neurodudes

Jonathon Keats (no, not that one) has written a scorching review of neuro grad student Jonah Lehrer’s new book, Proust was a Neuroscientist.

I saw this somewhat more favorable review a few weeks in the NYT and was intrigued by the book. As an undergrad, I majored in cognitive science and English and, naturally, was fascinated by the cultural differences of academics in these disparate fields.

As in the Salon article, I also think attempts to unify the “two cultures” (ie. arts and sciences) are misguided. A work like Lehrer’s book (which I have not read) will need to work hard to “prove” its thesis and likely sound very forced. What can we really say about arts vs. sciences? For that matter, is it important to make value judgments on this topic? I’d say, no. We seem to have a natural urge to categorize our activities and then try to order them. Science is more worthwhile. Art is a more creative endeavor. Are these blanket generalizations productive?

But there is overlap between the two cultures and those regions seem more and more important to me. And I think neuroscience in particular has a lot to say here, too. If we know what makes good art good (in a scientific way), will we stop appreciating it or enjoying it? (This is similar to the idea that if someone told you free will was simply an illusion would the illusion be any less powerful than it is right now?) Often, the surprise of creative thought underlies the best science and the best art. Okay, there’s my attempt at a unification!

On a separate note, there certainly seems to be a hunger amongst the reading public for neuroscience books, despite our incomplete picture of how the brain works. For those frustrated with slow progress in research, maybe we should just go write a book.

Finally, they’ve figured out something that neural systems have capitalized on for a while: Using all 3 dimensions. Check it out:

NYT article on Stuart Parkin and racetrack memory
IBM Almaden page on racetrack memory

And a nice semi-technical discussion from EE Times on current 2D RAM and how racetrack memory takes advantage of 3 dimensions. A key concept here (and an old one, see bubble memory below) is “massless motion”: Applying a current to a tape moves the magnetic domains along in a similar manner to mechanically moving the tape.

On the macro scale, a similar type of memory called bubble memory was available up to the early 1980s when it was replaced with hard disks. Seems like high speed (pushing the correct magnetic portion of the nanowire to the read head) and doing so without excessive current are what really make this a viable technology now.

This study provides fMRI evidence that, after forgetting some memories, the brain has to work less hard.

(more…)

This week’s Nature has quite a few additional halorhodopsin articles for photochannel fans.

Halorhodopsin article from Deisseroth’s lab:
Multimodal fast optical interrogation of neural circuitry [News & Views]

Also, there is an intriguing article on both the general excitement in the neuroscience community with this new technology and a possible intellectual property dispute over it.

Entrepreneur-turned-cognitive neuroscientist Jeff Hawkins is distributing a “research release” of their experimental code base implementing his idea of hierarchical temporal memory described in his book, “On Intelligence”. Hawkins drew inspiration for the model from his own reading about the structure and function of the human neocortex and believes that it represents the foundation for developing intelligent machines.

Jeff explains this surprising move to open source the code for the Numenta Platform for Intelligent Computing (NuPIC) on the Numenta web site:

Why are we making NuPIC available now?

We have been contacted by dozens of researchers and scientists who are excited about HTM and by our work at Numenta. These people are anxious to work on HTM, are willing to be pioneers, and are willing to accept the uncertainty associated with a new technology. We are making our tools available so that these sophisticated developers can start building a community around HTM technology. NuPIC has been under development for 18 months, is pretty solid, and is well documented – including several examples to make it easy to get started – so we’re ready to open up to more developers, even while knowing that we do not yet have benchmarking data, and we cannot make guarantees about applicability to specific problems.

Here’s why Hawkins thinks that HTMs are new.

We have been covering Hawkins’ work for a while now. See these previous posts for more background info.

Neurodudes is actively soliciting code reviews of the newly released software. Is NuPIC the next big thing, or are you left feeling cold? Post your thoughts yourself using the instructions on the right-hand column, or let us know at contactus -AT- neurodudes.com!

Postdoctoral/research scientist positions are available in the inter-disciplinary group of Dmitri Chklovskii at the new Janelia Farm Research Campus of the Howard Hughes Medical Institute located in the suburbs of Washington, D.C. Candidates are expected to have a PhD in neuroscience, physics, computer science or electrical engineering. Most of the work is theoretical or computational and is done in collaboration with several experimental laboratories. Successful applicants will work on projects centered on neuronal circuits such as high-throughput reconstruction of wiring diagrams as well as combining structural and physiological data to infer circuit function. Salary will be commensurate with qualifications. For more information about research directions in the group please see: http://www.hhmi.org/research/groupleaders/chklovskii.html
Interested applicants should send their CV and a statement of research interests to mitya (at) janelia.hhmi.org, and arrange for three recommendation letters to be emailed to me.

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.

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

It seems Markram is again back to getting some interesting results. Recently a new discovery from the Brain Mind Institute of the EPFL shows that the brain adapts to new experience by unleashing a burst of new neuronal connections, and only the fittest survive. The research further shows that this process of creation, testing, and reconfiguring of brain circuits takes place on a scale of just hours, suggesting that the brain is evolving considerably even during the course of a single day.

The paper can be found Here.

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.