So I wrote this code a while ago, but i'm trying to optimize my code. Is there anything that I should definitely change here to save memory/time? The code checks if the neuron is an output or hidden/input neuron, or a constant neuron (the last should stay unaltered).the input is a decimal, decimal and array with decimals.this.error is a dictionary containing error valuesthis.connections contains in, out, and self. Which all are arrays of connectionsconnection.xtrace is a dictionary in the form of {nodes: [], values : []}, nodes are instances and values are decimalsHere is the code:  propagate: function(rate, momentum, target) {    // Error accumulator    var error = 0;    momentum = momentum || 0;    // Output nodes get their error from the enviroment    if (this.type == 'output'){      this.error.responsibility = this.error.projected = target - this.activation;    } else { // the rest of the nodes compute their error responsibilities by backpropagation      // error responsibilities from all the connections projected from this node      for (var connection in this.connections.out) {        var connection = this.connections.out[connection];        var node = connection.to;        // Eq. 21        error += node.error.responsibility * connection.weight * connection.gain;      }      // Projected error responsibility      this.error.projected = this.derivative * error;      // Error responsibilities from all connections gated by this neuron      error = 0;      for(var conn in this.connections.gated){        conn = this.connections.gated[conn];        var node = conn.to;        var influence = node.connections.self.gater == this ? node.old : 0;        influence += conn.weight * conn.from.activation;        error += node.error.responsibility * influence;      }      // Gated error responsibility      this.error.gated = this.derivative * error;      // Error responsibility      this.error.responsibility = this.error.projected + this.error.gated;    }    if(this.type == 'constant') return;    // Learning rate    rate = rate || .3;    // Adjust all the node's incoming connections    for (var connection in this.connections.in) {      var connection = this.connections.in[connection];      var gradient = this.error.projected * connection.elegibility;      for(var i = 0; i < connection.xtrace.nodes.length; i++){        var node = connection.xtrace.nodes[i];        var value = connection.xtrace.values[i];        gradient += node.error.responsibility * value;      }      var deltaWeight = rate * gradient * this.mask + momentum * connection.previousDeltaWeight;      connection.weight += deltaWeight;      connection.previousDeltaWeight = deltaWeight;    }    // Adjust bias    this.bias += rate * this.error.responsibility;  },
Back-propagation code
javascript;neural network