Julian Kosciessa

@JulianKosciessa

Cognitive neuroscientist @ MPI for Human Development

Vrijeme pridruživanja: listopad 2017.

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  1. Prikvačeni tweet
    25. stu 2019.

    Now open access in : "Single-trial characterization of neural rhythms: Potential and challenges" Code available @ Highlights follow in the thread.

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  2. proslijedio/la je Tweet
    29. sij

    Check out our new modelling paper, now on Biorxiv! 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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  3. proslijedio/la je Tweet
    10. pro 2019.

    CCN 2019 recordings are up Featuring + more Population coding, (inverse) RL, Causality, functional decoding, neuropixels, free energy, ...

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  4. proslijedio/la je Tweet
    26. stu 2019.

    Going to stream (low tech hangout) our morning discussion on sharing brain data under GDPR re open brain consent 10am Brussels tomorrow

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  5. proslijedio/la je Tweet
    25. stu 2019.

    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.

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  6. proslijedio/la je Tweet
    25. stu 2019.
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  7. 15. stu 2019.

    Finally one step closer to having read out ever paper to me.

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  8. 15. stu 2019.

    Fantastic workshop on simulating power from linear mixed models by (see ) at today. Thanks for a great symposium.

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  9. proslijedio/la je Tweet
    11. stu 2019.

    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. w/ and Ulman Lindenberger

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  10. proslijedio/la je Tweet
    23. ruj 2019.

    I'm looking for a PhD student, a postdoc, and a lab manager for my new lab fall 2020. Excited about computational models of decision&emotion, and large smartphone data sets in patients? Contact me PhD Dec 1 deadline Please RT!

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  11. 3. ruj 2019.

    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.

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  12. 3. ruj 2019.

    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.

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  13. 3. ruj 2019.

    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.

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  14. 3. ruj 2019.

    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.

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  15. 3. ruj 2019.

    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.

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  16. 3. ruj 2019.

    New preprint on counterintuitive relations between spectral power and multi-scale sample entropy of brain signals. With and . 1/6 Highlights follow in the thread.

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  17. 3. ruj 2019.

    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.

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  18. 3. ruj 2019.

    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.

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  19. 3. ruj 2019.

    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.

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  20. 3. ruj 2019.

    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.

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