From what I’ve read about brain neural network and artificial neural network (ANN), I think that the first ANN was definitely inspired by the how the brain neural network functions, but as ML progresses, the ML implementation begins to diverge significantly from how our brain works, and the link between gradually weakens.
I’ll start with the similarities between our brain’s neural network and ANN. Their structures are similar in some ways. They both have their organizations to receive input, for the brain is the dendrites and the ANN the incoming connections. Then the soma in our brain’s neuron “sum up” inputs from previous steps and make a non-linear decision whether to activate the neuron and fire an output, in ANN the operation is called “the linear calculation and the activation functions”. Finally, the axon in the neuron carries the action signal and delivers it to the next layer, the output connections in ANN perform similar tasks. Also, when discussing CNN (convolutional neural networks), it is inspired by the visual pathway: the representation of the images is similar to that in the visual cortex, it starts from studying simple patterns to more complex shapes. Another thing that relates ANN with the brain is plasticity, the experiences we have strengthen or weaken the existing connection in our brains, and therefore affects our memory, ANN, too, iteratively modifies the weights of the network parameters based on inputs.
However, there are significant differences between them, our brain neural network is way more powerful and efficient than ANN and ML nowadays. Brains neurons are much stronger than artificial neurons in terms of numbers and internal complexity, chemical and electric mechanisms are more nuanced and robust in neurons compared to the artificial ones, a single brain cell can complete complex (multiple) composite tasks whereas an artificial neuron can only perform one task at a time. The components of ANN is parameters, weights, the liner and activation functions are basic and crude compared to the functioning of the brain network. The overall network architecture also varies significantly. For ANN, each layer is only connected to the previous and the next layers while for the network of neurons, there are tens of thousands of dendrites crossing “layers” and regions in numerous directions. Human brains don’t use backpropagation that leverages the chain rule over partial derivatives of an error function.
Nevertheless, the brain neural network continuously inspires the innovation of ML and AI. Power efficiency is what our brain exceeds ANN the most, the effort our brain takes performing the same task with an AI system is far less than what the latter needs. Expecting to train models with a smaller set of training examples is also a goal, currently some “built-in” models that allow “intuitive” understanding which simplifies the training process. When the brain is building a connection, it is always through unsupervised processes, while the AI we have today only perform supervised ones, if we successfully create an AI system that can train itself unsupervised, it will be more intelligent and human-like.