# Artificial Neuron Models

Computational neurobiologists have constructed very elaborate computer
models of neurons in order to run detailed simulations of particular
circuits in the brain. As Computer Scientists, we are more interested
in the general properties of neural networks, independent of how they
are actually "implemented" in the brain. This means that we can use
much simpler, abstract "neurons", which (hopefully) capture the essence
of neural computation even if they leave out much of the details of
how biological neurons work.

People have implemented model neurons in hardware as electronic circuits,
often integrated on VLSI chips. Remember though that computers run much
faster than brains - we can therefore run fairly large networks of simple
model neurons as software simulations in reasonable time. This has
obvious advantages over having to use special "neural" computer hardware.

## A Simple Artificial Neuron

Our basic computational element (model neuron) is often called a
**node** or **unit**. It receives input from some other units,
or perhaps from an external source. Each input has an associated
**weight** *w*, which can be modified so as to model synaptic
learning. The unit computes some function *f* of the weighted sum
of its inputs:

Its output, in turn, can serve as input to other units.

- The weighted sum is called the
**net input** to unit
*i*, often written *net*_{i}.
- Note that w
_{ij} refers to the weight from
unit *j* to unit *i* (not the other way around).
- The function
*f*
is the unit's **activation function**. In the simplest case, *f*
is the identity function, and the unit's output is just its net input.
This is called a **linear unit**.

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