Understanding the brain

I think that sometimes philosophy is important and this may be true for neuroscience right now. I don’t mean ivory tower, “what is life?” type philosophy (although that is important too) but trying to pin down what it would mean to say “we understand the brain.” When would we know that the game is won? I think this is important for neuroscience now to help to guide research. What should we be doing?

Materialism and meaning

Let me first say that I am a die hard materialist in that I do believe that there is nothing beyond the physical world. I also believe that physics and hence the physical world is computable in that it can be simulated on a computer. However, helped along by Stuart Kauffman’s new book Reinventing the Sacred, I have been gradually edging towards accepting that even in a purely materialistic world there is some amount of arbitrariness in our perception of reality. Kauffman argues that this arbitrariness is not “mathematizable”. I will argue here that the question can be formulated mathematically and can be shown to be undecidable or at best intractable. Kauffman’s thesis is that we should take advantage of this arbitrariness and make it the foundation of a new concept of the sacred.

Realistic versus abstract neural modeling

There is a very interesting discourse running on the comp-neuro email list. I’ve only caught the past week but it seems to be a debate between the benefits of “abstract” versus biological “realistic” models. (Let me caveat that everything here is my interpretation of the two points of view). Jim Bower, who is a strong proponent of realistic modeling, argues that abstract models (an example is a network of point neurons) add biases that lead us astray. He thinks that only through realistic modeling can we set down all the necessary constraints to discover how the system works. In a side remark, he also said that he thought the most important problem to understand is what information a given neuron transmits to another and the rest is just clean up. Bower believes that biology is pre-Copernican and that abstract modeling is akin to Ptolemy adding epicycles to explain planetary motion and realistic modeling is closer to the spirit of Kepler and Newton.

New Paper on insulin’s effect on free fatty acids

A paper I’ve been trying to get published for two years will finally appear in the American Journal of Physiology – Regulatory, Integrative, and Comparative Physiology. The goal of this paper was to develop a quantitative model for how insulin suppresses free fatty acid levels in the blood. A little background for those unfamiliar with human metabolism. All of the body’s cells burn fuel and for most cells this can be fat, carbohydrate or protein. The brain, however, can only burn glucose, which is a carbohydrate, and ketone bodies, which are made when the body is short of glucose. Why the brain can’t burn fat is still a mystery. It is not because fat cannot cross the blood brain barrier as is sometimes claimed. Thus, the body has a reason to regulate glucose levels in the blood. It does this through hormones, the most well known of which is insulin.

SIAM Lifesciences ’08

I’ve just returned from the Society of Industrial and Applied Mathematics (SIAM) Lifesciences meeting in Montreal. I haven’t traveled to a meeting since my baby was born so it was nice to catch up with old friends and the field. I thought that all of the plenary talks were excellent and I commend the organizing committee for doing a great job. Particularly interesting was a public lecture given by Stuart Kauffman on his new book “Reinventing the Sacred”. That talk was full of many ideas that I’ve been directly interested in and I’ll blog about them soon.

Penrose redux

In 2006, I posted my thoughts on Roger Penrose’s argument that human thought must be noncomputable. Penrose’s argument follows from the fact that Godel’s incompleteness theorem states that there exist true statements in a consistent formal system (e.g. arithmetic with integers) that cannot be proved within that system. The proof basically boils down to showing statements like “this statement cannot be proved” are true but cannot be proved because if they could be proved then there would be an inconsistency with the system. Turing later showed that this was equivalent to saying that there are problems, known as undecidable or uncomputable problems, that a computer could not solve. From these theorems, Penrose draws the conclusion that since we can recognize unprovable statements are true then we must not be a computer.