synaptic/src/Neuron.js

architecture-free neural network library

import Connection, {connections} from './Connection';

let neurons = 0;

// squashing functions
const squash = {
// eq. 5 & 5'
  LOGISTIC: function (x, derivate) {
    var fx = 1 / (1 + Math.exp(-x));
    if (!derivate)
      return fx;
    return fx * (1 - fx);
  },
  TANH: function (x, derivate) {
    if (derivate)
      return 1 - Math.pow(Math.tanh(x), 2);
    return Math.tanh(x);
  },
  IDENTITY: function (x, derivate) {
    return derivate ? 1 : x;
  },
  HLIM: function (x, derivate) {
    return derivate ? 1 : x > 0 ? 1 : 0;
  },
  RELU: function (x, derivate) {
    if (derivate)
      return x > 0 ? 1 : 0;
    return x > 0 ? x : 0;
  }
};


export default class Neuron {
  static squash = squash;

  constructor() {
    this.ID = Neuron.uid();

    this.connections = {
      inputs: {},
      projected: {},
      gated: {}
    };
    this.error = {
      responsibility: 0,
      projected: 0,
      gated: 0
    };
    this.trace = {
      elegibility: {},
      extended: {},
      influences: {}
    };
    this.state = 0;
    this.old = 0;
    this.activation = 0;
    this.selfconnection = new Connection(this, this, 0); // weight = 0 -> not connected
    this.squash = Neuron.squash.LOGISTIC;
    this.neighboors = {};
    this.bias = Math.random() * .2 - .1;
  }

  // activate the neuron
  activate(input) {
    // activation from enviroment (for input neurons)
    if (typeof input != 'undefined') {
      this.activation = input;
      this.derivative = 0;
      this.bias = 0;
      return this.activation;
    }

// old state
    this.old = this.state;

// eq. 15
    this.state = this.selfconnection.gain * this.selfconnection.weight *
      this.state + this.bias;

    for (var i in this.connections.inputs) {
      var input = this.connections.inputs[i];
      this.state += input.from.activation * input.weight * input.gain;
    }

// eq. 16
    this.activation = this.squash(this.state);

// f'(s)
    this.derivative = this.squash(this.state, true);

// update traces
    var influences = [];
    for (var id in this.trace.extended) {
      // extended elegibility trace
      var neuron = this.neighboors[id];

      // if gated neuron's selfconnection is gated by this unit, the influence keeps track of the neuron's old state
      var influence = neuron.selfconnection.gater == this ? neuron.old : 0;

      // index runs over all the incoming connections to the gated neuron that are gated by this unit
      for (var incoming in this.trace.influences[neuron.ID]) { // captures the effect that has an input connection to this unit, on a neuron that is gated by this unit
        influence += this.trace.influences[neuron.ID][incoming].weight *
          this.trace.influences[neuron.ID][incoming].from.activation;
      }
      influences[neuron.ID] = influence;
    }

    for (var i in this.connections.inputs) {
      var input = this.connections.inputs[i];

      // elegibility trace - Eq. 17
      this.trace.elegibility[input.ID] = this.selfconnection.gain * this.selfconnection
        .weight * this.trace.elegibility[input.ID] + input.gain * input.from
        .activation;

      for (var id in this.trace.extended) {
        // extended elegibility trace
        var xtrace = this.trace.extended[id];
        var neuron = this.neighboors[id];
        var influence = influences[neuron.ID];

        // eq. 18
        xtrace[input.ID] = neuron.selfconnection.gain * neuron.selfconnection
          .weight * xtrace[input.ID] + this.derivative * this.trace.elegibility[
          input.ID] * influence;
      }
    }

//  update gated connection's gains
    for (var connection in this.connections.gated) {
      this.connections.gated[connection].gain = this.activation;
    }

    return this.activation;
  }

// back-propagate the error
  propagate(rate, target) {
    // error accumulator
    var error = 0;

    // whether or not this neuron is in the output layer
    var isOutput = typeof target != 'undefined';

    // output neurons get their error from the enviroment
    if (isOutput)
      this.error.responsibility = this.error.projected = target - this.activation; // Eq. 10

    else // the rest of the neuron compute their error responsibilities by backpropagation
    {
      // error responsibilities from all the connections projected from this neuron
      for (var id in this.connections.projected) {
        var connection = this.connections.projected[id];
        var neuron = connection.to;
        // Eq. 21
        error += neuron.error.responsibility * connection.gain * connection.weight;
      }

