deathstarrobot.bsky.social
Author of Bridging Molecular Mechanisms and Neural Oscillatory Dynamics, Micah's New Law of Thermodynamics, and Super Dark Time: Gravity Computed from Local Quantum Mechanics. SVGN.io
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In Egyptian terms, he must live ma’at—not merely study it. Only then does the feather rise to meet the heart.
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He can be saved only by lightening the heart—by unburdening guilt, embracing truth beyond materialism, and aligning with Ma’at: not just intellectual rigor, but moral clarity, humility, and cosmic balance.
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In Ancient Egypt, the scale of Ma'at, in the Hall of Judgment. On the left scale a heart, on the right scale a feather. Beneath a crocodile. If Sean Carroll's heart becomes heavier than a feather, on the Egyptian scale, the alligator of guilt drags him in a downward spiral. How can he be saved?
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"maybe you are not as much of a Good Person as you thought you were" He was partly self-righteous & partly looking to others for his self-worth. Thus his heart was weighed against Ma'at’s feather of truth; if found heavier from sin, he will be devoured by Ammit, ending the soul’s journey.
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AI was used to help format my existing ideas in a nice way. I developed all of my key ideas before AI existed. When serious researchers look at the evidence on my github and in my published recordings they will find all of my ideas accounted for prior to AI.
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AI currently does not have the capability of creating new physics or consciousness ideas like this on it's own. Only a human being could have written what I wrote.
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AI was used to help format my existing ideas in a nice way. I developed all of my key ideas before AI existed. When serious researchers look at the evidence on my github and in my published recordings they will find all of my ideas accounted for prior to AI
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AI currently does not have the capability of creating new physics or consciousness ideas like this on it's own. Only a human being could have written what I wrote.
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AI was used to help format my existing ideas in a nice way. I developed all of my key ideas before AI existed. When serious researchers look at the evidence on my github and in my published recordings they will find all of my ideas accounted for prior to AI.
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AI was used to help format my existing ideas in a nice way. I developed all of my key ideas before AI existed. When serious researchers look at the evidence on my github and in my published recordings they will find all of my ideas accounted for prior to AI
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AI was used to help format my existing ideas in a nice way. I developed all of my key ideas before AI existed.
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It's not marketing it's news. I am also co-writing papers to architect next gen Conscious AI. See: Blumberg, Micah (2025). Self Aware Networks: Oscillatory Computational Agency. figshare. Journal contribution. doi.org/10.6084/m9.f...
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I came up with novel information patterns that link together different ideas and forge new ones. If I want to get credit for my intellectual work, I must write papers. With published papers I have something easily citable.
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No I didn't. I was speaking my ideas into a podcast series called the Neural Lace Podcast from 2017, and in many published audio recordings on Google Recorder, and then I wrote them down in text saved them to Github to create a permanent time stamped record of them in 2022, before AI was a thing.
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No I didn't. I was speaking my ideas into a podcast series called the Neural Lace Podcast from 2017, and in many published audio recordings on Google Recorder, and then I wrote them down in text saved them to Github to create a permanent time stamped record of them in 2022, before AI was a thing.
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Papers can also help defend my work history against those who try to falsely take credit for my work by presenting my work as theirs, which is happening a lot. People are using AI to paraphrase my work and try to pretend that they thought of the concepts I thought of first. Not so fast.
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I came up with novel information patterns that link together different ideas and forge new ones. If I want to get credit for my intellectual work, I must write papers. With published papers I have something easily citable.
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It's not marketing it's news. I am also co-writing papers to architect next gen Conscious AI. See: Blumberg, Micah (2025). Self Aware Networks: Oscillatory Computational Agency. figshare. Journal contribution. doi.org/10.6084/m9.f...
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The trajectory of AI is such that in a few short years no job will be safe. Not event the craftsman art jobs, not even White collar jobs. Anything a human can do AI can learn and do as well. There will be robots that can craft any object from any materials.
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She sends her thanks for comparing her to Barbie.
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We also found that the forward model has to match the true cortical column orientation for it to accurately recover the location of the source. Lots of work ahead to get an accurate estimate empirically!
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This figure features simulation across ca. 330000 vertices (1000s hours of cluster time) to show that anatomical features like: cortical thickness, gyrification, and orientation of cortical columns constrain the precision of the results beyond co-registration error and SNR
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Our 11 layer source space evenly samples the cortical thickness, but the true cortical laminae are not uniform across the brain. We used the BigBrain cytoarchitectonic atlas and mapped our results onto different laminae thicknesses. It still works!
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Co-registration error (HEAD MOVEMENT!) matters a lot as well. True laminar precision falls apart gracefully at an error bigger than 2 mm. Headcasts greatly contribute to surpassing the requirements for reduction in head movement.
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However, when recording in MEG there are many ways it can go not so well. You need SNR greater than -35dB to achieve accurate source recovery. Doable when you look at phenomena like e.g. ERFs or beta burst waveforms (www.sciencedirect.com/science/arti...)
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First you need a suitable source space. We have used an 11 layer mesh that evenly covers the thickness of the cortex between white matter and pial boundaries. Then we constrain the dipole orientation to vectors linking corresponding vertices.
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Overall, we show that true laminar precision with MEG is very much possible. Our simulations show the need for high signal quality, almost no head movement. We also show that both micro and macro scale anatomical features impact the quality of source reconstruction.
www.biorxiv.org/content/10.1...
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We pick a vertex and simulate a signal located at one layer and perform a source reconstruction (with MSP) separately for each layer. We then compare the model evidence across depths. If all goes well, the depth where the source was located has the highest model evidence.
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"Beyond deep versus superficial: true laminar inference with MEG"
Here we present a recipe on how to achieve it and what are the main constraints.
FIGURE CAPTIONS IN ALT!
www.biorxiv.org/content/10.1...
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You are spreading lies,
patek-svendsen.bsky.social
@patek-svendsen.bsky.social You are one of the Islamic Nazi terrorists spreading lies about Israel to harm Jews. You will pay the price for your lies in the end!
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You are spreading lies. You are one of the Islamic Nazi terrorists spreading lies about Israel to harm Jews. You will pay the price for your lies in the end!