public void CreateNetworkMeanField(NeuronRegime nr, Int32 totalTime)
{
this.synapses = new List <GapJunction>();
Int32 j, i = 0;
while (i < this.N)
{
j = 0;
while (j < this.N)
{
if (i != j)
{
this.synapses.Add(new GapJunction(this.neurons[i], this.neurons[j], this.synG));
this.neurons[j].AddInput(this.synapses.Last());
}
j++;
}
i++;
}
this.nSyn = this.synapses.Count;
Double[] ic = this.neurons[0].ResetToFP();
Random r = new Random();
this.neurons.ForEach(n => n.SetIC(ic.Select(x => x * r.NextDouble()).ToArray())); // randomizing IC for all neurons
}
public void Reset(NeuronRegime nr, Int32 totalTime)
{
// resets network
//this.synapses.ForEach(s => s.Reset());
//this.neurons.ForEach(n => n.Reset(nr, totalTime));
this.neuReg = nr;
this.totalTime = totalTime;
this.Initialize();
}
public ModelSimulator(NeuronRegime nr)
{
this.neuronRegime = nr;
this.KTzLogMap = new KTzLogModel(nr);
this.KTzTanhMap = new KTzTanhModel(nr);
this.RulkovMap = new RulkovModel(nr);
this.IzhikevichMap = new IzhikevichModel(nr);
this.HHLeechODE = new HHLeechModel(nr);
this.HHStdODE = new HHStdModel(nr);
this.ResetModels();
}
public override void Reset(NeuronRegime nr, Int32 totalTime)
{
this.ts_per_ms = 100.0 / 10.0;
this.transient = 1000; // in ts
this.dt = 0.1;
if (nr == NeuronRegime.Bursting)
{
this.SetBurstingParam();
}
else
{
this.SetExcitableParam();
}
}
private void CreateNeurons(NeuronType neut, NeuronRegime nr, Int32 totalTime)
{
this.neurons = new List <Neuron>(this.N);
Int32 i = 0;
while (i < this.N)
{
this.neurons.Add(NeuronFactory.Create(neut, nr, totalTime));
i++;
}
this.ts_per_ms = this.neurons[0].ts_per_ms;
this.transient = this.neurons[0].transient;
this.dt = this.neurons[0].dt;
}
private Dictionary <String, Statistics> tsForFPStat; // one entry for each model
public ModelSimulator(NeuronRegime nr, Int32 totalTime, Double tolerance, Int32 maxTime, NetworkType nt, Int32 N)
{
this.neuronRegime = nr;
this.GLExpMap = new GLExpModel(nr, totalTime);
this.LIFMap = new LIFModel(nr, totalTime);
this.KTzLogMap = new KTzLogModel(nr, totalTime);
this.KTzTanhMap = new KTzTanhModel(nr, totalTime);
this.RulkovMap = new RulkovModel(nr, totalTime);
this.IzhikevichMap = new IzhikevichModel(nr, totalTime);
this.HHLeechODE = new HHLeechModel(nr, totalTime);
this.HHStdODE = new HHStdModel(nr, totalTime);
if (nr == NeuronRegime.Excitable)
{
this.NetLIFMap = new NetworkModel(NeuronType.LIF, nr, totalTime, nt, N, 0.1);
this.NetGLExpMap = new NetworkModel(NeuronType.GLExp, nr, totalTime, nt, N, 0.1);
this.NetKTzLogMap = new NetworkModel(NeuronType.KTzLog, nr, totalTime, nt, N, 0.1);
this.NetKTzTanhMap = new NetworkModel(NeuronType.KTzTanh, nr, totalTime, nt, N, 0.04);
this.NetRulkovMap = new NetworkModel(NeuronType.Rulkov, nr, totalTime, nt, N, 0.08);
this.NetIzhikevichMap = new NetworkModel(NeuronType.Izhikevich, nr, totalTime, nt, N, 0.1);
this.NetHHModelODE = new NetworkModel(NeuronType.HodgkinHuxley, nr, totalTime, nt, N, 0.05);
}
else if (nr == NeuronRegime.Bursting)
{
this.NetLIFMap = new NetworkModel(NeuronType.LIF, nr, totalTime, nt, N, 0.1);
this.NetGLExpMap = new NetworkModel(NeuronType.GLExp, nr, totalTime, nt, N, 0.1);
this.NetKTzLogMap = new NetworkModel(NeuronType.KTzLog, nr, totalTime, nt, N, 0.0);
this.NetKTzTanhMap = new NetworkModel(NeuronType.KTzTanh, nr, totalTime, nt, N, 0.04);
this.NetRulkovMap = new NetworkModel(NeuronType.Rulkov, nr, totalTime, nt, N, 0.08);
this.NetIzhikevichMap = new NetworkModel(NeuronType.Izhikevich, nr, totalTime, nt, N, 0.1);
this.NetHHModelODE = new NetworkModel(NeuronType.HodgkinHuxley, nr, totalTime, nt, N, 1.0e-10);
}
this.ResetModels(totalTime, tolerance, maxTime);
this.tsForFixedPointPerModel = new List <Int32>();
this.tsForFPStat = new Dictionary <String, Statistics>(5);
this.tsForFPStat.Add(this.LIFMap.ToString(), new Statistics());
this.tsForFPStat.Add(this.GLExpMap.ToString(), new Statistics());
this.tsForFPStat.Add(this.KTzLogMap.ToString(), new Statistics());
this.tsForFPStat.Add(this.KTzTanhMap.ToString(), new Statistics());
this.tsForFPStat.Add(this.IzhikevichMap.ToString(), new Statistics());
this.tsForFPStat.Add(this.RulkovMap.ToString(), new Statistics());
