That preprint seems to be missing every single figure. From the text,
> The key feature of the model neuron is its use of
active dendrites and thousands of synapses, allowing the
neuron to recognize hundreds of unique patterns in large
populations of cells.
"active dendrites" and "thousands of synapses" sounds an awful like abstracting away a complex mathematical model to fit a particular definition of "single neuron".
They're researching the behaviour of real neurons by making computational models, rather than creating a "neural network" model for AI purposes, so the definition of "single neuron" they're using is meant to reflect the original meaning of the term.
But if the hypothesis is that thousands of little local functions can learn to recognize feature vectors, that's been a well-tested assumption in discriminative machine learning models for decades. Is there a biological twist to this finding?
It's mostly interesting biologically - it relates specifically to how synapses' vicinity to the main cell body affects their action, or more generally how the spatial configuration of a neuron's connections affects its computational function.
The gist seems to be that more distant synapses can't initiate an action potential, or firing of the neuron, but can prime the neuron to fire in reaction to synapses closer to the cell body. This means that outer synapses can provide 'context', e.g. indicate that prior steps in a sequence have been recognised, while inner synapses can cause firing if the context is fulfilled, i.e. context + necessary condition (recognition of the most recent step in the sequence) = action potential.
It's not computationally surprising, but it is a specific cellular mechanism.
