Difference between biological neuron and artificial neuron
Artificial vs. Biological. The main motivation behind Artificial Neural Networks (ANNs for short) is to mimic how Biological Neural Networks (BNNs for short), specifically the Human s’ BNNs, they work.
rs are driven by to the goal of “unlocking the Human intelligence”. As a general rough idea, an ANN is mathematical abstraction for the computational model that is actually happening in our BNNs and being developed by nature. Similarly to BNNs, artificial neurons in an ANN are designed to be connected to each others, gather input signals from the neurons they are connected to their left side (let’s say), and pass an output signal to the neurons connected to their right side (let’s say) based on a firing pattern defined to that particular artificial neuron.
Do we know how to wire these artificial neurons properly to have an ANN architecture that is equivalent to the BNN’s? Do we know the appropriate firing pattern to define at the neuron level? Do today’s ANNs behave the same as BNNs? Absolutely, not! “The human brain has 100 billion neurons, each neuron connected to 10 thousand other neurons. Sitting on your shoulders is the most complicated object in the known universe” — Michio Kaku. ANNs is a research effort to approximate BNN’s. Is Neuroscience the way to A.I.? This is the most controversial question that you can ever come across in the ANN research field from my perspective.