Neural networks
Is a neural network like the brain?
The name comes from a loose inspiration: artificial neurons were modeled, very roughly, on brain cells. But the resemblance is shallow, and researchers caution against reading a working brain into a neural network.
The word “neural” is where most of the confusion starts. The name is honest about its origin: an artificial network is made of units that “loosely model the neurons in the brain.”[1] The key word is loosely. A biological neuron is a living cell that does a great deal of its own processing. An artificial neuron multiplies some numbers, adds them up, and applies one function. The shared idea is only that a signal comes in, gets processed, and a signal goes out.
It is tempting to go further and treat a trained network as a small brain, but the research warns against that. An MIT study found that neural networks reproduced a known brain-like activity pattern only when researchers deliberately imposed assumptions that were, in their words, “inconsistent with the biology.” As one of the authors put it, “if the researchers hadn’t already known of the existence of grid cells, and guided the model to produce them, it would be very unlikely for them to appear as a natural consequence.”[2]
So the analogy is useful for one thing: it explains why these systems are called neural networks. It is not evidence that a neural network thinks, understands, or works the way a brain does. Treat the name as a label for a math structure, and you will read the rest of this subject more clearly.
References
- Neural network (machine learning) — Wikipedia
- Study urges caution when comparing neural networks to the brain — MIT News