This is a live blog from Neuroinformatics 2009.

Neuron systems are incredible stable over time. Looking at a number of systems, including pyloric pattern generator — stomachs in crabs (?). Is a pacemaker system; it’s very stable between individuals and over time. Despite this, for example, the maximal conductance in the neurons varies pretty widely. How come this variability doesn’t affect stability?

Have generator a single compartment model, looking at 8 dimensional parameter space and making a big database. Trying to replicate the variance that they see within the biological systems. Tend, with their model, to get similar output for these different conditions. Conclusion of this is that neuronal system have a large solution space within which they maintain their functioning.

Question, how are these solution spaces distributed within the total parameter space? Could be a single unique solution, could be islands, etc. Been collaborating in high-dimensional space visualisation using dimensional stacking. Think it’s a slices through the dimensional space, stacked out side-by-side.

Talks about cell-type specific co-regulation of ion channels; there are lots of correlations between different forms of current. Interestingly, most of the relationships are linear and, so far, all of them are positive; it’s not clear that there are any negative correlations.

Have classified their model space. Found that correlation between channels is always in the same direction — a correlation which is positive in one cell type will never be negative in another. However, they are showing a negative relationship in some circumstances, when the experimental processes show a positive relationship which is an open question.

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