this.tsForFPStat.Add(this.HHStdODE.ToString(), new Statistics());
this.tsForFPStat.Add(this.NetLIFMap.ToString(), new Statistics());
this.tsForFPStat.Add(this.NetGLExpMap.ToString(), new Statistics());
this.tsForFPStat.Add(this.NetKTzLogMap.ToString(), new Statistics());
this.tsForFPStat.Add(this.NetKTzTanhMap.ToString(), new Statistics());
this.tsForFPStat.Add(this.NetRulkovMap.ToString(), new Statistics());
this.tsForFPStat.Add(this.NetIzhikevichMap.ToString(), new Statistics());
this.tsForFPStat.Add(this.NetHHModelODE.ToString(), new Statistics());
}
public void CreateNetworkLinear(NeuronRegime nr, Int32 totalTime)
{
this.synapses = new List <GapJunction>();
Int32 i = 1;
while (i < this.N)
{
this.synapses.Add(new GapJunction(this.neurons[i - 1], this.neurons[i], this.synG));
this.neurons[i].AddInput(this.synapses.Last());
i++;
}
this.nSyn = this.synapses.Count;
this.neurons.ForEach(n => n.ResetToFP()); // only neuron 0 has activity in t = 0
this.neurons[0].Reset(nr, totalTime);
}
public static Neuron Create(NeuronType nt, NeuronRegime nr, Int32 totalTime)
{
if (nt == NeuronType.HodgkinHuxley)
{
if (nr == NeuronRegime.Bursting)
{
return(new HHLeechModel(nr, totalTime));
}
else if (nr == NeuronRegime.Excitable)
{
return(new HHStdModel(nr, totalTime));
}
else
{
throw new ArgumentOutOfRangeException("Unrecognized NeuronRegime");
}
}
else if (nt == NeuronType.Rulkov)
{
return(new RulkovModel(nr, totalTime));
}
else if (nt == NeuronType.Izhikevich)
{
return(new IzhikevichModel(nr, totalTime));
}
else if (nt == NeuronType.KTzTanh)
{
return(new KTzTanhModel(nr, totalTime));
}
else if (nt == NeuronType.KTzLog)
{
return(new KTzLogModel(nr, totalTime));
}
else if (nt == NeuronType.GLExp)
{
return(new GLExpModel(nr, totalTime));
}
else if (nt == NeuronType.LIF)
{
return(new LIFModel(nr, totalTime));
}
else
{
throw new ArgumentOutOfRangeException("Unrecognized NeuronType");
}
}
public override void Reset(NeuronRegime nr, Int32 totalTime)
{
this.initState = new Double[4];
this.currState = new Double[4];
this.c1 = new Double[4];
this.c2 = new Double[4];
this.c3 = new Double[4];
this.c4 = new Double[4];
this.dydt = new Double[4];
this.dt = 0.01;
this.ts_per_ms = 1 / this.dt;
this.transient = 100; // in ms
if (nr == NeuronRegime.Bursting)
{
this.SetBurstingParam();
}
else
{
this.SetExcitableParam();
}
}
public NetworkModel(NeuronType neut, NeuronRegime nr, Int32 totalTime, NetworkType nt, Int32 N, Double synG)
{
this.isNetwork = true;
this.netType = nt;
this.neuType = neut;
this.neuReg = nr;
this.totalTime = totalTime;
this.N = N;
this.synG = synG;
if (nt == NetworkType.Linear)
{
this.CreateNetwork = this.CreateNetworkLinear;
}
else if (nt == NetworkType.MeanField)
{
this.CreateNetwork = this.CreateNetworkMeanField;
}
else
{
throw new ArgumentOutOfRangeException("Unrecognized NetworkType");
}
this.Initialize();
}
/*public Double dt { get; private set; } // the step of the integration
* public Double ts_per_ms { get; private set; }
* public Int32 transient { get; private set; }*/
public HHStdModel(NeuronRegime nr, Int32 totalTime)
{
this.Reset(nr, totalTime);
}
/*public Double dt { get; private set; } // the step of the integration
* public Double ts_per_ms { get; private set; }
* public Int32 transient { get; private set; }*/
public RulkovModel(NeuronRegime nr, Int32 totalTime)
{
this.Reset(nr, totalTime);
}
/*public Double dt { get; private set; } // the step of the integration
* public Double ts_per_ms { get; private set; }
* public Int32 transient { get; private set; }*/
public IzhikevichModel(NeuronRegime nr, Int32 totalTime)
{
this.Reset(nr, totalTime);
}
/*public Double dt { get; private set; } // the step of the integration
* public Double ts_per_ms { get; private set; }
* public Int32 transient { get; private set; }*/
public KTzTanhModel(NeuronRegime nr, Int32 totalTime)
{
this.Reset(nr, totalTime);
}
/*public Double dt { get; private set; } // the step of the integration
* public Double ts_per_ms { get; private set; }
* public Int32 transient { get; private set; }*/
public GLExpModel(NeuronRegime nr, Int32 totalTime)
{
this.Reset(nr, totalTime);
this.rand = new System.Random();
}
public abstract void Reset(NeuronRegime nr, Int32 totalTime);
