neuron.v0.0.4.js

const uid = require("cuid");

function Neuron() {
  this.id = uid(); // ID
  this.bias = bias == undefined ? Math.random() * 2 - 1 : bias; // this.bias ∈ ℝ && -1 < this.bias < 1
  
  // Incoming Connections
  this.incoming = {
    neurons: {}, // new Map()
    weights: {} // new Map()
  }
  // Outgoing Connections
  this.outgoing = {
    neurons: {}, // new Map()
    weights: {} // new Map()
  }

  this._output; // f'(x)
  this.output; // f(x)
  this.error; // E'(f(x))
  
  this.connect = function(neuron, weight) {
    this.outgoing.neurons[neuron.id] = neuron;
    neuron.incoming.neurons[this.id] = this;
    this.outgoing.weights[neuron.id] = neuron.incoming.weights[this.id] = weight == undefined ? Math.random() * 2 - 1 : weight; // weight ∈ ℝ && -1 < weight < 1
  }
  
  this.activate = function(input) {
    const self = this;
    
    function sigmoid(x) { return 1 / (1 + Math.exp(-x)) } // f(x) = 1 / (1 + e^(-x))
    function _sigmoid(x) { return sigmoid(x) * (1 - sigmoid(x)) } // f'(x) = f(x) * (1 - f(x))
    
    // Input Neurons
    if(input) {
      this._output = 1; // f'(x)
      this.output = input; // f(x)
    }
    // Hidden/Output Neurons
    else {
      // Σ (x • w)
      const sum = Object.keys(this.incoming.targets).reduce(function(total, target, index) {
        return total += self.incoming.targets[target].output * self.incoming.weights[target];
      }, this.bias);
      
      this._output = _sigmoid(sum); // f'(x)
      this.output = sigmoid(sum); // f(x)
    }
    
    return this.output;
  }
}

module.exports = Neuron;

+ neuron.v0.0.3.js
+ neuron.activate() - allows for feed-forward neural networks (i.e. forward feeding)