Vesicles

Why do neurons have synapses at all? Why not just wire dendrite to axon directly?

Vesicles discretize synaptic events. The "quantal count" is the number of vesicles per synaptic event; by interposing synapses, rather than just passing the electric potential directly, the signal is rendered digital.

Presumably a more important aspect, synapses allow the trans-cellular environment to affect the signal, e.g., NO.

Transcription Factors and Learning

What's the role of transcription factors (e.g., CREB) in LTP? Looking into Kandel's work for the 2000 Nobel prize.

Random Thought

A search algorithm layered on top of (or underneath, I suppose) an evolutionary search that finds areas of broad neutrality and pushes the population out to the edges by way of a gradient search.

If a mutation is found to be nearly neutral, pause evolution and probe in the direction of the mutation, looking for the range of neutrality. Then spawn children within that range (stocasticly or evenly distributed).

Rough idea: more efficiently take advantage of neutral planes.

Although it would surely suck, I'd be interested in watching this method taken to the extreme-- that is, doing an exhaustive search for neutral areas periodically. Perhaps a reasonable approach when evolution stalls.

To Read

Fitness Landscapes and Evolvability. Smith, Husbands, Layzell, O'Shea. Evolutionary Computation 2002.

A Small-systems Approach to Motor Pattern Generation. Nusbaum, Beenhakker. Nature 2002.

"Freezing and Freeing of Degrees of Freedom"

An interesting paper in Adapative Behavior, "Motor Skill Acquisition under Environmental Perturbations: On the Necessity of Alternate Freezing and Freeing of Degrees of Freedom" (Berthouze, Lungarella; Neuroscience Research Institute, Tsukuba, Japan). I'd like to try to conceive of the "freezing and freeing" approach along the lines of neutral mutations. I realize it's a bit far-fetched, especially in the paper's particular focus on infant development, but I'm interested in ways to coerce a system to be fully traversable by nearly neutral mutations, and this is perhaps good inspiration.

Realistic Neuron Models

After reading "An Analysis of Neural Models for Walking Control" I've been interested in looking more carefully at neuron models that go beyond the sigma-in-a-circle that ANN research usually considers.

NEURON and GENESIS are too big software packages that seem popular. NeuroConstruct looks nice as well.

(One confounding factor worth considering is that there are so many different types of neurons with such different properties. Purkinje cells, Pyramidal cells, Granule cells, "mossy fibers", etc.)

Intercellular connections: Gap junctions. Chemical synapses.


MorphML