I'm still wandering about a service that would be to arXiv what Github became to Sourceforge. Order of magnitude improvement of collaboration and interconnection between published materials.
I've had the very same feelings playing Elite Dangerous. Of course it's a space sim, not an RPG. But it had this sense of a world living without you. And a ton of mechanics where each one seems boring on its own, but allows you to build your unique complex stories and memories.
But does predictive coding perceived as a valid theory for cortical neurons functioning?
There was a paper from 2017 drawing similar conclusions about backprop approximation with Spike-Timing-Dependent Plasticity: https://arxiv.org/abs/1711.04214
Looks more grounded to current models of neuronal functioning. Nevertheless, it changed nothing in the field of deep learning since then.
Biological neurons don't just emit constant 0...1 float values, they communicate using time sensitive bursts of voltage known as "spike trains". Spiking Neural Networks (SNN) are a closer aproximation of natural networks than typical ML ANNs. [0] gives a quick overview.
Spike-Timing-Dependant-Plasticity is a local learning rule experimentally observed in biological neurons. It's a form of Hebbian learning, aka "Neurons that fire together wire together."
Summary from [1]. The top graph gives a clear picture of how the rule works.
> With STDP, repeated presynaptic spike arrival a few milliseconds before postsynaptic action potentials leads in many synapse types to Long-Term Potentiation (LTP) of the synapses, whereas repeated spike arrival after postsynaptic spikes leads to Long-Term Depression (LTD) of the same synapse.
