brain

feedforward neural network

Installation

``sh
$ yarn add @shoki/brain
`

Usage

@shoki/brain makes it simple to set up a neural network.

$3

A Brain is created from a Genome. A Genome represents the "physical" structure of the brain.

You can create a basic 1-1 neuron network like so:

`ts
import { createGenome, mutation, Brain } from "@shoki/brain";

const genome = createGenome();

const inputNeuronIndex = mutation.addNeuron(genome, {
type: "input",
// 1 <-> 1 map from input value to output value
activation: ActivationFunctionType.CONSTANT,
description: "input",
});

const outputNeuronIndex = mutation.addNeuron(genome, {
type: "output",
// 1 <-> 1 map from input value to output value
activation: ActivationFunctionType.CONSTANT,
description: "output",
});

// synapse weight is 1 by default
mutation.addSynapse(genome, {
neuronIn: inputNeuronIndex,
neuronOut: outputNeuronIndex,
weight: 1,
});

const brain = new Brain(genome);

brain.think({
[inputNeuronIndex]: 1,
});

brain.getNeuronValue(outputNeuronIndex); // 1
`

!image of 1-1 network

The input value of 1 has the following journey:

- set to input neuron (type is constant, so it isn't modified)
- passed through synapse (weight is 1, so value is 1 * 1)
- set to output neuron (type is constant again, so it isn't modified)

$3

Activation functions allow you to manipulate a value within a neuron.

Let's see how we can make a neuron convert negative numbers to positive with the absolute activation function.

`ts
import { createGenome, mutation, Brain } from "@shoki/brain";

const genome = createGenome();

const inputNeuronIndex = mutation.addNeuron(genome, {
type: "input",
// 1 <-> 1 map from input value to output value
activation: ActivationFunctionType.CONSTANT,
description: "input",
});

const outputNeuronIndex = mutation.addNeuron(genome, {
type: "output",
// 1 <-> 1 map from input value to output value
activation: ActivationFunctionType.ABSOLUTE,
description: "output",
});

// synapse weight is 1 by default
mutation.addSynapse(genome, {
neuronIn: inputNeuronIndex,
neuronOut: outputNeuronIndex,
weight: 1,
});

const brain = new Brain(genome);

brain.think({
[inputNeuronIndex]: -1,
});

brain.getNeuronValue(outputNeuronIndex); // 1
`

!image of 1-1 network

Here you can see how the absolute activation type turns the negative input of -1 into a positive input of 1.

$3

One neuron can receive inputs from multiple synapses. The only aggregation function available here at the moment is sum.

You can create this simply by binding multiple addSynapse calls to the same output neuron.

!image of 2-1 network

$3

You can create hidden neurons within the network at any point.

Inputs / outputs are only determined by finding neurons which don't have any input synapses, or output synapses, respectively.

To insert a neuron within an existing synapse, you can use insertNeuron.

`ts
mutation.insertNeuron(genome, {
synapseIndex,
neuron: {
description: "hidden",
activation: ActivationFunctionType.ABSOLUTE,
},
});
`

When inserting a neuron within a synapse, the right-hand synapse carries the weight from the replaced synapse, while the left-hand synapse is given a weight of 0`.

!hidden inserted neuron

References

- Efficient Evolution of Neural Network Topologies

_Kenneth O. Stanley and Risto Miikkulainen_

https://nn.cs.utexas.edu/downloads/papers/stanley.cec02.pdf