I really dislike the analogy that neural networks ‘are similar to’ our brains. It isn’t true, nor has it ever been. We have made about 50 years of progress in neuroscience research since the misnormer happened – if they would not have the same name, you would not confuse them in how they work, even by accident.
Yes, originally we knew very little about brains and neurons, and it definitely showed that (at least initially) AI research was aimed at replicating existing biological intelligence. However, it seems that most of the AI research community has shunted anything we learned from progress in neuroscience since. This can be seen e.g. by claiming that biological and artificial neural networks ‘are similar’ in just about every introductory lecture on machine learning – but it only demonstrates disdain for both disciplines.
No, they are not similar. It’s a weird quirk of history that they got named similarly, and we know better now. By not being aware of the differences, you are only demonstrating your own ignorance.
Short List of Stark Differences between Biological and Artificial Neural Networks (ANNs):
- Feedback: In the brain, for every feedforward connection, there are about nine back. So 90% of signals are not ‘feedforward’ but ‘feedback’ (or: predictive. confirmation bias anyone?). In ANNs, most of the time 100% of connections are feedforward
- Sparse Activation: at every moment, only about 5% of columns in the neocortex are active, and even fewer neurons (afaik e.g. the cerebellum with 80% of neurons works differently, though I don’t know much about it). In ANNs, even if a connection is zeroed out, all neuron activations are being calculated (except architectures such as pathways, but even they work differently)
- Plasticity: biological brains are modified (learn) by activation, and have internal state continuously modeling the outer world. ANNs are static in their weights when they’re not actively being trained
- Biologically, connections are part of plasticity, mostly random and adjust themselves when unused. They can cross various layer boundaries (in fact, additional layers can grow over time from the same amount of neurons/columns due to ‘automatic’ optimization, and suppression of redundant activation).
- It has been shown that dendrites have some form of computation / ‘activation function’ as well. They can be primed (to activate / predict to activate), something without analogue in ANNs. ANN connections are static (they exist, or they don’t) and can have weights at best, no additional computation / prediction / activation / modification.
- Activation potentials in biological neurons are much simpler and have timing — there are also ANNs (spiking neural networks) that try to imitate something similar, but with limited success. Timing of firing in brains is actually extremely important and can mess up just about everything, particularly coordination. Ever been drunk? yeah, timings are ever-so-slightly off
At best, ANNs are biologically inspired (by our understanding at the time).