Brad

@bradtheilman

Computational neuroscience Ph.D. student at UC San Diego. Studying neural population activity in songbirds with topology. From Cincinnati. Callsign: KD6UC

San Diego, CA
Vrijeme pridruživanja: ožujak 2011.

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  1. 30. sij

    The causal structure of living things is circular. If you zoom in to the circle, it looks more and more like a line (input -> output). But life is a global section, so we can't forget about that global topology. Even though the input->output POV is incredibly tempting.

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    29. sij

    Watch the M7.7 Caribbean earthquake waves roll across the USArray Transportable Array seismic network ( )!

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  3. 28. sij

    Amazing surface waves arriving now in San Diego from the in the

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  4. 28. sij

    M7.3 Earthquake in the Caribbean sea btwn Cuba and Jamaica. Actually able to watch the seismic waves arrive at San Diego in real time!

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  5. 10. sij

    This is interesting- isn't it true that "dynamical triangulation" approaches to quantum gravity face a similar issue? The universes that result are all crumpled up.

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  6. 23. pro 2019.

    I'm thankful to have been a participant in 2018. It was one of the best things I've ever done in graduate school!

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    12. pro 2019.

    Just posted a paper on biorxiv about using latent models to visualize, quantify, and synthesize animal vocalizations with examples from 29 different species including humpback whale, songbirds, monkeys, humans, mice, and bats. 

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    13. pro 2019.

    Are you an undergraduate interested in working on a seismological research project this summer? If so, consider applying for an a paid summer in the IRIS Summer Internship Program.

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  9. 8. pro 2019.

    Math classes are for learning math, yes- but more importantly for learning how to learn math. This point often gets lost.

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    7. pro 2019.

    Temporally distributed, and a little spatially dispersed HVC activity of zebra finches can be explained by a polychronous network model with various axonal conduction delays (1-7.5 ms, as in rat L4) rather than a synfire chain model Egger et al. (Long lab)

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  11. 5. pro 2019.

    Useful "applied" way to think about an exact sequence of sheaves: 0 -> A -> S -> M -> 0 Ambiguity, System, Measurement Sheaf cohomology measures ways measurements fail to specify the system. Inspired by:

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  12. 4. pro 2019.

    The last pic is a newer spectrometer with a folded optical axis. I haven't tried it yet because I need to get an appropriate mirror. You can see the space for it on the top right. It was a lot of fun to figure out all the angles and positions for the components.

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  13. 4. pro 2019.

    The biggest problem was the Bayer (color) filter on the image sensor seriously degraded the spectra from continuous sources like the Sun (I wanted to look at Fraunhofer lines). So I need a B&W camera. Or to carefully etch away the filter...

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  14. 4. pro 2019.

    Back in May I got interested in spectrometers but never posted what I ended up doing after the initial attempts. I made one with a RasPi NoIR camera that was surprisingly functional - shown is the spectrum from a fluorescent bulb that agrees with a reference I found online.

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  15. 1. pro 2019.

    The plots have the same scales. I think the bulk of the noise is due to the wind stirring up the ocean which barrages the coast, less than a mile from the lab. Probably the wind shakes the building some too.

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  16. 1. pro 2019.

    San Diego experienced some heavy wind/rain over Thanksgiving. It was fascinating to see the effect this had on the seismic noise in my seismometer. On the left, the background during the worst of the storms. On the right, the background from the last six hours of calm weather.

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  17. 1. pro 2019.
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  18. 1. pro 2019.

    We might be tempted to call these noise sources, but they are critical to the operation of the circuit!

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  19. 1. pro 2019.

    Another example comes from the field of Evolvable Hardware: Evolved circuits made use of radio waves emitted by computers in the room to enhance their fitness, or only worked if they were plugged in to the 10 MOhm input impedance of a scope!

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  20. 1. pro 2019.

    I think this is an import point for theory development because we want to mold our theories to fit neuroscience, not mold the neuroscience to fit our own expectations. At least for me, glossing over these kinds of points makes it easy to fall into the latter.

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