Question

Difference between artificial neural network and biological neural network

Answer 1

Artificial neural networks (ANNs) are mathematical constructs, originally designed to approximate biological neurons. Each "neuron" is a relatively simple element --- for example, summing its inputs and applying a threshold to the result, to determine the output of that "neuron".

Several decades of research went into discovering how to build network architectures using these mathematical constructs, and how to automatically set the weighting on each of the connections between the neurons to perform a wide range of tasks. For example, ANNs can do things like recognition of hand-written digits.

A "biological neural network" would refer to any group of connected biological nerve cells. Your brain is a biological neural network, so is a number of neurons grown together in a dish so that they form synaptic connections. The term "biological neural network" is not very precise; it doesn't define a particular biological structure.

In the same way, an ANN can mean any of a large number of mathematical "neuron"-like constructs, connected in any of a large number of ways, to perform any of a large number of tasks.

Answer 2

Artificial Neural network: Artificial neural network are computing systems inspired by the biological neural network. It is one of the tool use in the machine learning, and based on structure and functions of biological neural network. Artificial neural networks are considered non-linear statistical data modelling tools where the complex relationships between input and outputs are modeled or patterns are found. Artificial neural network have three layers that are interconnected. The first layer consists of input neurons. These neurons send data to second layer which in turn sends the output neurons to the third layer.

Biological neuron system: The neural system of the human body consist of three stages - receptors, a neural network and effectors. The receptors receive the stimuli either internally or from the external world, then pass the information into neurons in a form of electrical impulses. The neural network then  processes the inputs then  makes proper decision of outputs.