      // projected error responsibility
      this.error.projected = this.derivative * error;

      error = 0;
      // error responsibilities from all the connections gated by this neuron
      for (var id in this.trace.extended) {
        var neuron = this.neighboors[id]; // gated neuron
        var influence = neuron.selfconnection.gater == this ? neuron.old : 0; // if gated neuron's selfconnection is gated by this neuron

        // index runs over all the connections to the gated neuron that are gated by this neuron
        for (var input in this.trace.influences[id]) { // captures the effect that the input connection of this neuron have, on a neuron which its input/s is/are gated by this neuron
          influence += this.trace.influences[id][input].weight * this.trace.influences[
            neuron.ID][input].from.activation;
        }
        // eq. 22
        error += neuron.error.responsibility * influence;
      }

      // gated error responsibility
      this.error.gated = this.derivative * error;

      // error responsibility - Eq. 23
      this.error.responsibility = this.error.projected + this.error.gated;
    }

    // learning rate
    rate = rate || .1;

    // adjust all the neuron's incoming connections
    for (var id in this.connections.inputs) {
      var input = this.connections.inputs[id];

      // Eq. 24
      var gradient = this.error.projected * this.trace.elegibility[input.ID];
      for (var id in this.trace.extended) {
        var neuron = this.neighboors[id];
        gradient += neuron.error.responsibility * this.trace.extended[
          neuron.ID][input.ID];
      }
      input.weight += rate * gradient; // adjust weights - aka learn
    }

    // adjust bias
    this.bias += rate * this.error.responsibility;
  }

  project(neuron, weight) {
    // self-connection
    if (neuron == this) {
      this.selfconnection.weight = 1;
      return this.selfconnection;
    }

    // check if connection already exists
    var connected = this.connected(neuron);
    if (connected && connected.type == 'projected') {
      // update connection
      if (typeof weight != 'undefined')
        connected.connection.weight = weight;
      // return existing connection
      return connected.connection;
    } else {
      // create a new connection
      var connection = new Connection(this, neuron, weight);
    }

    // reference all the connections and traces
    this.connections.projected[connection.ID] = connection;
    this.neighboors[neuron.ID] = neuron;
    neuron.connections.inputs[connection.ID] = connection;
    neuron.trace.elegibility[connection.ID] = 0;

    for (var id in neuron.trace.extended) {
      var trace = neuron.trace.extended[id];
      trace[connection.ID] = 0;
    }

    return connection;
  }

  gate(connection) {
    // add connection to gated list
    this.connections.gated[connection.ID] = connection;

    var neuron = connection.to;
    if (!(neuron.ID in this.trace.extended)) {
      // extended trace
      this.neighboors[neuron.ID] = neuron;
      var xtrace = this.trace.extended[neuron.ID] = {};
      for (var id in this.connections.inputs) {
        var input = this.connections.inputs[id];
        xtrace[input.ID] = 0;
      }
    }

    // keep track
    if (neuron.ID in this.trace.influences)
      this.trace.influences[neuron.ID].push(connection);
    else
      this.trace.influences[neuron.ID] = [connection];

    // set gater
    connection.gater = this;
  }

// returns true or false whether the neuron is self-connected or not
  selfconnected() {
    return this.selfconnection.weight !== 0;
  }

// returns true or false whether the neuron is connected to another neuron (parameter)
  connected(neuron) {
    var result = {
      type: null,
      connection: false
    };

    if (this == neuron) {
      if (this.selfconnected()) {
        result.type = 'selfconnection';
        result.connection = this.selfconnection;
        return result;
      } else
        return false;
    }

    for (var type in this.connections) {
      for (var connection in this.connections[type]) {
        var connection = this.connections[type][connection];
        if (connection.to == neuron) {
          result.type = type;
          result.connection = connection;
          return result;
        } else if (connection.from == neuron) {
          result.type = type;
          result.connection = connection;
          return result;
        }
      }
    }

    return false;
  }

// clears all the traces (the neuron forgets it's context, but the connections remain intact)
  clear() {
    for (var trace in this.trace.elegibility) {
      this.trace.elegibility[trace] = 0;
    }

    for (var trace in this.trace.extended) {
      for (var extended in this.trace.extended[trace]) {
        this.trace.extended[trace][extended] = 0;
      }
    }

    this.error.responsibility = this.error.projected = this.error.gated = 0;
  }

// all the connections are randomized and the traces are cleared
  reset() {
    this.clear();

    for (var type in this.connections) {
      for (var connection in this.connections[type]) {
        this.connections[type][connection].weight = Math.random() * .2 - .1;
      }
    }

    this.bias = Math.random() * .2 - .1;
    this.old = this.state = this.activation = 0;
  }

