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I’m currently (as of 2005) a graduate student in the computational neurobiology program at UCSD, in Charles Stevens's lab. In general, I’m interested in models of neural coding, computation, and learning in systems with of about 3-30,00 neurons.

Posters

Really really broad topics of interest

Most of these questions are so big that I don’t expect them to be answered in my lifetime.

  • how qualitative mental states can be encoded in quantitative data parameters

  • meaning

  • fundamental computational elements of local microcircuits, network motifs

  • the neural coding of semantic knowledge / semantic memory

  • exploring the differerent classes of neural network models and other models of computation that have been imagined

  • interoperability in the brain; how is data shared between different "thought modules"? how is data routed through the neural network?

More specific research interests

  • Thalamus, basal ganglia, and neocortex. I’m interested in understanding the "basic plan" of these structures, at the microstructure/local circuit level, across placental mammals; I’m also interested in theoretical models of their operation and function.

  • Neural coding

  • Neuronal cell culture as an experimental preparation for studying network activity and local circuit computation. Yes it’s unnatural, but it’s the only prep we’ve got where we have a shadow of a chance of simultaineously monitoring the activity of ALL of the cells involved in a particular computation (at least for the next couple of years). I’m also interested in the experimental techniques necessary for doing these sorts of experiments (for example, all-optical electrophysiology, multi-electrode arrays).

  • Central pattern generators. Particularly small ones in invertebrates. These serve as a tractable system for cataloging part of the fundamental reportoire of neural computational architectures.

Even more specific research interests

  • Song memory in the Zebra Finch: In this system, we have a real chance to discover exactly how the song is encoded in memory. And, we can do this by going backwards from the motor outputs, without having to worry about other parts of the system (for instance, audition) (if we were trying to figure out how the song is learned, we’d have to care about this other stuff; but determining the format of the storage of the memory should be easier).

  • Multi-electrode arrays: seem to be the best way right now to simultaineously record or stimulate a number of neurons

  • All-optical electrophysiology techniques: over the course of decades, I agree with Eric that optical methods will displace MEAs. </ul>

Activities

Random thoughts and ideas

Random notes

  • /notes-science-neuro (beware: these notes were written for personal use; they are not necessarily readable)

Potential future projects

  • develop the theory of arc computation

  • How do humans hold inconsistent beliefs? How do humans do what A.I. calls "commonsense reasoning"? This is related to paraconsistent logics, confabulation after brain injury, and to conspiracy theories.