On Reading Minds

Nature1

Who hasn’t had the desire to see through another’s eyes? Some researchers at Berkeley think they’ve taken the first steps towards achieving such a goal.

Jack L. Gallant and his lab-mates have managed the feat of decoding human fMRI measurements in such a way that they can infer the image that generated the recorded neuronal activity1. fMRI as a technique assesses brain excitation indirectly, through blood-flow. The degree of excitation is clearly in some way related to the BOLD (Blood-oxygen-level dependent) signal obtained, but it is a bit crude in the sense that it isn’t very spatially or temporally precise2. The data can pinpoint activity to a few square millimeters, and within a window of about 6 seconds.

The paper detailing their results, appearing in Nature, describes how this remarkable trick was accomplished. First, the researchers consulted fMRI signals from subjects viewing a wide variety of natural images. They correlated this information with the pixels in the pictures themselves, and this allowed them to construct a model which predicted the pattern of blood-flow one might observe with fMRI in response to an arbitrary image. Once this was done, they essentially turned the model on it’s head so that they could ascertain the viewed image from the fMRI data. In fact, at present it’s quite a brute force approach that requires that the scientist have a set of images which are fed into the model to generate synthetic fMRI data to compare with the measured signals. However, it is possible that models of this form will eventually be sophisticated enough to avoid this.

If these techniques could, for example, be extended to other forms of mental reckoning, we might some day be able to see into the thoughts of those who are unable to communicate. Regardless of the practical applications, and however far from sneaking a peek the richly textured visual experience we each have, this type of savvy utilization of data and modeling techniques is exciting because it tickles the basic desire we all have to know another’s being.

Notes/References:

1. Kay KN, Naselaris T, Prenger RJ, Gallant JL. (2008) Identifying natural images from human brain activity. Nature, 452;352-355
2. Other technologies sacrifice the large volume of brain space that fMRI can cover for spatial precision (over 10000x better, single cells) and temporal precision (over 100000 times better, though that much is not necessary).

On Emergent Causation

Max Ernst – “L’invention” or “L’oiseau de L’infini”

I recently read a one-page book review of a text whose subject matter strides through consciousness, free will, and emergence1. The review, by Todd S. Ganson, focuses on how the book, Did My Neurons Make Me Do it?, contends with a classic problem in neuroscience and the philosophy of mind: how is it possible to attribute mental states exclusively to the brain while avoiding a completely determined (lacking in free will) existence2?

Ever since Descarte pointed out the problems with dualism (a separation of the material and the mental), philosophers have been hard at work to find a middle ground between eliminating the mental and resorting to the supernatural. On the one hand, subjective conscious experiences cannot be denied, and it thus seems foolish to claim that they do not exist. However, there is absolutely no hint of a description as to how mentality might be caused by our biological apparatus, and it is thus somewhat attractive to assert some other author to our cognitive being, leading some to invoke the supernatural.

It has been suggested that one way to illustrate the manufacture of subjective experience is describe it as emergent. An example of an emergent property that I find to be particularly useful is the liquidity of water. A single molecule of H2O is not a fluid; rather the quality of being liquid is predicated on the interactions between many molecules. It is a property that emerges from the collective. Another example might be sand dunes: the patterns present in large quantities of grains are a feature of their concert, not guaranteed by the individuals.

Emergence has been very helpful to some because it paints a picture in which consciousness is not a priori predictable from the actions of single neurons, and yet retains a tangible quality. It doesn’t explain how the cerebrum causes consciousness but it does assert a mode in which consciousness might stymie our current scientific attempts to understand it based on the actions of single brain cells.

This book takes the utility of emergence one step further by putting forth the idea that emergence might help us reconcile our personal feeling of responsibility for our actions with our materially deterministic substrates of brain. The idea is that the complex system that is our emergent consciousness “can causally influence what bottom-level events occur by shaping the conditions that trigger these events.2

An apt analogy here again is the sand dune. Its over-all shape determines how the individual grains interact with such forces as the wind. If it forms a flatter dune, it will be less susceptible to the whims of the wind while a tall structure will be more fragile. In this sense, the collective behavior can influence the actions of the individuals which make up the whole.