// hardcodes the behaviour of the neuron into an optimized function
  optimize(optimized, layer) {

    optimized = optimized || {};
    var store_activation = [];
    var store_trace = [];
    var store_propagation = [];
    var varID = optimized.memory || 0;
    var neurons = optimized.neurons || 1;
    var inputs = optimized.inputs || [];
    var targets = optimized.targets || [];
    var outputs = optimized.outputs || [];
    var variables = optimized.variables || {};
    var activation_sentences = optimized.activation_sentences || [];
    var trace_sentences = optimized.trace_sentences || [];
    var propagation_sentences = optimized.propagation_sentences || [];
    var layers = optimized.layers || {__count: 0, __neuron: 0};

    // allocate sentences
    var allocate = function (store) {
      var allocated = layer in layers && store[layers.__count];
      if (!allocated) {
        layers.__count = store.push([]) - 1;
        layers[layer] = layers.__count;
      }
    };
    allocate(activation_sentences);
    allocate(trace_sentences);
    allocate(propagation_sentences);
    var currentLayer = layers.__count;

    // get/reserve space in memory by creating a unique ID for a variablel
    var getVar = function () {
      var args = Array.prototype.slice.call(arguments);

      if (args.length == 1) {
        if (args[0] == 'target') {
          var id = 'target_' + targets.length;
          targets.push(varID);
        } else
          var id = args[0];
        if (id in variables)
          return variables[id];
        return variables[id] = {
          value: 0,
          id: varID++
        };
      } else {
        var extended = args.length > 2;
        if (extended)
          var value = args.pop();

        var unit = args.shift();
        var prop = args.pop();

        if (!extended)
          var value = unit[prop];

        var id = prop + '_';
        for (var i = 0; i < args.length; i++)
          id += args[i] + '_';
        id += unit.ID;
        if (id in variables)
          return variables[id];

        return variables[id] = {
          value: value,
          id: varID++
        };
      }
    };

    // build sentence
    var buildSentence = function () {
      var args = Array.prototype.slice.call(arguments);
      var store = args.pop();
      var sentence = '';
      for (var i = 0; i < args.length; i++)
        if (typeof args[i] == 'string')
          sentence += args[i];
        else
          sentence += 'F[' + args[i].id + ']';

      store.push(sentence + ';');
    };

    // helper to check if an object is empty
    var isEmpty = function (obj) {
      for (var prop in obj) {
        if (obj.hasOwnProperty(prop))
          return false;
      }
      return true;
    };

    // characteristics of the neuron
    var noProjections = isEmpty(this.connections.projected);
    var noGates = isEmpty(this.connections.gated);
    var isInput = layer == 'input' ? true : isEmpty(this.connections.inputs);
    var isOutput = layer == 'output' ? true : noProjections && noGates;

