The receiving neuron adds up all of these input signals to see if the sum is greater than its theshhold value. If the threshhold value is reached, then the neuron fires and sends out a signal of its own to all of the other neurons, which they will then add to their input sums. This threshhold behavior is similar to the way a household thermostat works. In the summer, is the house begins to heat and the indoor temperature reaches the threshhold value that was set on the thermostat, then the air conditioner will come on until the temperature falls below the theshhold value. The artificial neurons that my research is based on greatly simplify the many components that make up real, biological neurons.
Real neurons can have hundreds or thousands of input and output connections to other neurons.
To be practical, artificial neurons are generally limited to dozens of connections or less. My work deals with the behavior of a network of either two or three neurons. A Hopfield neuron is a type of artificial neuron that is named after the man who came up with the model. He came up with a way of describing the behavior of a neuron with a relatively simple mathematical equation. In my research, I use a computer to run neural simulations based on these Hopfield equations.