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Personality Basins

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wavemode ◴[] No.42204042[source]
This article approaches human psychology from the perspective that, we are all neural networks and our output (actions) are all a learned function of our inputs (experiences).

This is a common (and convenient) perspective, especially among engineers, but doesn't reflect reality particularly well. We know large swathes of a person's personality is directly linked to their genetics.

The article extrapolates this neural network perspective onto other topics like, mental disorders and depression. The solution is made clear then - just learn how to not be mentally ill! Again, convenient. But not really reflective of reality.

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Balgair ◴[] No.42208594[source]
I've made the jump from physics to neuroscience, so I can talk to the engineers here (I've taken a lot of EE and worked professionally in it too).

The linkages between neurons is somewhat similar to how and RNN looks. But you must remember, there are electrical and chemical elements going on here. It's not just one neuron spiking another. There are many different biochemical processes that modify the behavior of little parts of a neuron, stoichiometrically. And there are many different types of neurons and they all change over time, sometimes drastically so. Most of the goings on is biochem. It's not digital, or even analog. You really need to go down to the field equations at times, finite elements will get you far, but only just so.

RNNs thinking certainly will help you understand better what is going on in a brain, but, like, these things are millions of years old, and optimized just to make more of themselves, not to be understood. It's tough going, and we as a species are only at the very beginning of hundreds of years of study of the brain.

If you'd like to learn more, I can recommend some texts.

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lbeckman314 ◴[] No.42209617[source]
Not the OP but I'd be very interested to hear text recommendations on this!
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1. Balgair ◴[] No.42211541[source]
Bear's Neuroscience: Exploring the Brain

Purves's Neuroscience

Kandel's Principles of Neural Science (grad level, but really the bible for neuro)

Dayan and Abbot's Theoretical Neuroscience (good for compneuro)

The Art of Electronics, 2nd edition (cheating here, but it's good to go back through the fundamentals before going into the edge cases that is neuro)

In general, AI is so new that there really isn't a good classical text between AI thingys and neuro. It will take time to suss one out and write one.