Computational neuroscience vs Neuroinformatics

Below are some hastily sketched thoughts, by no means complete, on the distinction between computational neuroscience and neuroinformatics.

Computational Neuroscience: The field posits computational candidates for mechanisms by which the brain carries out a certain function. When we say computational candidates, we loosely talk about algorithms. I think algorithms have two or more theoretical aspects. I'll try to articulate those below.

First, the goal of the function performed by the brain must be articulated by a cost function. For example, if the goal is reaching out for an object and grasping it, then the cost function could minimize muscular effort, minimize the #neurons needed to encode the task, or minimize the error rate of the task assuming that it is performed several times. Sometimes, the cost function need not describe a very specific task such as grasping, but could describe a general organizing principle of the brain - such as minimize energy consumption, minimize the use of connective tissue, etc.

Second, the process by which the cost function is optimized must be articulated, keeping in mind that such a process must be feasible in the wet brain. The wet brain provides structural and functional bounds on what a candidate algorithm can and cannot do.

With these basic ingredients, the flavours then vary because the choice of level of description can be vastly different. Someone can talk about how ion channel ratios on the cell membrane are optimal for grasping, whereas someone else can talk about why the number of cortical areas devoted to grasping the brain is optimal. Both these optimalities could be treated computationally by using selective pressures during evolution, or selective pressures during brain
development as explanatory variables. To complicate matters further, optimality in the brain can be posited at the level of evolution, brain development, learning (plasticity), and adaptation.

Neuroinformatics: The field is concerned with issues of data analysis and visualization of neuroimaging data for human interpretation purposes. The algorithms applied here (such as ICA/CCA/ridge regression etc.) need not conform to any constraints posited by the wet brain. The field does not aspire to explain how the brain performs a certain function - it just aids the process of evidence accumulation, which is of course important for theoretical and CNS because otherwise we wouldn't have phenomena to explain and our theories cannot be validated. In this sense neuroinformatics is a tool for experimental neuroscience.

Building on the brain

I came across this piece in the latest issue of Neuron.

John P Eberhard. 2009. Applying Neuroscience to Architecture. Neuron 62:753-756.

Basically, it is a promo piece for the author's latest book Brain Landscape, which advocates architects to apply findings of brain imaging to their design of schools, hospitals, public spaces, old age homes and memorials. Eberhard is the founding President of the non-profit Academy of Neuroscience for Architecture, established in 2003. You get the picture.

Why have ideas such as these and other low-hanging fruit (neuroeconomics, neurocinematics) become so popular these days, without anybody bothering to address how neuroscientific knowledge (such as: the prefrontal cortex is involved in decision making) is not completely superfluous to knowledge from conventional psychology and the behavioral sciences (such as: natural light improves class grades) as far as application domains (such as architecture) are concerned? Note that I do not dispute the fact that such neuro-marriages may be intellectually stimulating.

The saving grace is that it wasn't called neuroarchitecture (not to be confused with neuroarchitectonics: the beautiful and painstaking characterization of brain anatomy in terms of cell types, synapse densities, tissue properties, relative thickness of cortical layers, vasculature etc. etc. which early 20th century greats like Cajal and Brodmann pioneered).

I'm not arguing for traditionalism, I'm not arguing for scientists to be conservative with their imagination. On that contrary, I am disappointed that out-of-the-box thinking falls so dreadfully short of the mark. Why can't we be more original? It is not as though fresh insight and imagination cannot be applied to traditional stuff of the brain such as anatomy, hemodynamics, connectivity, learning, memory etc.

What next? Neuromusicology, neuromarketing, neuropublishing, neurojournalism, neurolaw, neuro-neuroscience? Up for grabs. Quick, before somebody else.

Contests for innovation

So, here's a simple (perhaps simplistic) idea to accelerate the development of innovative methods for neuromagnetic source separation.

1. Create a simulated dataset for benchmarking purposes with unknown number of sources. It should be hard to find the underlying sources. The hardness is engineered by placing sources in known blind spots of existing algorithms (eg. since we know that ICA cannot separate Gaussian sources, or MUSIC cannot separate correlated sources, we deliberately introduce them).

2. Release the benchmarking dataset online, provide a two-year time frame, and announce a small prize money like $50k [roughly, less than the cost of one grad student, without factoring in the resources needed to create a benchmarking dataset].

3. Provide a minimum set of rules for reporting the method and its results.

4. Incentivize journal editors to make a 'special issue' out of the contest solutions.

This would

a) force the already tiny 'neuroimaging methods' community to take each others' work into account more seriously than merely paying lip service.
b) incentivize the abolishment of 'idea embargoes' non-scientific in root.
c) avoid the repeated publication of existing solutions in lower tier journals ad infinitum.

and thus, save resources, and accelerate solutions.