protected void ShowNeuronBasedOnState(FastPixel fp, NeuronPlot np)
{
Color color = Color.Black;
switch (np.Neuron.ActionState)
{
case Neuron.State.Integrating:
int offset = np.Neuron.Config.ActionPotentialThreshold - np.Neuron.CurrentMembranePotential;
int range = np.Neuron.Config.ActionPotentialThreshold - np.Neuron.Config.RestingPotential;
int percent = 255 - (offset * 255 / range);
int cval = percent.Min(0).Max(255);
color = Color.FromArgb(0, cval, 0);
break;
case Neuron.State.Firing:
color = Color.White;
break;
case Neuron.State.RefractoryStart:
case Neuron.State.AbsoluteRefractory:
color = Color.FromArgb(255, 64, 64);
break;
case Neuron.State.RelativeRefractory:
color = Color.Yellow;
break;
}
Plot(fp, np.Location, color);
}
protected void ShowNeuronBasedOnState(FastPixel fp, NeuronPlot np)
{
Color color = Color.Black;
switch (np.Neuron.ActionState)
{
case Neuron.State.Integrating:
int offset = np.Neuron.Config.ActionPotentialThreshold - np.Neuron.CurrentMembranePotential;
int range = np.Neuron.Config.ActionPotentialThreshold - np.Neuron.Config.RestingPotential;
int percent = 255 - (offset * 255 / range);
int cval = percent.Min(0).Max(255);
color = Color.FromArgb(0, cval, 0);
break;
case Neuron.State.Firing:
color = Color.White;
break;
case Neuron.State.RefractoryStart:
case Neuron.State.AbsoluteRefractory:
color = Color.FromArgb(255, 64, 64);
break;
case Neuron.State.RelativeRefractory:
color = Color.Yellow;
break;
}
Plot(fp, np.Location, color);
}
protected void UpdateLocations()
{
foreach (DataRow row in dtStudy.Rows)
{
Neuron n = rowNeuronMap[row];
NeuronPlot np = studyNeuronPlots.Single(p => p.Neuron == n);
row["Location"] = np.Location;
}
}
protected void ShowNeuronBasedOnFiredCountDown(FastPixel fp, NeuronPlot np)
{
if (np.Neuron.ActionState == Neuron.State.Firing)
{
np.FiredCountDown = 11;
}
if (np.FiredCountDown > 0)
{
--np.FiredCountDown;
Color color = countDownColor[np.FiredCountDown];
Plot(fp, np.Location, color);
}
}
protected void ShowNeuronBasedOnFiredCountDown(FastPixel fp, NeuronPlot np)
{
if (np.Neuron.ActionState == Neuron.State.Firing)
{
np.FiredCountDown = 11;
}
if (np.FiredCountDown > 0)
{
--np.FiredCountDown;
Color color = countDownColor[np.FiredCountDown];
Plot(fp, np.Location, color);
}
}
public override void SelectObject(Point pos, List<NeuronPlot> plots)
{
foreach (NeuronPlot np in plots)
{
if ((np.Location.X - 5 < pos.X) &&
(np.Location.X + 5 > pos.X) &&
(np.Location.Y - 5 < pos.Y) &&
(np.Location.Y + 5 > pos.Y))
{
selectedNeuron = np;
NeuronSelected.Fire(this, new SelectedNeuronEventArgs() { Neuron = selectedNeuron.Neuron });
break;
}
}
}
protected int TestNeuronFiring(NeuronPlot np)
{
int idx = -1;
if (np.Neuron.ActionState == Neuron.State.Firing)
{
np.FiredCountDown = 11;
}
if (np.FiredCountDown > 0)
{
--np.FiredCountDown;
idx = np.FiredCountDown;
}
return(idx);
}
public override void SelectObject(Point pos, List <NeuronPlot> plots)
{
foreach (NeuronPlot np in plots)
{
if ((np.Location.X - 5 < pos.X) &&
(np.Location.X + 5 > pos.X) &&
(np.Location.Y - 5 < pos.Y) &&
(np.Location.Y + 5 > pos.Y))
{
selectedNeuron = np;
NeuronSelected.Fire(this, new SelectedNeuronEventArgs()
{
Neuron = selectedNeuron.Neuron
});
break;
}
}
}
public override bool Draw(FastPixel fp, Graphics gr, List <NeuronPlot> plots)
{
gr.Clear(Color.White);
foreach (NeuronPlot np in plots)
{
int colorIdx = TestNeuronFiring(np);
Pen pen = np == selectedNeuron ? penGreen : penBlack;
// Draw the neuron circle as a border if not firing, otherwise as a solid circle on a fire event and post-fire countdown.