    // optimize neuron's behaviour
    var rate = getVar('rate');
    var activation = getVar(this, 'activation');
    if (isInput)
      inputs.push(activation.id);
    else {
      activation_sentences[currentLayer].push(store_activation);
      trace_sentences[currentLayer].push(store_trace);
      propagation_sentences[currentLayer].push(store_propagation);
      var old = getVar(this, 'old');
      var state = getVar(this, 'state');
      var bias = getVar(this, 'bias');
      if (this.selfconnection.gater)
        var self_gain = getVar(this.selfconnection, 'gain');
      if (this.selfconnected())
        var self_weight = getVar(this.selfconnection, 'weight');
      buildSentence(old, ' = ', state, store_activation);
      if (this.selfconnected())
        if (this.selfconnection.gater)
          buildSentence(state, ' = ', self_gain, ' * ', self_weight, ' * ',
            state, ' + ', bias, store_activation);
        else
          buildSentence(state, ' = ', self_weight, ' * ', state, ' + ',
            bias, store_activation);
      else
        buildSentence(state, ' = ', bias, store_activation);
      for (var i in this.connections.inputs) {
        var input = this.connections.inputs[i];
        var input_activation = getVar(input.from, 'activation');
        var input_weight = getVar(input, 'weight');
        if (input.gater)
          var input_gain = getVar(input, 'gain');
        if (this.connections.inputs[i].gater)
          buildSentence(state, ' += ', input_activation, ' * ',
            input_weight, ' * ', input_gain, store_activation);
        else
          buildSentence(state, ' += ', input_activation, ' * ',
            input_weight, store_activation);
      }
      var derivative = getVar(this, 'derivative');
      switch (this.squash) {
        case Neuron.squash.LOGISTIC:
          buildSentence(activation, ' = (1 / (1 + Math.exp(-', state, ')))',
            store_activation);
          buildSentence(derivative, ' = ', activation, ' * (1 - ',
            activation, ')', store_activation);
          break;
        case Neuron.squash.TANH:
          var eP = getVar('aux');
          var eN = getVar('aux_2');
          buildSentence(eP, ' = Math.exp(', state, ')', store_activation);
          buildSentence(eN, ' = 1 / ', eP, store_activation);
          buildSentence(activation, ' = (', eP, ' - ', eN, ') / (', eP, ' + ', eN, ')', store_activation);
          buildSentence(derivative, ' = 1 - (', activation, ' * ', activation, ')', store_activation);
          break;
        case Neuron.squash.IDENTITY:
          buildSentence(activation, ' = ', state, store_activation);
          buildSentence(derivative, ' = 1', store_activation);
          break;
        case Neuron.squash.HLIM:
          buildSentence(activation, ' = +(', state, ' > 0)', store_activation);
          buildSentence(derivative, ' = 1', store_activation);
        case Neuron.squash.RELU:
          buildSentence(activation, ' = ', state, ' > 0 ? ', state, ' : 0', store_activation);
          buildSentence(derivative, ' = ', state, ' > 0 ? 1 : 0', store_activation);
          break;
      }

      for (var id in this.trace.extended) {
        // calculate extended elegibility traces in advance
        var neuron = this.neighboors[id];
        var influence = getVar('influences[' + neuron.ID + ']');
        var neuron_old = getVar(neuron, 'old');
        var initialized = false;
        if (neuron.selfconnection.gater == this) {
          buildSentence(influence, ' = ', neuron_old, store_trace);
          initialized = true;
        }
        for (var incoming in this.trace.influences[neuron.ID]) {
          var incoming_weight = getVar(this.trace.influences[neuron.ID]
            [incoming], 'weight');
          var incoming_activation = getVar(this.trace.influences[neuron.ID]
            [incoming].from, 'activation');

          if (initialized)
            buildSentence(influence, ' += ', incoming_weight, ' * ', incoming_activation, store_trace);
          else {
            buildSentence(influence, ' = ', incoming_weight, ' * ', incoming_activation, store_trace);
            initialized = true;
          }
        }
      }

      for (var i in this.connections.inputs) {
        var input = this.connections.inputs[i];
        if (input.gater)
          var input_gain = getVar(input, 'gain');
        var input_activation = getVar(input.from, 'activation');
        var trace = getVar(this, 'trace', 'elegibility', input.ID, this.trace
          .elegibility[input.ID]);
        if (this.selfconnected()) {
          if (this.selfconnection.gater) {
            if (input.gater)
              buildSentence(trace, ' = ', self_gain, ' * ', self_weight,
                ' * ', trace, ' + ', input_gain, ' * ', input_activation,
                store_trace);
            else
              buildSentence(trace, ' = ', self_gain, ' * ', self_weight,
                ' * ', trace, ' + ', input_activation, store_trace);
          } else {
            if (input.gater)
              buildSentence(trace, ' = ', self_weight, ' * ', trace, ' + ',
                input_gain, ' * ', input_activation, store_trace);
            else
              buildSentence(trace, ' = ', self_weight, ' * ', trace, ' + ',
                input_activation, store_trace);
          }
        } else {
          if (input.gater)
            buildSentence(trace, ' = ', input_gain, ' * ', input_activation,
              store_trace);
          else
            buildSentence(trace, ' = ', input_activation, store_trace);
        }
        for (var id in this.trace.extended) {
          // extended elegibility trace
          var neuron = this.neighboors[id];
          var influence = getVar('influences[' + neuron.ID + ']');

