists are more comfortable explaining the function of brain systems in terms of chains of events with definite beginnings and definite ends. Physicists, on the other hand, are more comfortable explaining the brain in terms of dynamics, which do not require definite beginnings or definite ends. Computer scientists care more what the consequence of an event is in the brain, whereas physicists are more concerned with an concise account of the dynamics of what is occurring.

This divide is visible in the distinct modeling approaches of neurons that derive from these two disciplines. The canonical neuronal model contributed by the physics philosophy is the multi-compartmental conductance based (Hodgkin-Huxley like) model. This model is concerned with matching waveforms of current and voltage traces with those that are measured in real neurons. This model helps us to understand how changes of the properties of excitable membranes over time result in changes of neuronal behavior over time. The computational complexity of these models is thought to prevent more than a few hundred neurons modeled in this way from being analyzed concurrently.

Alternatively, the canonical neuronal model contributed by the computer science philosophy is the integrate-and-fire neuron. This model does away with modeling conductances explicitly as functions of time and simply performs a weighted sum of its inputs at each time step. Here a time step is a discrete event whose duration is a parameter of the model. The simplicity of this model allows large networks to be constructed, which are useful for modeling systems of many thousands of neurons.

The physics approach provides insight into the activity of single cells and small networks, whereas the computer science approach provides insight into the activity of large networks. Neither approach is optimal and neither approach provides all the tools that are necessary to truly understand the brain. As these two perspectives are better understood, the field of computational neuroscience can benefit from finding creative ways to merge these two conceptions of time into models that capture both small scale and large scale neuronal activity.

In conclusion, I have demonstrated that what begins as a division between discrete and continuous time amounts to a divide between a bottom-up and a top-down approach. Furthermore, I have shown that understanding the relative contributions of different sciences to computational neuroscience is important for understanding the paradigms that pervade the field.

HOW DOES THE BRAIN CONTROL BEHAVIOR?

HOW CAN TECHNOLOGY EMULATE BIOLOGICAL INTELLIGENCE?

The conference is aimed at researchers and students of computational neuroscience, cognitive science, neural networks, neuromorphic engineering, and artificial intelligence. It includes invited lectures and contributed lectures and posters by experts on the biology and technology of how the brain and other intelligent systems adapt to a changing world. The conference is particularly interested in exploring how the brain and biologically-inspired algorithms and systems in engineering and technology can learn. Single-track oral and poster sessions enable all presented work to be highly visible. Three-hour poster sessions with no conflicting events will be held on two of the conference days. Posters will be up all day, and can also be viewed during breaks in the talk schedule.

ELEVENTH INTERNATIONAL CONFERENCE
ON COGNITIVE AND NEURAL SYSTEMS

May 16 – 19, 2007

Boston University
677 Beacon Street

Boston, Massachusetts 02215 USA

http://www.cns.bu.edu/meetings/

Sponsored by the Boston University

Center for Adaptive Systems and
Department of Cognitive and Neural Systems (http://www.cns.bu.edu/)
with financial support from the National Science Foundation (http://cns.bu.edu/CELEST/)

Is science just about facts, or are theories and conceptualizations important too? Should we worry about having good theories, or do the facts pretty much give us everything we need to know. This article, entitled “Facts, concepts, and theories: The shape of psychology’s epistemic triangle“, discusses this issue for the field of Psychology, though its contents are also applicable to Neuroscience and AI.

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