//Constructor
/// <summary>
/// Creates an initialized instance.
/// </summary>
/// <param name="name">The name of the input field.</param>
/// <param name="idx">The zero-based index of the input field among other input fields.</param>
/// <param name="coordinates">The coordinates of input neurons in 3D space.</param>
/// <param name="dataWorkingRange">The output range of the input data.</param>
/// <param name="featureFilterCfg">The configuration of the feature filter.</param>
/// <param name="spikesEncodingCfg">The configuration of the spikes coder.</param>
/// <param name="routeToReadout">Specifies whether to route the input field values to readout layer.</param>
/// <param name="inputNeuronsStartIdx">The zero-based index of the first input neuron of this field among all other input neurons.</param>
public InputField(string name,
int idx,
int[] coordinates,
Interval dataWorkingRange,
IFeatureFilterSettings featureFilterCfg,
InputSpikesCoderSettings spikesEncodingCfg,
bool routeToReadout,
int inputNeuronsStartIdx
)
{
Name = name;
Idx = idx;
RouteToReadout = routeToReadout;
_featureFilter = FeatureFilterFactory.Create(dataWorkingRange, featureFilterCfg);
_iAnalogStimuli = 0;
_currentDataIdx = 0;
//Spikes encoder
_spikesEncoder = new InputSpikesCoder(spikesEncodingCfg);
//Analog neuron
int verticalCycles = _spikesEncoder.Regime == InputEncoder.InputSpikesCoding.Vertical ? _spikesEncoder.LargestComponentLength : 1;
AnalogNeuron = new AnalogInputNeuron(new NeuronLocation(InputEncoder.ReservoirID, inputNeuronsStartIdx, InputEncoder.PoolID, inputNeuronsStartIdx, idx, coordinates[0], coordinates[1], coordinates[2]), verticalCycles);
++inputNeuronsStartIdx;
//Spiking neurons
int spikingPopulationSize;
if (_spikesEncoder.Regime == InputEncoder.InputSpikesCoding.Horizontal)
{
//Population encoding
spikingPopulationSize = _spikesEncoder.AllSpikesFlatCollection.Length;
}
else if (_spikesEncoder.Regime == InputEncoder.InputSpikesCoding.Vertical)
{
//Spike-train encoding
spikingPopulationSize = _spikesEncoder.ComponentSpikesCollection.Length;
}
else
{
//Forbidden encoding
spikingPopulationSize = 0;
}
SpikingNeuronCollection = new SpikingInputNeuron[spikingPopulationSize];
for (int i = 0; i < SpikingNeuronCollection.Length; i++)
{
SpikingNeuronCollection[i] = new SpikingInputNeuron(new NeuronLocation(InputEncoder.ReservoirID, inputNeuronsStartIdx, InputEncoder.PoolID, inputNeuronsStartIdx, idx, coordinates[0], coordinates[1], coordinates[2]));
++inputNeuronsStartIdx;
}
return;
}
//Constructor
/// <summary>
/// Creates an initialized instance
/// </summary>
/// <param name="name">Name of the input field</param>
/// <param name="idx">Index of the input field</param>
/// <param name="coordinates">Input coordinates (entry point)</param>
/// <param name="dataWorkingRange">Input data range</param>
/// <param name="featureFilterCfg">Feature filter configuration</param>
/// <param name="spikeCodeCfg">Configuration of the input spike code</param>
/// <param name="routeToReadout">Specifies whether to route values as the additional predictors to readout</param>
/// <param name="inputNeuronsStartIdx">Index of the first input neuron of this unit among all input neurons</param>
public InputField(string name,
int idx,
int[] coordinates,
Interval dataWorkingRange,
IFeatureFilterSettings featureFilterCfg,
SpikeCodeSettings spikeCodeCfg,
bool routeToReadout,
int inputNeuronsStartIdx
)
{
Name = name;
Idx = idx;
