Tootfinder

What we witnessed was not journalism but the choreography of propaganda.
CBS, in particular, offered thirty uninterrupted minutes of state-sanctioned fantasy,
anchored by a fawning interview with Secretary of War Pete Hegseth,
a man implicated in the killing of more than one hundred people at sea without evidence, accountability, or due process.
Rather than interrogating power, the networks shifted seamlessly into spectacle.
At no point did either network rais…

As Trump Expands Imperial Aggression In Venezuela, Corporate Media Falls in Line
What we are witnessing is fascism unbound, armed with military force and insulated with media complicity.

Multi state neurons
Robert Worden
https://arxiv.org/abs/2512.08815 https://arxiv.org/pdf/2512.08815 https://arxiv.org/html/2512.08815
arXiv:2512.08815v1 Announce Type: new
Abstract: Neurons, as eukaryotic cells, have powerful internal computation capabilities. One neuron can have many distinct states, and brains can use this capability. Processes of neuron growth and maintenance use chemical signalling between cell bodies and synapses, ferrying chemical messengers over microtubules and actin fibres within cells. These processes are computations which, while slower than neural electrical signalling, could allow any neuron to change its state over intervals of seconds or minutes. Based on its state, a single neuron can selectively de-activate some of its synapses, sculpting a dynamic neural net from the static neural connections of the brain. Without this dynamic selection, the static neural networks in brains are too amorphous and dilute to do the computations of neural cognitive models. The use of multi-state neurons in animal brains is illustrated in hierarchical Bayesian object recognition. Multi-state neurons may support a design which is more efficient than two-state neurons, and scales better as object complexity increases. Brains could have evolved to use multi-state neurons. Multi-state neurons could be used in artificial neural networks, to use a kind of non-Hebbian learning which is faster and more focused and controllable than traditional neural net learning. This possibility has not yet been explored in computational models.
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