neurodudes

On Campus, the 1960s Begin to Fade as Liberal Professors Retire - NYTimes.com

Although the shift away from liberalism amongst faculty is interesting, this graphic caught my attention:

Should we take this to mean that there should be more faculty jobs as the avg age increases? (Or is this negated by the fact that people are living longer and working longer?)

Salon has an interesting piece condemning a recent PBS show purportedly on Alzheimer’s treatment but really more of a sketchy informercial. The program concerns a neurologist with tenuous ties to UC Irvine who advocates SPECT (single photon emission computed tomograpy, a technique which, similar to PET, uses a radiotracer) and some unfounded preventative treatments for Alzheimer’s. The neurologist Bill Amen has appeared on many big-name media outlets including CNN, the Today Show, and Fox News (and the real sign of media success — Oprah) although his approach to Alzheimer’s detection and treatment is lacking in scientific credibility:

“SPECT scans are not sufficiently sensitive or specific to be useful in the diagnosis of A.D.,” neurologist Michael Greicius , who runs the Stanford University memory clinic, and has a special interest in the use of functional brain imaging in the diagnosis of A.D., tells me. “The PBS airing of Amen’s program provides a stamp of scientific validity to work which has no scientific validity.”

Continued pontification on neuroethics issues after the jump. (more…)

Haim Sompolinsky has written an excellent book chapter on the scientific view of free will and choice, pulling in good ideas from physics and neuroscience along with contemporary philosophical commentary.

I think this chapter might be helpful for neuroscientists outside of the lab. Often a dinner table discussion has moved to the idea of “quantum consciousness” or “quantum free will”. Often, someone will mention Roger Penrose, who has become something of a poster boy for this idea that quantum indeterminacy (eg. Heisenberg’s uncertainty principle) is one possible way that free will is really free. And then, people look around and say, “Well, you’re a neuroscientist. Do we have free will?” (And that’s when I take another big drink or bite while I figure out something semi-coherent to say.)

Sompolinsky does a nice job of evaluating such claims (in the end, he says we cannot rule out the possibility that the brain is an indeterministic system but it seems unlikely) and provides nice scientific insight. In his view, it is far more likely that the brain’s apparent randomness (eg. individual cell spike rasters vary across repeated presentations of the same stimulus) is more simply explained by thermal noise (think of varying channel gating properties) and chaotic brain dynamics. (Recall, a chaotic system is still deterministic; it simply exhibits aperiodic behavior due to exquisite sensitivity to initial conditions. It is difficult to predict the long-term behavior of chaotic systems. The more we know the initial conditions in detail, the better our prediction.) On the other hand, he argues that the relevant length and time scales for neurons (micrometers and milliseconds) are far larger by many orders of magnitude than those of quantum noise. Chaos might amplify such quantum events, but this is far from being the simplest, most parsimonious explanation. Given the current level of neuroscience understanding, this is almost idle speculation. Regardless of the (in)determinacy of the world, Sompolinsky effectively argues against any non-physical, purely mental (ie. dualistic) agent of causation.

Thus, in sum, the world and our brains might not be determined but, even given that, there’s no reason to believe we have any causative ability to change things in the sense of traditional free will. These observations seem right on the mark to me. I hope they bring some insight for others. Or at least a way to fend off the dinner-table-free-will-conversation barrage of questions.

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.

The supercomputer center in San Diego has created a cool site called SciVee for scientists to upload brief videos introducing/explaining their publications.

There is quite some variety in the style of these short lectures (even though there are only a few currently posted). Some give a list of the key findings of the publications and others doing a much better job of making their work more accessible by providing an introduction/context and avoiding technical jargon.

“It sounds like a science-fiction version of stupid pet tricks: by toggling a light switch, neuroscientists can set fruit flies a-leaping and mice a-twirling and stop worms in their squiggling tracks. But such feats, unveiled in the past two years, are proof that a new generation of genetic and optical technology can give researchers unprecedented power to turn on and off targeted sets of cells in the brain, and to do so by remote control…”

Reviews the use of photosensitive proteins in neuroscience and even gives a shout-out to Ed Boyden, of Stanford and MIT fame…

– Davie (who had the same advisor as Ed for about a day and is therefore 0.01% more famous by association)

Recently, I was pointed to this article in the WSJ (”A Pentagon Agency Is Looking at Brains — And Raising Eyebrows“) by Sharon Begley. It touches on some noninvasive recording techniques for assessing affective state and cognitive enhancers like ampakine CX717 (previously mentioned on Neurodudes here and here).

It was the very last paragraph that caught my eye:

Ever since the atomic bomb, physicists have known that their work has potential military uses, and have spoken up about it. But on the morality of sending orders directly to the brain (of a soldier, employee, child, prisoner …), or of devices that read thoughts and intentions from afar, neuroscientists have been strangely silent. The time to speak up is before the genie is out of the bottle.

Whoa! To me, the physicists who spoke out early on against nuclear proliferation seemed (and still seem) both very courageous and prescient in their ideas. Are we neuroscientists dropping the ball? I would love to start a discussion on this subject and to hear your responses (both from neuro people and others) in the comments below.

I’ll start: I personally don’t think the arena of neural enhancement/intrusion (mind reading, mind control, cognitive enhancement, etc.) is comparable to the sheer destructive power of nuclear weapons. I do see in the near future the unfortunate potential for abuse of neurotechnology and violation of personal freedoms, but the threat does not seem as horrifying or deadly. Still, if neurotechnology allows governments greater control over their citizens, it seems reasonable that scientists who enable such technologies should intervene. Perhaps it is time for a neural bill of rights, which, similar to the freedoms granted by the US Bill of Rights, will clearly state what aspects of a person’s mental state or capacity cannot be infringed upon without permission from that person. Thoughts?

Science has a special online feature this week on behavioral science. One of the articles is a review by Paul Bloom and Deena Skolnick Weisberg (a fellow SymSys alum!) presents some interesting evidence about how dualistic ideas about mind/brain are present from an early age. They state:

Another consequence of people’s common-sense psychology is dualism, the belief that the mind is fundamentally different from the brain (5). This belief comes naturally to children. Preschool children will claim that the brain is responsible for some aspects of mental life, typically those involving deliberative mental work, such as solving math problems. But preschoolers will also claim that the brain is not involved in a host of other activities, such as pretending to be a kangaroo, loving one’s brother, or brushing one’s teeth (5, 17). Similarly, when told about a brain transplant from a boy to a pig, they believed that you would get a very smart pig, but one with pig beliefs and pig desires (18). For young children, then, much of mental life is not linked to the brain.

And,

For one thing, debates about the moral status of embryos, fetuses, stem cells, and nonhuman animals are sometimes framed in terms of whether or not these entities possess immaterial souls (20, 21). What’s more, certain proposals about the role of evidence from functional magnetic resonance imaging in criminal trials assume a strong form of dualism (22). It has been argued, for instance, that if one could show that a person’s brain is involved in an act, then the person himself or herself is not responsible, an excuse dubbed “my brain made me do it” (23).

The authors conclude that adult resistance to science is strongest in fields where scientific claims are contested by the society (that is, contested by non-science alternatives rather than by scientific uncertainty). They claim that this accounts for the difference in the United States (versus other countries with less vociferous advocacy of non-science) in the resistance to the central tenets of evolutionary biology and neuroscience.

I think this says something important about science education, namely that it should start earlier in life. And there’s no reason that neuroscience should be left as a “college-level” subject. I think modern neuroscience has progressed to the point where we can confidently teach some basics at a high-school or earlier stage. Judging from my own experiences, I think the desire to learn about neuroscience is certainly there in younger children.

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.

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.