Artificial Neural Networks

Artificial neural networks (ANNs) are machine learning models built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. ANNs are based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain.

An artificial neuron receives signals, processes them, and can signal neurons connected to it. The output of each neuron is computed by some non-linear function of the sum of its inputs.

The connections between artificial neurons are called edges. These connections, similar to synapses in a biological brain, transmit signals to other neurons. The purpose of the connections is to transmit information and influence the output of connected neurons.

<p>Neurons and edges in artificial neural networks typically have weights that adjust as learning proceeds. These weights increase or decrease the strength of the signal at a connection, thereby influencing the overall behavior and output of the neural network.</p> Signup and view all the answers

<p>Neurons in artificial neural networks may have thresholds that determine whether a signal is sent or not. If the sum of the weighted inputs to a neuron exceeds its threshold, a signal is sent to connected neurons. Thresholds help control the activation and firing of neurons in the network.</p> Signup and view all the answers