This means I'm on the lookout for potential PhD students and post-docs. Nothing certain yet, as grants haven't come back, but if you're looking for a place to do you PhD, or thinking about a post-doc in the next year or two, hit me up.
It turns out, San Diego's a pretty nice city, and has pretty good cognitive science and neuroscience programs (FALSE HUMILITY PRECEDING).
You can find me in the following ways:
- Via my email address on my faculty webpage
- At the UCSD booth at the 4th Enhancing Neuroscience Diversity through Undergraduate Research Education Experiences (ENDURE) meeting on Saturday, Nov 15 from 9:30-11:00am at the Marriott Marquis, Independence Ballroom EFGH
- At our book signing at the Princeton University Press booth from 11:00-12:00 on Monday, Nov 17
- At BANTER on Monday night (probably)
- At my collaborator's poster session Tuesday, Nov 18, 13:00-17:00, off and on (abstract below)
|Presentation Title:||Automated “spectral fingerprinting” of electrophysiological oscillations|
|Location:||WCC Hall A-C|
|Presentation time:||Tuesday, Nov 18, 2014, 1:00 PM - 5:00 PM|
|Presenter at Poster:||Tue, Nov. 18, 2014, 3:00 PM - 4:00 PM|
|Topic:||++G.04.e. Electrophysiology: Electrode arrays|
|Authors:||M. HALLER1, P. VARMA1,2, T. NOTO4, R. T. KNIGHT1,3, A. SHESTYUK1,3, *B. VOYTEK4,5,6;|
1Helen Wills Neurosci. Inst., 2Electrical Engin. and Computer Sci., 3Psychology, Univ. of California, Berkeley, Berkeley, CA; 4Cognitive Sci., 5Neurosciences Grad. Program, 6Inst. for Neural Computation, UCSD, La Jolla, CA
|Abstract:||Neuronal oscillations play an important role in neural communication and network coordination. Low frequency oscillations are comodulated with local neuronal firing rates and correlate with a physiological, perceptual, and cognitive processes. Changes in the population firing rate are reflected by a broadband shift in the power spectral density of the local field potential. On top of this broadband, 1/f^α field, there may exist concurrent, low frequency oscillations. The spectral peak and bandwidth of low frequency oscillations differ among people, brain regions, and cognitive states. Despite this widely-acknowledged variability, the vast majority of research uses a priori bands of interest (e.g., 1-4 Hz delta, 4-8 Hz theta, 8-12 Hz alpha, 12-30 Hz beta). Here we present a novel method for identifying the oscillatory components of the physiological power spectrum on an individual basis, which captures 95-99% of the variance in the power spectral density of the signal with a minimal number of parameters. This algorithm isolates the center frequency and bandwidth of each oscillation, providing a blind method for identifying individual spectral differences. We demonstrate how automated identification of individual oscillatory components can improve neurobehavioral correlations and identify population differences in spectral and oscillatory parameters.|