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Now open access in
#NeuroImage: "Single-trial characterization of neural rhythms: Potential and challenges" https://www.sciencedirect.com/science/article/pii/S105381191930922X … Code available @ https://github.com/jkosciessa/eBOSC … Highlights follow in the thread. https://twitter.com/JulianKosciessa/status/1168859061636521984 …pic.twitter.com/Ye8w1kAMbv
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Julian Kosciessa proslijedio/la je Tweet
Check out our new modelling paper, now on Biorxiv! https://www.biorxiv.org/content/10.1101/2020.01.28.921205v1 … We simulate how one can decode cortical alpha oscillations to identify memories by their unique temporal signatures, via some very cool binding and timing mechanisms.
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Julian Kosciessa proslijedio/la je Tweet
CCN 2019 recordings are up https://ccneuro.org/2019/videos.asp Featuring
@anne_churchland@behrenstimb@bschoelkopf@gershbrain@NandoDF@nathanieldaw@neuro_kim + more Population coding, (inverse) RL, Causality, functional decoding, neuropixels, free energy, ...@CogCompNeuropic.twitter.com/eBAqS1nuzdPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Julian Kosciessa proslijedio/la je Tweet
Going to stream (low tech hangout) our morning discussion on sharing brain data under GDPR re open brain consent 10am Brussels tomorrow https://github.com/CPernet/open-brain-consent/blob/GLiMR-workshop/README.mkd …
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Julian Kosciessa proslijedio/la je Tweet
First ELM Lab preprint is out! Its a methods paper on adaptive spectral decomposition for ephys data. So if you've ever thought, "is my theta 4-7 or 4-8 or 4-12 Hz?", this paper is for you. We show a way to quantitatively evaluate this question and we look forward to feedback.https://twitter.com/biorxiv_neursci/status/1199113066497134592 …
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Julian Kosciessa proslijedio/la je Tweet
Our tutorial for running entropy analysis in FieldTrip is online at the
@fieldtriptoolbx website! See http://www.fieldtriptoolbox.org/example/entropy_analysis/ …. Our toolbox is at https://github.com/LNDG/mMSE With@JulianKosciessa@LPolyanska@Garrett_Neuropic.twitter.com/TrGgStWmx5
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Finally one step closer to having
@FarlKriston read out ever paper to me.https://twitter.com/spiantado/status/1194841198785024001 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Fantastic workshop on simulating power from linear mixed models by
@shravanvasishth (see https://vasishth.github.io/ ) at@MPI_CBS today. Thanks@doinggood_symp for a great symposium.pic.twitter.com/qwyGEfydiQ
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Julian Kosciessa proslijedio/la je Tweet
New preprint out! We show that a boost in moment-to-moment neural variability in frontal cortex strongly reflects how much people shift their decision bias to become more liberal. https://www.biorxiv.org/content/10.1101/834614v1 … w/
@Garrett_Neuro@jjfahrenfort@JulianKosciessa and Ulman Lindenbergerpic.twitter.com/h7ZlTJIIei
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Julian Kosciessa proslijedio/la je Tweet
I'm looking for a PhD student, a postdoc, and a lab manager for my new lab
@Yale fall 2020. Excited about computational models of decision&emotion, and large smartphone data sets in patients? Contact me http://www.rutledgelab.org PhD Dec 1 deadline https://psychology.yale.edu/graduate/admissions/applying-admission … Please RT!Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
6/6 Finally, based on the observed mismatches between spectral and entropy estimates, we recommend multiple procedures to strengthen claims regarding unique effects of time-series irregularity.pic.twitter.com/VtjGoJlhmx
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5/6 We highlight how control over a signal’s spectral content may alleviate such problems and thus aids in characterizing signal irregularity at specific time scales.pic.twitter.com/0AesX9j0Vx
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4/6 Critically, in a traditional application of adult age differences, mechanistic links between entropy and power led to counterintuitive mismatches between the time scale of neural events and their reflection in entropy time scales.pic.twitter.com/FcBVTYchkQ
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3/6 Moreover, multi-scale entropy aims to capture signal irregularity across multiple time-scales of brain operation. However, we observed that time-scale specific events may be reflected in a rather global manner, thus questioning time scale-specificity.pic.twitter.com/Ea0Rs5RuCG
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2/6 Neural dynamics can be highly irregular, which may reflect healthy brain function. Multi-scale sample entropy has been proposed as an index of such ‘complexity’, although it’s direct relation to spectral power questions such common interpretation.pic.twitter.com/hMF1qwNA0L
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New preprint on counterintuitive relations between spectral power and multi-scale sample entropy of brain signals. With
@neuro_klooster and@Garrett_Neuro. 1/6 Highlights follow in the thread.https://twitter.com/biorxiv_neursci/status/1168706282020376577 …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
7/7 Beyond affording novel rhythm-focused analyses, indices specific to rhythmic periods were more sensitive to task effects compared with traditional, arrhythmic-biased estimates. This suggests a high practical relevance for single-trial rhythm analyses.pic.twitter.com/A2RHJ9d6Rr
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6/7 Despite such challenges, single-trial identification of rhythmic episodes has many benefits, such as a separation of rhythms from arrhythmic background ‘noise’, associated with ‘boosted’ amplitude estimates, and the potential to investigate sustained and transient events.pic.twitter.com/4JhquqEsHB
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5/7 While optimal identification of rhythmic episodes is feasible in noise-free scenarios, detection is impaired when the signal-to-noise ratio (SNR) is low. As this is a stable individual characteristic, there is a strong association between amplitude and duration estimates.pic.twitter.com/apwJqQZ38T
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4/7 To achieve this goal, we extended a previously suggested method (BOSC, Whitten et al., NeuroImage, 2011) and validated its performance in simulations and empirical data.pic.twitter.com/pplbNX5Uoe
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