          var trace = getVar(this, 'trace', 'elegibility', input.ID, this.trace
            .elegibility[input.ID]);
          var xtrace = getVar(this, 'trace', 'extended', neuron.ID, input.ID,
            this.trace.extended[neuron.ID][input.ID]);
          if (neuron.selfconnected())
            var neuron_self_weight = getVar(neuron.selfconnection, 'weight');
          if (neuron.selfconnection.gater)
            var neuron_self_gain = getVar(neuron.selfconnection, 'gain');
          if (neuron.selfconnected())
            if (neuron.selfconnection.gater)
              buildSentence(xtrace, ' = ', neuron_self_gain, ' * ',
                neuron_self_weight, ' * ', xtrace, ' + ', derivative, ' * ',
                trace, ' * ', influence, store_trace);
            else
              buildSentence(xtrace, ' = ', neuron_self_weight, ' * ',
                xtrace, ' + ', derivative, ' * ', trace, ' * ',
                influence, store_trace);
          else
            buildSentence(xtrace, ' = ', derivative, ' * ', trace, ' * ',
              influence, store_trace);
        }
      }
      for (var connection in this.connections.gated) {
        var gated_gain = getVar(this.connections.gated[connection], 'gain');
        buildSentence(gated_gain, ' = ', activation, store_activation);
      }
    }
    if (!isInput) {
      var responsibility = getVar(this, 'error', 'responsibility', this.error
        .responsibility);
      if (isOutput) {
        var target = getVar('target');
        buildSentence(responsibility, ' = ', target, ' - ', activation,
          store_propagation);
        for (var id in this.connections.inputs) {
          var input = this.connections.inputs[id];
          var trace = getVar(this, 'trace', 'elegibility', input.ID, this.trace
            .elegibility[input.ID]);
          var input_weight = getVar(input, 'weight');
          buildSentence(input_weight, ' += ', rate, ' * (', responsibility,
            ' * ', trace, ')', store_propagation);
        }
        outputs.push(activation.id);
      } else {
        if (!noProjections && !noGates) {
          var error = getVar('aux');
          for (var id in this.connections.projected) {
            var connection = this.connections.projected[id];
            var neuron = connection.to;
            var connection_weight = getVar(connection, 'weight');
            var neuron_responsibility = getVar(neuron, 'error',
              'responsibility', neuron.error.responsibility);
            if (connection.gater) {
              var connection_gain = getVar(connection, 'gain');
              buildSentence(error, ' += ', neuron_responsibility, ' * ',
                connection_gain, ' * ', connection_weight,
                store_propagation);
            } else
              buildSentence(error, ' += ', neuron_responsibility, ' * ',
                connection_weight, store_propagation);
          }
          var projected = getVar(this, 'error', 'projected', this.error.projected);
          buildSentence(projected, ' = ', derivative, ' * ', error,
            store_propagation);
          buildSentence(error, ' = 0', store_propagation);
          for (var id in this.trace.extended) {
            var neuron = this.neighboors[id];
            var influence = getVar('aux_2');
            var neuron_old = getVar(neuron, 'old');
            if (neuron.selfconnection.gater == this)
              buildSentence(influence, ' = ', neuron_old, store_propagation);
            else
              buildSentence(influence, ' = 0', store_propagation);
            for (var input in this.trace.influences[neuron.ID]) {
              var connection = this.trace.influences[neuron.ID][input];
              var connection_weight = getVar(connection, 'weight');
              var neuron_activation = getVar(connection.from, 'activation');
              buildSentence(influence, ' += ', connection_weight, ' * ',
                neuron_activation, store_propagation);
            }
            var neuron_responsibility = getVar(neuron, 'error',
              'responsibility', neuron.error.responsibility);
            buildSentence(error, ' += ', neuron_responsibility, ' * ',
              influence, store_propagation);
          }
          var gated = getVar(this, 'error', 'gated', this.error.gated);
          buildSentence(gated, ' = ', derivative, ' * ', error,
            store_propagation);
          buildSentence(responsibility, ' = ', projected, ' + ', gated,
            store_propagation);
          for (var id in this.connections.inputs) {
            var input = this.connections.inputs[id];
            var gradient = getVar('aux');
            var trace = getVar(this, 'trace', 'elegibility', input.ID, this
              .trace.elegibility[input.ID]);
            buildSentence(gradient, ' = ', projected, ' * ', trace,
              store_propagation);
            for (var id in this.trace.extended) {
              var neuron = this.neighboors[id];
              var neuron_responsibility = getVar(neuron, 'error',
                'responsibility', neuron.error.responsibility);
              var xtrace = getVar(this, 'trace', 'extended', neuron.ID,
                input.ID, this.trace.extended[neuron.ID][input.ID]);
              buildSentence(gradient, ' += ', neuron_responsibility, ' * ',
                xtrace, store_propagation);
            }
            var input_weight = getVar(input, 'weight');
            buildSentence(input_weight, ' += ', rate, ' * ', gradient,
              store_propagation);
          }

