That's what side effects are. Anytime you take psychoactive medications, they're going to mess with a thousand different brain systems that ALSO happen to use the neurotransmitter(s) affected by the drug, but were functioning normally. Thankfully, the brain is usually pretty good at compensating for stuff like that (which is why drug tolerance is a thing). Otherwise, side effects would probably be a LOT worse in general.

But when you say you're worried about schizophrenia, that's still thinking about it wrong. Schizophrenia is not caused by a simple overabundance of dopamine, it's caused by some kind of complex disturbance in the activity of one or more networks of brain circuits. The fact that some of those circuits have some dopaminergic components, and that some of the drugs that can partially alleviate symptoms affect dopamine among other neurotransmitters does not make dopamine central to schizophrenia.

That's exactly right! In the synthesis of dopamine, converting tyrosine to L-DOPA is the slowest step, so having extra L-DOPA available significantly increases dopamine production.

>how come we treat Parkinson's by flooding the brain with dopamine

That's the neat thing! You don't.

You flood the brain with stuff to make dopamine, which lets the surviving SNc neurons release more dopamine, essentially amplifying the signals of the ones that are left. If they all died, L-DOPA treatment wouldn't work. Again, the really important thing is the neurons, what inputs they get, how they process those inputs, and where they send their outputs, not which chemical they are releasing. In fact, most work on realistically simulating the activity of networks of neurons doesn't even bother to simulate neurotransmitters in any way, except to say "when this neuron fires, that one gets excited by +2millivolts after a delay of 2ms" or something to that effect.

So in the valve analogy, it's like we're increasing the water pressure so that the semi-busted toilet valve can still sort of function.

Very much so! If you're curious about neural circuits, check out the app Neuronify.. it's a simple spiking neuron simulator, you can play around with using neurons with different properties to create circuits with multiple stable states, circuits that generate periodic output sequences, and circuits that do specific things in response to an input.

Neuroscience PhD here: Dopamine does different things in different places. Asking why one group of dopamine releasing neurons can't substitute for another is like saying "my toilet valve is broken, but the valve in my shower is fine, so shouldn't my toilet work anyway?"

Dopamine releasing neurons in the substantia nigra and the dopamine releasing neurons in other areas have 1 thing in common, which is the neurotransmitter they use. But what neurotransmitter they use is relatively unimportant to their function. What's far more important is what neurons they get input from, how those inputs are processed, and what neurons they connect to.

Any given group of neurons that releases dopamine can only release dopamine where their axons are, and that dopamine cannot freely diffuse throughout the brain. This is the whole point of synapses: to help confine the spread of neurotransmitters so that neurotransmitters can act as signals from one neuron to another instead of broadcast signals.

If this seems like a significant departure from how you're used to thinking about neurotransmitters, GOOD. Neurotransmitters do not have functions, neurons do. Repeated with emphasis: NEUROTRANSMITTERS DO NOT HAVE FUNCTIONS, NEURONS DO. Two different neurons can use the same neurotransmitter for totally different functions. Dopamine, for example, is also used in the control of lactation, which is totally unrelated to its role in the control of movement.

Well, it's possible, and it certainly sounds like there's reason to suspect some kind of normalization related gotcha.

Maybe the scale of the values, rather than the mean being zero, is the issue?Perhaps a larger or smaller SD would change the outcome? Or a different initialization, especially if using relu. You could also try normalizing the data to have mean 0.1 and SD 1, in case it's some kind of dead relu issue. I'm really spitballing there.

This seems like a good time to use data augmentation. Anytime you think a model is using features of the data that you don't want it to use but that happen to correlate with the desired output, you get in there and make them NOT correlated.

E.g. add / subtract some random value from each image before using it for training, and/or multiply by some random factor, so that the scaling of different images is all over the place and the model can't effectively use that as a shortcut.

This is a sort of minor point, but just to make it clear what you're saying, it might help to define what you mean by power. At the end of your comment you draw a distinction between horsepower and power, so I'm assuming that when you say power, you're not using "power" in the physics sense of the word (amount of work done per unit of time, measured in Watts), since HP is a unit of power (one HP = around 750 Watts). I think, from the body of your comment, that by "power" you mean torque at low RPM, or maybe the minimum amount of torque available across the entire RPM range.

I'm not trying to disagree with anything you're saying (I'm nowhere near knowledgeable enough), just pointing out that the terminology used by people with automotive expertise may differ from the terminology commonly used in other areas (sciences, some fields of engineering) in a way that causes some confusion.

I'm also curious, what would you say is the reason why cars with lots of low-RPM torque were so popular? How much do factors like being able to produce very high HP or large amounts of torque actually affect people who just drive their cars to work / the grocery store?

We can do (and have done) experiments to determine how well they can distinguish one color stimulus from another, and they perform worse than humans (and worse than other animals with true color vision).

The key piece of information that's often left out is that their color vision is actually terrible.

Picture a movie playing in color. Now picture three black-and-white movies playing side by side: one showing the red channel, one showing the green channel, one showing the blue channel. Same information, but there's a lot of stuff your brain can't actually SEE if the information isn't combined the right way.

The mantis shrimp doesn't combine information across color channels the way we do, for a simple reason: its expensive. It takes a lot of extra neurons to combine different cone inputs in a way that lets you see what we know as color, and neurons cost calories.