5 ms·
My theory is that, instead, the brain is a super-duper complex neural network with some differences about how the update of the weights happen :) Let me explain
by marcyb5st 8y ago
My theory is that, instead, the brain is a super-duper complex neural network with some differences about how the update of the weights happen :) Let me explain:
Albeit true that our brain is much slower than a modern CPU in terms of ops/s, every neuron is connected to up to 10000 other neurons[0]. And we have something around 100 billion neurons in our brains. This means that the number of synapses is between 100 and 1000 trillions.
If we assume that every synapse can be considered like a "weight" in a neural network, we have a model that is much much bigger than anything developed to date. Moreover, the topology of said neural network is not a simple stack of layers, but much more complex like a super intricated residual network.
Finally, the brain is not using backpropagation to update the mentioned weights/synapses' strength, but instead is based on how often said synapses fire.
[0]: http://www.human-memory.net/brain_neurons.html http://www.human-memory.net/brain_neurons.html
- dnadler 8y agoPeople also gloss over the fact that there are multiple neurotransmitters involved.
- skohan 8y ago> If we assume that every synapse can be considered like a "weight" in a neural network, we have a model that is much much bigger than anything developed to date. This is a very bad assumption. Each synapse is a highly complex chemical computer, and its "weight" with regard to its contribution to the postsynaptic neuron's activation at any point in time depends on a multitude of factors. Those factors include dozens of interacting metabolic pathways, the interactions of various neurotransmitter systems which each have distinct behavior, and things like where the individual synapses sit relative to one another on the dendritic arbor. There's even evidence that at least in some neurons the activation pattern may influence RNA production in the neuron, creating a kind of recording which influences the behavior of the cell. Current ANN models should be considered a very low dimensional approximation of the real thing.
- jcranmer 8y ago> My theory is that, instead, the brain is a super-duper complex neural network with some differences about how the update of the weights happen Neural networks were not meant to be accurate analogues to the actual operation of the neurons in the brain. They were known to be poor matches for the actual biology, even back in 1959. The concept of the multilayer neural network was actually inspired from our knowledge of the visual cortex, rather than the lower-level fundamentals of the brain. The brain is most certainly not a neural network, as the term is understood in machine learning.
- skohan 8y ago> The brain is most certainly not a neural network, as the term is understood in machine learning. The brain is most certainly a neural network. It is literally a network of neurons. But you're right insofar that artificial neural networks are not very similar to the brain.
- tptacek 8y agoThat is exactly what the parent comment said.
- skohan 8y agoI was objecting to the statement: "The brain is most certainly not a neural network" - as if to say that the types of artificial neural networks employed in machine learning are somehow the central definition of what a neural network is. I would be happy with "the brain is not an ANN", or "The brain is very different from the types of neural networks used in Machine Learning", but to say the brain is anything other than a neural network is just plain wrong.
- tptacek 8y agoThat's not what they said. They said, "The brain is most certainly not a neural network, as the term is understood in machine learning.".