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> The neuronal model used in most artificial intelligence networks contains few synapses and no dendrites. Does anyone understand why this is true? In a typic
by rand_r 9y ago
> The neuronal model used in most artificial intelligence networks contains few synapses and no dendrites.
Does anyone understand why this is true?
In a typical multi-layer network, doesn't each node in a lower layer connect to every node in a higher layer? All the {L(i, n-1), L(a, n)} edges going from the nodes in layer n-1 to to a particular node (a) in layer n would constitute a dendrite.
- ewjordan 9y agoIn this article they're using the word "dendrite" to mean a bit more than just an input to a neuron (which does exist in NN models) - one of the often criticized simplifications of most neural net models is that they don't account for the structure and function of the entire dendritic tree. Specifically, in the image you're referencing in the article they point out that synapses very near the soma directly cause action potentials, whereas further out they will cause some depolarization but not actually cause the cell to fire. The way some authors explain it is that you can think of a neuron as really having an entire neural net embedded inside of it (based on the structure and function of the dendritic tree), that does some non-trivial amount of information processing even before you consider the connectivity of neurons to each other. Exactly how non-trivial that is is a deeper question, but it's worth noting that these structures are extremely plastic, and change over timescales of seconds to minutes, so it's not hard to imagine that these details are significant.
- rand_r 9y agoAh, thanks. So another way of wording it would be to say that the activation function of nodes in a neural network is a gross oversimplification of the amount of logic that happens via dendrites in actual neurons.