It's pretty meaningful actually. Because biological neurons don't sit alone. There's a third player in every synapse: the astrocyte. And astrocytes form a large syncitium, a collection of cells that are "open" to each other through gap junctions. This syncitium has gotten more extensive over evolution, and human astrocytes (which are larger and more complex) when transplanted in mice, make the mice smarter at maze tasks etc.

So every neural network is actually entertained in an astrocytic syncitium. Every synapse is being "listened to" and modulated by it's astrocytic end foot (which forms the third arm of what we now call the tripartite synapse).

If neurons are about breaking down signals, the astrocytes are the slow, steady integrators that (potentially) can serve as the global workspace that scientists have been theorizing but not yet found.

So there actually is a problem with modern neural networks, in so far as comparisons with human brains go: they're missing the design spec for literally half of it.

Which is fine. Connectionist models come from a time when the height of neuroscience said neurons were the bees knees and glia were just support staff. Today, astrocytes, and other glia, are forcefully back in the conversation. And they don't seem to do anything like neural networks.

But a LLM literally does assign a token from a fixed set of tokens (its vocab) to an input, rinse and repeat, until the stop token.

Classifiers have been giving logits since decades ago.

Sure, but then there's no such thing as a network that isn't a classifier. Every physically computable function that terminates in finite time will map an input to a fixed set of outputs. And it goes against the common usage, where in machine learning we talk about classifiers, regressors, generative models, etc. as different things. They all become classifiers.

The parent commenter was trying to draw some insight from LLMs being classifiers that wouldn't apply equally to everything else.