Posted November 6, 2012 by Dr. Henri Montandon in brain science

We never know what we are talking about

Thus mathematics may be defined as the subject in which we never know what we are talking about, nor whether what we are saying is true.

Bertrand Russell said it.

But when we try to talk about some things in neuroscience including consciousness science, mathematics creeps in on little cat feet. One of those things is patterns.

Cook up a big ol’ heap o’ data, looking for the patterns. Or find some patterns in the math and look for them in the data. It works both ways, and back and forth too.

The Journal of Mathematical Neuroscience is an Open Access online journal. Here’s what it does:

The Journal of Mathematical Neuroscience (JMN) publishes research articles on the mathematical modeling and analysis of all areas of neuroscience, i.e., the study of the nervous system and its dysfunctions. The focus is on using mathematics as the primary tool for elucidating the fundamental mechanisms responsible for experimentally observed behaviours in neuroscience at all relevant scales, from the molecular world to that of cognition.

Journal articles are free to anyone, (although with different rules of use and copyright depending on the journal.) In what they call a “reversed business model” it is the authors who pay to have their work published in the journal. OK. I don’t understand why anyone would do that, when it is so easy to put a piece of work online for much less than the cost per article ($1290). Maybe it’s a brand thing. (But it’s really an academic publishing thing.)

Anyway, there are some nifty patterns at hand for those who like them. Who knew that the transition from spiking to bursting in each model system is given by an explosion of torus canards? You might never actually use this finding, but tucked away, it might come in handy some day. As a person who has never studied a gradient he didn’t like, I am pleased with articles like Gradient estimation in dendritic reinforcement learning. While in an article on signal processing in the cochlea, it is nice to see the use of symmetry groups, wavelets and Lie algebras, well, just because these have a familiar classical feel. In fact, many of the articles can be comfortably browsed.










Dr. Henri Montandon