As mentioned previously, an alternative to searching for ways in which our seemingly ephemeral consciousness can effect the matter in our heads, we can adopt the view that free-will is an illusion; another mechanism of our brains that keeps us happy in the delusion that we’re in charge of our own actions.

In any case, the suggestion concerning emergent causation may not explain anything in specific, but it does help to frame an alternative way to think about the relationship between that vexing triad I mentioned at the top, free will, consciousness and emergence.

Notes:

1. The interested reader might click here for RadioLab’s excellent show on the subject of emergence.
2. Ganson, T.S. (2008) Finding Freedom Through Complexity. Science; 319:104

On Combinatorial Construction of Language

Figure 1

It’s rather fortuitous that the article I’m about to discuss popped up right after my last post, a discussion of how critters’ need for audio-specific brain adaptations depends in part on the complexity of their vocalizations.

The piece of work I’m referring to is a relatively brief description of research on the putty-nosed monkey (figure 1). The finding is that these animals use two types of calls, so-called “pyows” and “hacks” in a combinatorial way: they string together these two words (if you will) to form longer phrases1.

The authors demonstrate that different combinations (pyow hack pyow pyow, vs. hack hack pyow pyow, or something to this effect) can code for distinct predators (leopard vs. hawk). Further, they indicate that novel combinations of the sounds elicit different group behaviors, and that the animals behave differently when the certain calls come from a within-group male rather than a stranger.

This smacks of the beginnings of language to me, in part because one feature of our sentential grammar is iterative construction. Noam Chomsky and others pointed out this method of building up of new sentences with new meaning by tacking extra bits onto the old ones.

References:

1. Arnolda, K & Zuberbühler, K (2008) Meaningful call combinations in a non-human primate. Curr. Biol; 18(5):R202-R203

On Recognizing Conspecifics

Nature Neuroscience1

Communication with members of ones own species is extremely important for social animals. Non-verbal messages can signal socially significant events such as the presence of a predator or the movement of the group. It is therefore no great surprise that some recent research has found monkey brain areas specialized for recognizing conspecifics1. This is a sensible sensory strategy, one which ensures that individuals are able to distinguish between the various growls and caws that they might be privy to, and pluck out the ones most relevant to their continued survival.

In animals where vocalizations transcend the guttural, even more specialization has been unearthed (unbrained?) in the skull. Work on Zebra finches has demonstrated that there are neurons which are active specifically during the production of an individuals song (they have, after all, only one in a lifetime) or while the animal hears his song being played back. Again we can confidently say that this type of anatomical customization is full of utility since it allows the animal to monitor and potentially modulate its learning. In fact it would be difficult to imagine the learning process without this type of helpful structure.

References:
1. Petkov CI, Kayser C, Steudel T, Whittingstall K, Augath M, Logothetis NK. (2008) A voice region in the monkey brain. Nat Neurosci. 11(3):367-74.
2. Prather JF, Peters S, Nowicki S, Mooney R. (2008) Precise auditory-vocal mirroring in neurons for learned vocal communication. Nature. 451(7176):305-10.

On Learning and Time-Scales

Moo Ming Poo, PhD

On Thursday (2/28/08), I attended a lecture, part of the Neurological Institute at Columbia University’s continuing Seminar series. The talk, titled Activity-Induced Modifications of Neural Circuits was given by Moo Ming Poo, perhaps the most active researcher in this fascinating sub-field, from UC Berkeley. The lecture hall, which has seats for perhaps 80 that are never filled at these events, was packed. Standing room was all that was available, with people spilling into the side aisles and a few intrepid souls (the principle investigator in the lab I work in amongst them) seating themselves in the center aisle as well. Eric R. Kandel, James H. Schwartz, and Thomas M. Jessel (the authors of the most widely used undergraduate and graduate textbook on Neuroscience and all professors at Columbia) were in attendance, as well as countless other researchers and graduate students like myself.

