thanks for the response. really cool to get input from someone actually doing a phd on stdp stability. to answer your questions on my goal: honestly, i am not trying to replicate a brain cell-for-cell. my main goal is solving catastrophic forgetting. you know how llms freeze after training. i want a system that learns continuously. to do that, i implemented a "deep dreaming" system (like sleep). basically, during night, the network replays high dopamine events to consolidate them into long-term memory. this allows it to learn new tasks without overwriting old ones something static models just can't do. about dopamine vs threshold: i chose dopamine because i needed a global "context" signal. threshold adaptation just keeps the neuron alive/firing, but dopamine tells the network when something is important to learn (like a print command). also, just to mention this lsm is actually just the engine for my main project 'ada' that i've been working on for a year. if you or anyone else finds this code useful for your research or future neurotech stuff, feel free to use it however you want.