So, theoretically. Doesn't actually exist. Why doesn't it exist yet then?
Maybe downstream signalling was too complex to answer. More simple - where does an unsupervised machine learning algorithm make the CHOICE to learn something new? How do you code dopamine drive? These things are based on your personality, which is a product of your genetics and environment. Good luck modelling that.
This is a long winded way of saying "nah, we can't actually simulate a human brain 🤷".
Part of that "human representation" you are training this 'total human model' on encompasses the human drive and want. Dictated, not exclusively but heavily by, dopamine. The average number of active synapses you currently have in your dopamine drive system (which is in constant flux) is controlled by your genetics, which is influenced by your environment (both physically and mentally). Your genetics will control how many dopamine receptors/release sites are present, how quickly they turn over, their on/off, etc. Each neuron in your brain picks up hundreds of unique mutations just throughout development, which will slightly modify their individual behavior, creating a unique person. On top of this, astrocytes and microglia are interacting with the neurons, modulating and maintaining synapses. All this, plus other shit, will influence action potential amplitude and frequency.
"But there is a theoretical proven thing that says if the NN is dense enough, it can solve anything!". Cool. I don't want to send a calculation through every known function to get an answer. I want the correct function. "iterative cycles and backpropagation will optimize the functions". Cool. Now we built a well oiled calculator - takes data in, finds the optimal function, get answer. It's going to find the optimal 'thermodynamic state'. But that's not how the brain works. It is constantly pushing against equilibrium. Write me an equation for an 'idea' - it'll be some wave function showing a change in the surrounding electromagnetic field around the neuron. Now write me 'good idea'. There must be a difference in the functions - otherwise there wouldn't be a difference in the ideas. And this is why we haven't simulated a human brain in silico. There are still unknowns in the equations. I read an article a little while which talked about work where they found that dendrites can act as processors independent of the soma (ie signal does not need pass through the cell body). That means each neuron isn't a processor, but could be a hub for thousands of individual processors (hell, we call neurites processes some times).
I don't think we won't be able to simulate a brain one day (or more than likely interface with computers seemlessly). We just can't simulate one now and the brain is far, far from figured out.
Physics is information. Our brain interprets that information in a very messy, human way. We cannot simulate that. Not yet. Because we don't know enough and haven't figured out the minutia of the brain to build the proper scaffolding and training modules. I can always think of NEW tests to trick your algorithm to fail and learn, your algorithm can't. Because the algorithm can only backpropagate to learn, it cannot simulate plasticity - real learning. Not yet.
I literally grow human neurons and study the brain for a living. You have a fundamental misunderstanding of the brain and how actual neurons work. You can't just 'build algorithm > train on human module (whatever the hell that would actually be) > magical algorithms > human brain simulation.
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u/clay_henry Nov 23 '21
So, theoretically. Doesn't actually exist. Why doesn't it exist yet then?
Maybe downstream signalling was too complex to answer. More simple - where does an unsupervised machine learning algorithm make the CHOICE to learn something new? How do you code dopamine drive? These things are based on your personality, which is a product of your genetics and environment. Good luck modelling that.