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Neuronal electricality founded in murburn-thermodynamic principles: 1. Background and basic theoretical formulation
Kelath Murali Manoj, Nagamani Sukumar
https://arxiv.org/abs/2604.24772 https://arxiv.org/pdf/2604.24772 https://arxiv.org/html/2604.24772
arXiv:2604.24772v1 Announce Type: new
Abstract: Trans-membrane gradients and fluxes of cations (H , Na , K , etc.) were deemed to be the rationale of electrical activities of aerobic cells/organelles, as per classical perceptions. Murburn concept (an umbrella of theorization based in stochastic redox processes) has afforded novel models for various metabolic, bioenergetic and electrophysiological outcomes. Herein, the foundational mechanistic formalisms for the electrical activities of neurons that lead signal relay along the axonal length are provided. Electron Holding potential (EHP), a dimensionless field/state variable (related logarithmically to electron chemical potential) is used to explain neuronal activity. By combining local redox relaxation dynamics with spatial transport driven by thermodynamic gradients, we derive a unified reaction-transport-relaxation equation that captures resting potential, excitability, waveform generation, and signal propagation within a single framework. Nonlinear local redox kinetics naturally give rise to threshold behavior, all-or-none responses, and stable spike waveforms. The framework accommodates known physiological variability and provides a direct bridge between metabolic/redox state and electrophysiological behavior. This work establishes a chemically grounded, non-circular alternative to ion-centric models and offers testable predictions for neuronal dynamics across biological systems. In the second part of this work, we compare the new theory with existing systems, provide further evidence, simulations and describe elaborate agendas for falsification and validation.
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@… Well, in the 80s and 2000s, folks in the IM industry were quite reluctant to say the SEC didn't have authority, for fear of retribution. Also, the ICI historically has been reluctant to say the SEC doesn't have authority, for two reasons. First, it preferred to work with the SEC. Second, it often wanted the SEC to have broad authority, either to give …

Ich habe letzte Woche ein 30 Jahre lang brach liegendes Werk veröffentlicht und vergessen, hier darüber zu posten. Auf die eine Woche kommt es dann auch nicht mehr an. Achtung, die Musik ist anstrengend.
From: @…
https:/…

u:labor | unterhaltung (@ulabor@berlin.social)
Attached: 1 image I joined #subvertfm, the cooperative Bandcamp alternative. And finally released something obscure I recorded 30 years ago. I had a lot of talks about it on Popkomm '96. Like everyone I talked to got fascinated while pointing out unsellability. HEUTE – Eirisch EP What if you time-modulated a 4/4 beat grid with a sine wave? You end up with very strange rhythmics, depending on amplitude sometimes even 3/4-ish. You might want to break free from your usual dance patterns. htt…

On Bluesky last night
Catherine Rampell noted that in documents Trump released
there were confirmations of Russia’s work on Trump’s behalf in 2020
and that China “did not intend to “try to affect the election.”
https://bsky.app/profile/crampell.bsky.social/…

Catherine Rampell (@crampell.bsky.social)
one of the supposedly damning docs the White House released tonight says Russia tried to undermine BIDEN. Also says China "prefers that President Trump be defeated" but actually "Beijing did not intend to try to affect the election" (!)

A discrete Boltzmann model with state-dependent power-law relaxation time for nonequilibrium transport in compressible flows
Demei Li, Zhongyi He, Huilin Lai, Yanbiao Gan, Hailong Liu, Pengfei Lin
https://arxiv.org/abs/2605.18216 https://arxiv.org/pdf/2605.18216 https://arxiv.org/html/2605.18216
arXiv:2605.18216v1 Announce Type: new
Abstract: Thermodynamic nonequilibrium effects play a central role in momentum and energy transport in compressible flows. In conventional BGK kinetic models, the relaxation time $\tau$ is taken as a constant, which neglects the dependence of the relaxation process on local macroscopic states. To overcome this limitation, we develop a discrete Boltzmann model with a density- and temperature-dependent power-law relaxation time, termed DTRT-DBM, in which $\tau=\tau_0(\rho/\rho_0)^a(T/T_0)^b$. This formulation extends the discrete Boltzmann framework to flows with spatially varying nonequilibrium intensity. The model is validated by the Sod shock tube and by analytical solutions for viscous stress and heat flux, demonstrating accurate recovery of both macroscopic wave structures and nonequilibrium quantities across shock waves, rarefaction waves, and contact discontinuities. On this basis, phase diagrams of viscous stress and heat flux are constructed to examine how these quantities depend on the power-law exponents $a$ and $b$. The extrema of these quantities depend exponentially on the model parameters and exhibit regime-dependent behaviour. The roles of $a$ and $b$ are not symmetric: the nonequilibrium response is more sensitive to $a$ when density gradients dominate, but more sensitive to $b$ when temperature gradients dominate. Within the parameter range and flow configurations examined here, higher-order viscous stress increases the growth rate of the total viscous-stress extremum, whereas higher-order heat flux reduces the growth rate of the total heat-flux extremum. These results show that the proposed model can capture different higher-order nonequilibrium responses in compressible flows and provides a framework for the modelling and analysis of multiscale nonequilibrium processes.
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Just realized that the fact that newer large language models keep getting bigger in terms of parameters is kind of a tell about how they work, even as it's also kind of a requirement from the investment standpoint.
Very roughly, models develop complex functional internal state about some sub-domains, and merely memorize many examples in others. In reality it's more complicated than this and even in this simplified metaphor it's a mix between memorization and "real" "understanding" in each domain. But the point is that if companies were really working towards AGI, they'd be feeding more data into models with *fewer* parameters (that's how you force a model not to memorize) instead of building bigger and bigger models (expands the illusion of competence through increased capacity to memorize).
But being the only ones with the hardware to train an even-bigger model is one of their few competitive advantages, and signing new deals for even more hardware is one of the only ways they can signal to investors that they'll retain their advantage and thus not be destroyed by a food of competitors. That's also how they can convince the hardware dealers like NVidia to continue with circular investments. So they have to run in that direction, regardless of the scientific merits.
This is why someone like LeCun would leave that side of things.
#LLMs #AI

‪Lisa Schiff€
‪@lschiff.bsky.social€
· 3m
"The case for academic freedom and the independence of the university is far shakier today than it was even a decade ago..." Register for the 5/19 #DefendResearch webinar with Sara Rouhi and Brandon Turner
on this aspect of #censorship

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