The talk was probably so well attended in part because of Dr. Poo’s notoriety, but also because his work is of some universal intrigue, having relevance in all brain areas and a diverse set research programs.

The title of the seminar refers to the property that individual neurons display in changing the strength of the connections (synapses) between each other in a way that depends on their relative activities. Specifically, if neuron A sends a connection to neuron B, the latter cell needs a way to update the importance of A’s input. Neurons communicate by sending aroundspikes, large pulses of voltage, and it is generally not the case (in the mammalian brain) that a single presynaptic neuron (A) can cause a postsynaptic neuron (B) to fire (spike), rather some several hundred or thousand presynaptic neurons must spike at almost the same time to cause a postsynaptic neuron to fire. I say this because it makes clear the subtle specificity that is needed in synaptic modification, to pick out which of these many inputs have useful information for the cell.

The sensible mechanism, called spike-timing dependent plasticity (STDP), works by up-weighting or strengthening synapses from presynaptic neurons that spiked a short time (20 milliseconds) before the postsynaptic neuron, and down-weighting those in which the presynaptic spike came during a short period after the postsynaptic spike. The large-scale analogy (mentioned by Dr. Poo in his talk) is that of Pavlov’s dog: the canine learned to associate the bell with the meat when the sound preceded the reward, but not when the order was reversed.

In his excellent lecture, Dr. Poo presented some convincing results concerning the molecular mechanisms that might be at work on a microscopic level to achieve this effect, but he concluded with a different and stimulating point.

He presented data from experiments that had been conducted by a post-doctoral researcher in his lab, German Sumbre, using Zebrafish. The results indicated that even very young members of this species are able to learn a predicable series of 60 or so light pulses, delivered every 0.5, 1 or 4 seconds, as indicated by their immaculately timed execution of an escape response called a tail-flick for two or so extra intervals beyond the conclusion of series, right when the pulses would have arrived.

That the fish were able to do this is not incredibly surprising, many animals display this type of predictive behavior. However, it is entirely mysterious how this might be happening at a neuronal level. Dr. Poo has made magnificent progress in understanding how learning proceeds on very short time-scales (10s – 100s of milliseconds), but it is still quite unclear how learning of longer-period phenomena might be achieved by the nervous system. In fact, Dr. Poo appealed to the audience, saying: “If anybody has any ideas how this might be studied, I am anxious to hear them.”

Mirroring the urgency apparent in Dr. Poo’s request, there was a paper published recently on this very topic showing that amoeba are capable of just this sort of learning of intervals1. No doubt this will be a hot topic of research in the near future. Further, this sort of example shows us both how far we’ve come in understanding brains, and the chasms yet to be bridged in moving forward.

References

1. Saigusa T, Tero A, Nakagaki T, Kuramoto Y. (2008) Amoebae anticipate periodic events. Phys Rev Lett. 100(1):018101

On Sexual Selection

Calypte anna

This gentlebird has developed a very neat ability. He can produce a high pitched chirp through the vibrations of its tail-feathers in a high speed dive. Christopher James Clark and Teresa J. Feo at Berkeley did some careful analysis to show that this was in fact not a vocalization and they speculate that this act of sonic production is most likely useful for attracting females1.

Generally, I think of evolution as being driven by fitness, but this case reminded me of how critically important sexual selection is in determining special stability and the retention of genetic traits. Peacocks present a more well-known example, their elaborate tail feathers being of a similarly minimal utility in any other (non-reproductive) aspect of their lives.

In sexual creatures, its actually fairly obvious that this would be the case, since maximal reproduction leads to maximal offspring. It is thus important to remember that what drives evolution is not simply survival, but survival and reproduction.

References

1. Clark CJ, Feo TJ. (2008) The Anna’s hummingbird chirps with its tail: a new mechanism of sonation in birds. Proc Biol Sci. 22;275(1637):955-62.