        } else if (noGates) {
          buildSentence(responsibility, ' = 0', store_propagation);
          for (var id in this.connections.projected) {
            var connection = this.connections.projected[id];
            var neuron = connection.to;
            var connection_weight = getVar(connection, 'weight');
            var neuron_responsibility = getVar(neuron, 'error',
              'responsibility', neuron.error.responsibility);
            if (connection.gater) {
              var connection_gain = getVar(connection, 'gain');
              buildSentence(responsibility, ' += ', neuron_responsibility,
                ' * ', connection_gain, ' * ', connection_weight,
                store_propagation);
            } else
              buildSentence(responsibility, ' += ', neuron_responsibility,
                ' * ', connection_weight, store_propagation);
          }
          buildSentence(responsibility, ' *= ', derivative,
            store_propagation);
          for (var id in this.connections.inputs) {
            var input = this.connections.inputs[id];
            var trace = getVar(this, 'trace', 'elegibility', input.ID, this
              .trace.elegibility[input.ID]);
            var input_weight = getVar(input, 'weight');
            buildSentence(input_weight, ' += ', rate, ' * (',
              responsibility, ' * ', trace, ')', store_propagation);
          }
        } else if (noProjections) {
          buildSentence(responsibility, ' = 0', store_propagation);
          for (var id in this.trace.extended) {
            var neuron = this.neighboors[id];
            var influence = getVar('aux');
            var neuron_old = getVar(neuron, 'old');
            if (neuron.selfconnection.gater == this)
              buildSentence(influence, ' = ', neuron_old, store_propagation);
            else
              buildSentence(influence, ' = 0', store_propagation);
            for (var input in this.trace.influences[neuron.ID]) {
              var connection = this.trace.influences[neuron.ID][input];
              var connection_weight = getVar(connection, 'weight');
              var neuron_activation = getVar(connection.from, 'activation');
              buildSentence(influence, ' += ', connection_weight, ' * ',
                neuron_activation, store_propagation);
            }
            var neuron_responsibility = getVar(neuron, 'error',
              'responsibility', neuron.error.responsibility);
            buildSentence(responsibility, ' += ', neuron_responsibility,
              ' * ', influence, store_propagation);
          }
          buildSentence(responsibility, ' *= ', derivative,
            store_propagation);
          for (var id in this.connections.inputs) {
            var input = this.connections.inputs[id];
            var gradient = getVar('aux');
            buildSentence(gradient, ' = 0', store_propagation);
            for (var id in this.trace.extended) {
              var neuron = this.neighboors[id];
              var neuron_responsibility = getVar(neuron, 'error',
                'responsibility', neuron.error.responsibility);
              var xtrace = getVar(this, 'trace', 'extended', neuron.ID,
                input.ID, this.trace.extended[neuron.ID][input.ID]);
              buildSentence(gradient, ' += ', neuron_responsibility, ' * ',
                xtrace, store_propagation);
            }
            var input_weight = getVar(input, 'weight');
            buildSentence(input_weight, ' += ', rate, ' * ', gradient,
              store_propagation);
          }
        }
      }
      buildSentence(bias, ' += ', rate, ' * ', responsibility,
        store_propagation);
    }
    return {
      memory: varID,
      neurons: neurons + 1,
      inputs: inputs,
      outputs: outputs,
      targets: targets,
      variables: variables,
      activation_sentences: activation_sentences,
      trace_sentences: trace_sentences,
      propagation_sentences: propagation_sentences,
      layers: layers
    }
  }

  static uid() {
    return neurons++;
  }

  static quantity() {
    return {
      neurons: neurons,
      connections: connections
    }
  }
}