Ideal Neuron Interface

[luiscarbonell]

Ideal Neuron Interface

Properties

Key	Description
bias	Bias
rate	Learning rate
activation	Activation/squash function
connections	All neuron connections
connections.incoming	Incoming neuron connections
connections.outgoing	Outgoing neuron connections

Instance Functions

Key	Description
Neuron.prototype.is.input()	Tests whether neuron has no input connections
Neuron.prototype.is.output()	Tests whether neuron has no output connections
Neuron.prototype.project()	Connects to another neuron, layer, or group
Neuron.prototype.activate()	Activates neuron
Neuron.prototype.propagate()	Updates neuron; propagates error

Class Constants

Key	Description
Neuron.activation	An object of typical activation/squash functions
Neuron.activation.SIGMOID	sigmoid Squash Function
Neuron.activation.ReLU	ReLU Squash Function
Neuron.activation.TANH	tanh Squash Function
Neuron.activation.IDENTITY	identity Squash Function
Neuron.activation.PERCEPTRON	perceptron Squash Function
Neuron.update	An object of typical activation/squash function derivatives
Neuron.activation.SIGMOID	sigmoid Squash Function Partial Derivative
Neuron.activation.ReLU	ReLU Squash Function Partial Derivative
Neuron.activation.TANH	tanh Squash Function Partial Derivative
Neuron.activation.IDENTITY	identity Squash Function Partial Derivative
Neuron.activation.PERCEPTRON	perceptron Squash Function Partial Derivative

new Neuron()

• new Neuron(): Creates a new neuron.
• new Neuron({ inputs: [n0, n1], outputs: [n2] }): Creates a new neuron with n0 and n1 as incoming connections, and n2 as an outgoing connection.
• new Neuron(n0): Creates a new neuron with the same connections as n0

.is.input([callback])

• neuron.is.input(): Returns true if neuron has no incoming connections

.is.output([callback])

• neuron.is.output(): Returns true if neuron has no outgoing connections

.project(object[, callback])

• neuron.projects(other_neuron): Projects neuron to other_neuron
• neuron.project(layer): Projects neuron to every neuron in layer
• neuron.project(group): Projects neuron to every neuron in group

.activate(inputs[,callback])

• .activate([0, 1, 0, 1]): Activates neuron with the given inputs; inputs.length must equal connections.length

.propagate(feedback[,callback])

• .propagate([1, 0, 1, 0]): Updates weights, and propagates errors.

[luiscarbonell]

Supporting Forward and Backwards Passes

The following helps abstract away the particular activation function or algorithm for backpropagation.

let Neuron = (...) => {
  activate,
  learn,
  ...
  forward(...) {
    activate()
  },
  backward(...) {
    learn()
  }
}

[luiscarbonell]

Static Activation Functions

Having class-wide activation function will help create neurons quickly.

• Neuron.activation.SIGMOID
• Neuron.activation.ReLU
• Neuron.activation.TANH
• Neuron.activation.IDENTITY
• Neuron.activation.PERCEPTRON

CHECK: https://abdulapopoola.com/2013/03/30/static-and-instance-methods-in-javascript/

[luiscarbonell]

Callback Invocation

Documentation does not show what callback is invoked with.

That would be useful.

Check async for a great example.