RouteToReadout = routeToReadout;
_featureFilter = FeatureFilterFactory.Create(dataWorkingRange, featureFilterCfg);
//Analog neuron
AnalogNeuron = new AnalogInputNeuron(new NeuronLocation(InputEncoder.ReservoirID, inputNeuronsStartIdx, InputEncoder.PoolID, inputNeuronsStartIdx, idx, coordinates[0], coordinates[1], coordinates[2]));
++inputNeuronsStartIdx;
//Spiking neurons
_realSpikeCode = null;
int populationSize = -1;
switch (_featureFilter.Type)
{
case FeatureFilterBase.FeatureType.Real:
_realSpikeCode = new SpikeCode(spikeCodeCfg);
populationSize = _realSpikeCode.Code.Length;
break;
case FeatureFilterBase.FeatureType.Binary:
populationSize = 1;
break;
case FeatureFilterBase.FeatureType.Enum:
populationSize = ((EnumFeatureFilterSettings)featureFilterCfg).NumOfElements;
break;
}
SpikingNeuronCollection = new SpikingInputNeuron[populationSize];
for (int i = 0; i < SpikingNeuronCollection.Length; i++)
{
SpikingNeuronCollection[i] = new SpikingInputNeuron(new NeuronLocation(InputEncoder.ReservoirID, inputNeuronsStartIdx, InputEncoder.PoolID, inputNeuronsStartIdx, idx, coordinates[0], coordinates[1], coordinates[2]));
++inputNeuronsStartIdx;
}
return;
}
private void CreateNetwork()
{
var iTop = new AnalogInputNeuron()
{
Tag = 0
};
var iLeft = new AnalogInputNeuron()
{
Tag = 1
};
var iRight = new AnalogInputNeuron()
{
Tag = 2
};
var iBottom = new AnalogInputNeuron()
{
Tag = 3
};
var sensoryNeurons = new List <Neuron>()
{
iTop, iRight, iBottom, iLeft
};
var n001 = new IzhikevichNeuron()
{
Tag = 4
};
var n002 = new IzhikevichNeuron()
{
Tag = 5
};
var n003 = new IzhikevichNeuron()
{
Tag = 6
};
var n004 = new IzhikevichNeuron()
{
Tag = 7
};
var n005 = new IzhikevichNeuron()
{
Tag = 8
};
var n006 = new IzhikevichNeuron()
{
Tag = 9
};
var n007 = new IzhikevichNeuron(true)
{
Tag = 10
};
var n008 = new IzhikevichNeuron(true)
{
Tag = 11
};
var n009 = new IzhikevichNeuron(true)
{
Tag = 12
};
var interNeurons = new List <Neuron>()
{
n001, n002, n003, n004, n005, n006, n007, n008, n009
};
var oAngle = new AnalogOutputNeuron()
{
Tag = 13
};
var oForce = new AnalogOutputNeuron()
{
Tag = 14
};
var outputNeurons = new List <Neuron>()
{
oAngle, oForce
};
var rand = new Random(1000);
// connect inputs to each interneuron
foreach (var n in interNeurons)
{
n.Dendrites = new Synapse[interNeurons.Count + sensoryNeurons.Count];
for (int i = 0; i < sensoryNeurons.Count; i++)
{
n.Dendrites[i] = new Synapse()
{
Source = sensoryNeurons[i], Target = n, Weight = 120.0d
};
}
}
// connect all inter neurons
foreach (var n in interNeurons)
{
for (int i = sensoryNeurons.Count; i < n.Dendrites.Length; i++)
{
if ((int)n.Tag < 10)
{
n.Dendrites[i] = new Synapse()
{
Source = interNeurons[i - sensoryNeurons.Count], Target = n, Weight = 0.5 * rand.NextDouble()
};
}
else
{
n.Dendrites[i] = new Synapse()
{
Source = interNeurons[i - sensoryNeurons.Count], Target = n, Weight = -1d * rand.NextDouble()
};
}
}
}
// connect all interneurons to ouput neurons
foreach (var n in outputNeurons)
{
n.Dendrites = new Synapse[interNeurons.Count];
for (int i = 0; i < n.Dendrites.Length; i++)
{
if ((int)interNeurons[i].Tag < 10)
{
n.Dendrites[i] = new Synapse()
{
Source = interNeurons[i], Target = n, Weight = 2.5 * rand.NextDouble()
};
}
else
{
n.Dendrites[i] = new Synapse()
{
Source = interNeurons[i], Target = n, Weight = -0.1d * rand.NextDouble()
};
}
}
}
iTop.AnalogInput = 10d;
_network = new VisualNetwork(GraphicsDevice, sensoryNeurons, interNeurons, outputNeurons);
}
