In a sense; linear regression can be computed exactly so refers to a specific technique for producing a linear model. Most artificial neurons are trained stoch

In a sense; linear regression can be computed exactly so refers to a specific technique for producing a linear model. Most artificial neurons are trained stochastically rather than holistically, i.e. rather than looking at the entire training set and computing the gradient to minimize the squared loss or something similar, they look at each training example and compute the local gradient and make small changes in that direction. In addition, the "activation function" almost universally used now is the rectified linear unit, which is linear for positive input and zero for negative input. This is non-decreasing at least as a function, but the fact that it is not monotonic means that there is no additional loss accrued for overcorrecting in the negative direction. Given this, using the term "linear regression" to describe the model of an artificial neuron is not really a useful heuristic.