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Cat Hicks (grimalkina@mastodon.social)'s status on Thursday, 24-Sep-2026 00:06:31 JST Cat Hicks

Look at this cool neuroscience work that my wife is part of!!!!!!!
In the words of one of the authors Brad Voytek: "We show that reducing action potentials to binary {0,1} events throws away meaningful variation in each spike’s waveform. "
https://www.biorxiv.org/content/10.64898/2026.09.15.751814v1
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  Action potential waveforms are state-dependent
  
  Action potentials are brief electrical impulses that form the mechanistic basis for how neurons communicate. While it is well known that the shape of action potentials can differ across neurons, one fundamental assumption is that the complex voltage waveforms of action potentials within a given neuron are reducible to binary spikes. This assumption has constrained our conception of possible neural codes to those amenable to binary signaling, such as rate, temporal, and population codes. Here, we show that action potential waveform variability is not random, but is, instead, state dependent. To show this, we parameterize action potential waveforms in a set of very high temporal resolution (200 kHz) intracellular action potential recordings. We show that an action potential is not a digital '1', but is instead a rich signal whose fine-scale features influence the shape and timing of the next action potential and whose waveform is systematically biased by input drive. We then show that intracellular action potential waveforms can vary as a function of the extracellular local field potential, but do so heterogeneously, as a function of the field potential amplitude and standard deviation. Our results have profound implications for systems and computational neuroscience, especially regarding the development of next-generation, biologically-inspired artificial neural networks that incorporate waveform dynamics. Non-binary action potentials point to a broader landscape of possible neural codes, whereby neurons communicate not just via binary spikes, but through their state-dependent waveform features. ### Competing Interest Statement The authors have declared no competing interest. NIH National Institute of General Medical Sciences, R01GM134363-01
- Mr. Bill repeated this.
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  Cat Hicks (grimalkina@mastodon.social)'s status on Thursday, 24-Sep-2026 00:06:31 JST Cat Hicks
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  ""What’s wild is when people started building ANNs in the 1940s they copied the binary {0,1} template. But essentially all modern AI has abandoned binary neurons because they just don’t learn as well. AI engineers gave up on the (presumed) biology and now modern artificial neurons use (presumably non-biological) smooth, non-linear activation functions. But our work suggests that spikes were never binary to begin with (which is funny because every neurophysiologist is like: yeah, we know!)"
  
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