Class: Neuron

Neuron(options)

new Neuron(options)

Neurons are the engines that process and guide information though a neural network. A neural networks "intelligence" is usually measured by the ability for neurons to effectively process information as a group.

What is a neuron?

A Neuron is a simplified mathematical model of a biological neuron.

In people, a neuron is a cell that collects inputs from synapses (i.e. eyes, ears, etc. or other neurons) and triggers an output signal when the incoming signals pass a certain threshold.

In biological neurons (in animals) or in artificial neurons (i.e. AI, NN, Deep Learning, etc.), one neuron doesn’t do much, but when combined, neural networks allow us to recognize the world around us, solve problems, and interact with our environment.

How do they work?

Neural networks were inspired by the human brain, and like in a human brain the basic building block is called a Neuron. Its functionality is similar to a human neuron, i.e. it takes in some inputs and fires an output. In purely mathematical terms, a neuron in the machine learning world is a placeholder for a mathematical function, and its only job is to provide an output by applying the function on the inputs provided.

The function used in a neuron is generally called an "activation function". There have been 5 major activation functions tried to date, step, sigmoid, tanh, and ReLU. For this neuron we are using a "sigmoid" activation function.

What is a "sigmoid activation function"?

A sigmoid function - or logistic function - is defined mathematically as:

The value of the function tends to zero when z tends to negative infinity and tends to 1 when z tends to infinity. A sigmoid activation function is an approximation of how a "real neuron" would behave; it's an assumption in the field of deep learning.

Parameters:
Name Type Description
options Object
Properties
Name Type Attributes Description
id number <optional>
bias number <optional>
optimizer Object
Properties
Name Type Description
rate number
momentum number
decay number
alpha number
beta number
gamma number
Properties:
Name Type Description
id string
bias number
optimizer Object
Properties
Name Type Description
rate number
momentum number
decay number
alpha number
beta number
gamma number
incoming Object
Properties
Name Type Description
targets Object
weights Object
outgoing Object
Properties
Name Type Description
targets Object
weights Object
_output number
output number
error number
Source:
Example
//===============================================
// One Neuron (No Hidden Layers) ================
//===============================================
const { Neuron } = require("@liquidcarrot/nn")

const neuron = new Neuron();

neuron.activate(0); // 0
neuron.propagate(1); // -1

//===============================================
// Three Neurons (Hidden Layers) ================
//===============================================
const { Neuron } = require("@liquidcarrot/nn")

const input = new Neuron(); // Input Neuron (Layer)
const hidden = new Neuron(0.1); // Hidden Neuron (Layer)
const output = new Neuron(0.2); // Output Neuron (Layer)

input.connect(hidden, 0.3); // Connects input layer to hidden layer
hidden.connect(output, 0.4); // Connects hidden layer to output layer

input.activate(0); // 0
hidden.activate(); // 0.52497918747894
output.activate(); // 0.6010858826658407

output.propagate(1); //  -0.09565228299910712
hidden.propagate(); // -0.009900697661026392
input.propagate(); // -0.0029702092983079176

Members

(static) optimizers

Optimizers are initiated with { rate, momentum, decay, alpha, beta, epsilon, gamma }

Source:

Methods

activate(inputopt) → {number}

Parameters:
Name Type Attributes Description
input number <optional>
Source:
Returns:

Returns the neuron's output

Type
number
Example
//===============================================
// One Neuron ===================================
//===============================================
const { Neuron } = require("@liquidcarrot/nn")

const neuron = new Neuron();

neuron.activate(3);

//===============================================
// Two Neurons ==================================
//===============================================
const { Neuron } = require("@liquidcarrot/nn")

const neuron = new Neuron();
const other = new Neuron(0.1);

neuron.connect(other, 0.2);

neuron.activate(3); // 3
other.activate(); // 0.6681877721681662

connect(neuron, weightopt)

Parameters:
Name Type Attributes Description
neuron Neuron
weight number <optional>
Source:
Example
const { Neuron } = require("@liquidcarrot/nn")

const neuron = new Neuron();
const other = new Neuron();

neuron.connect(other);

propagate(target, rateopt) → {number}

Parameters:
Name Type Attributes Default Description
target number
rate number <optional>
 0.3
Source:
Returns:

Returns neuron's marginal error

Type
number
Example
//===============================================
// One Neuron ===================================
//===============================================
const { Neuron } = require("@liquidcarrot/nn")

const neuron = new Neuron();

neuron.activate(3); // 3
neuron.propagate(0); // 3

//===============================================
// Two Neurons ==================================
//===============================================
const { Neuron } = require("@liquidcarrot/nn")

const neuron = new Neuron();
const other = new Neuron(0.1);

neuron.connect(other, 0.2);

neuron.activate(3); // 3
other.activate(); // 0.6681877721681662

other.propagate(0); // 0.14814583086672545
neuron.propagate(); // 0.009876697690471913

weights(arrayopt) → {Object|Array.<Array.<Number>>}

Parameters:
Name Type Attributes Default Description
array boolean <optional>
 false

Iff true, will return an Array ([[INCOMING_WEIGHTS], [OUTGOING_WEIGHTS]]) - instead of a JSON Object ({ incoming: [INCOMING_WEIGHTS], outgoing: [OUTGOING_WEIGHTS])
Source:
Returns:

Returns an Array or Object of incoming and outgoing weights

Type
Object | Array.<Array.<Number>>