For example if the neural network has been generating sad music, its current context which is computed from what it has already generated will light-up the the features that correspond to "sad music". And in turn the fact that the features had been lit-up will make it more likely to generate a minor chord.
The dimension of this inner-state is growing at each time-step. And it's quite hard to predict where it will go. For example if you prompt it (or if it prompts itself) "happy music now", the network will switch to generating happy music even if in its current context there is still plenty of "sad music" because after the instruction it will choose to focus only on the recent more merrier music.
Up until recently, I was quite convinced that using a neural network in evaluation mode (aka post training with its weight frozen) was "(morally) safe", but the ability of neural network of performing few-shot learning changed my mind (The Microsoft paper in question : https://arxiv.org/pdf/2212.10559.pdf : "Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers" ).
The idea in this technical paper is that with attention mechanism even in forward computation there is an inner state that is updated following a meta-gradient (aka it's not so different from training). Pushing the reasoning to the extreme would mean that "prompt engineering is all you need" and that even with frozen weight with a long enough time-horizon and correct initial prompt you can bootstrap a consciousness process.
Does "it" feels something ? Probably not yet. But the sequential filtering process that Large Language Models do is damn similar to what I would call a "stream of consciousness". Currently it's more like a markov chain of ideas flowing from idea to the next idea in a natural direction. It's just that the flow of ideas has not yet decided to called itself it yet.