Kai Miller on Muck Rack

Kai Miller

New York
As seen in: Medium, Nature, Wiley Online Library, PLOS, Frontiers, Black Entertainment Television (BET), Cell Press, NewsOne.com, JNeurosci, ScreenAnarchy and
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Articles

Kainic acid pig model of hippocampal epilepsy

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By Filip Mivalt, Daniela D. Maltais, Inyong Kim, Jiwon Kim, Andrea Lopez, Andrea Lรณpez Verified, Elizabeth Harty, Luke Kim, Nicholas M. Gregg, Christopher Gow, Kai Miller, JAmie VAn Gompel, Kent Leyde, Vaclav Kremen, Su-Youne Chang, Gregory Worrell, Patrik Began, Veronika Krakorova, Shelja Sharma, Daniel R. Montonye
| Nature Verified
Abstract Translational large-animal models that can accommodate human-scale implantable devices are essential for advancing neuromodulation therapies in epilepsy. This study establishes a kainic acid (KA)-induced porcine model of mesial temporal lobe epilepsy (mTLE) using clinical imaging, stereotactic surgery, and a fully implantable neural stimulator-recorder (INSR) device designed for humans.
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The Next Competitive Advantage in Advertising Is Geographic

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By Kai Miller
| 4A's
Advertising has spent the last decade chasing precision while overlooking one of the oldest and most reliable predictors of consumer behavior: proximity. The industry built increasingly sophisticated ways to identify audiences, optimize impressions and automate delivery. Yet one of the strongest indicators of whether advertising drives action remains underweighted in modern media planning: how near a household is to where conversion can actually happen.
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Broadband synergy versus oscillatory redundancy in the visual cortex

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By Louis A. Roberts, Juho Aijala, Florian Burger, Cem Uran, Michael Jensen, Kai Miller, Robin Ince, Martin Vinck, Dora Hermes, Andres Canales-Johnson
| Nature Verified
Abstract The cortex generates diverse neural dynamics, ranging from broadband fluctuations to narrowband oscillations at specific frequencies. Here, we investigated whether broadband and oscillatory dynamics play different roles in the encoding and transmission of visual information. We used information-theoretical measures to dissociate neural signals sharing common information (i.e., redundancy) from signals encoding complementary information (i.e., synergy).
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