if (colorIdx == -1)
{
gr.DrawEllipse(pen, new Rectangle(np.Location.X - 5, np.Location.Y - 5, 10, 10));
}
else
{
gr.FillEllipse(brushCountDown[colorIdx], new Rectangle(np.Location.X - 5, np.Location.Y - 5, 10, 10));
gr.DrawEllipse(pen, new Rectangle(np.Location.X - 5, np.Location.Y - 5, 10, 10));
}
// If no connections, just draw a vertical "no connection" endpoint
if (np.Neuron.Connections.Count == 0)
{
Point endpoint = np.Location - new Size(0, 45);
DrawSynapse(pen, gr, np.Location, endpoint, Connection.CMode.Excitatory);
}
else
{
foreach (Connection conn in np.Neuron.Connections)
{
NeuronPlot npConn = plots.Single(p => p.Neuron == conn.Neuron);
Point endpoint = npConn.Location;
DrawSynapse(pen, gr, np.Location, endpoint, conn.Mode);
}
}
}
return(false);
}
protected void UpdateStudyPlot(DataTable dt)
{
rowNeuronMap.Clear();
studyNeuronPlots.Clear();
pnlScope.ClearProbes();
probeColorIdx = 0;
foreach (DataRow row in dt.Rows)
{
Neuron n = new Neuron();
rowNeuronMap[row] = n;
for (int colIdx = 1; colIdx <= 7; ++colIdx)
{
UpdateNeuronProperty(n, row, colIdx);
}
n.Leakage = row["LKG"].ToString().ToPotential();
pnlScope.AddProbe(n, probeColors[probeColorIdx]);
NeuronPlot np = new NeuronPlot()
{
Neuron = n, Location = new Point(20 + probeColorIdx * 30, pnlNetwork.Height / 2)
};
if (row["Location"] != DBNull.Value)
{
string pos = row["Location"].ToString();
np.Location = new Point(pos.Between('=', ',').to_i(), pos.RightOf(',').Between('=', '}').to_i());
}
studyNeuronPlots.Add(np);
probeColorIdx = (probeColorIdx + 1) % probeColors.Length;
}
pnlNetwork.SetPlots(studyNeuronPlots);
pnlNetwork.Tick();
}
protected void UpdateStudyPlot(DataTable dt)
{
rowNeuronMap.Clear();
studyNeuronPlots.Clear();
pnlScope.ClearProbes();
probeColorIdx = 0;
foreach (DataRow row in dt.Rows)
{
Neuron n = new Neuron();
rowNeuronMap[row] = n;
for (int colIdx = 1; colIdx <= 7; ++colIdx)
{
UpdateNeuronProperty(n, row, colIdx);
}
n.Leakage = row["LKG"].ToString().ToPotential();
pnlScope.AddProbe(n, probeColors[probeColorIdx]);
NeuronPlot np = new NeuronPlot() { Neuron = n, Location = new Point(20 + probeColorIdx * 30, pnlNetwork.Height / 2) };
if (row["Location"] != DBNull.Value)
{
string pos = row["Location"].ToString();
np.Location = new Point(pos.Between('=', ',').to_i(), pos.RightOf(',').Between('=', '}').to_i());
}
studyNeuronPlots.Add(np);
probeColorIdx = (probeColorIdx + 1) % probeColors.Length;
}
pnlNetwork.SetPlots(studyNeuronPlots);
pnlNetwork.Tick();
}
public override void DeselectObject()
{
selectedNeuron = null;
}
public override void DeselectObject()
{
selectedNeuron = null;
}
protected int TestNeuronFiring(NeuronPlot np)
{
int idx = -1;
if (np.Neuron.ActionState == Neuron.State.Firing)
{
np.FiredCountDown = 11;
}
if (np.FiredCountDown > 0)
{
--np.FiredCountDown;
idx = np.FiredCountDown;
}
return idx;
}
protected void CreateNetwork()
{
Cursor = Cursors.WaitCursor;
networkNeuronPlots = new List <NeuronPlot>();
int mx = pnlNetwork.Width / 4;
int my = pnlNetwork.Height / 4;
int c = NetworkConfig.DefaultConfiguration.NumConnections; // # of connections each neuron makes
int d = NetworkConfig.DefaultConfiguration.MaxDistance; // max distance of each connection. Must be multiple of 2
int r = NetworkConfig.DefaultConfiguration.Radius; // radius (as a square) of connections. Must be multiple of 2
int p = NetworkConfig.DefaultConfiguration.Pacemakers; // # of pacemaker neurons
// Initialize an mx x my array of neurons. The edges wrap top-bottom / left-right.
for (int x = 0; x < mx; x++)
{
for (int y = 0; y < my; y++)
{
Neuron n = new Neuron();
networkNeuronPlots.Add(new NeuronPlot()
{
Neuron = n, Location = new Point(x * 4, y * 4)
});
}
}
// Each neuron connects to c other neurons in a localized r x r region at a maximum distance of d from the originating neuron.
foreach (NeuronPlot np in networkNeuronPlots)
{
int x = np.Location.X / 4;
int y = np.Location.Y / 4;
// Note: rnd.Next(min,max) is inclusive of the lower bound and exclusive of the upper bound.
// To avoid introducing bias, we add 1 to the upper bound.
// Target location:
int targetx = x + rnd.Next(-d / 2, (d / 2) + 1);
int targety = y + rnd.Next(-d / 2, (d / 2) + 1);
for (int i = 0; i < c; i++)
{
// Connection location around the target.
int adjx = targetx + rnd.Next(-r / 2, (r / 2) + 1);
int adjy = targety + rnd.Next(-r / 2, (r / 2) + 1);
if (adjx < 0)
{
adjx += mx;
}
if (adjy < 0)
{
adjy += my;
}
int qx = adjx % mx;
int qy = adjy % my;
// Find the neuron at this location
NeuronPlot npTarget = networkNeuronPlots.Single(np2 => np2.Location.X == qx * 4 & np2.Location.Y == qy * 4);
np.Neuron.AddConnection(new Connection(npTarget.Neuron));
}
}
// Pick p neurons to be pacemakers at different rates.
for (int i = 0; i < p; i++)
{
Neuron n = networkNeuronPlots[rnd.Next(mx * my)].Neuron;
// You get more interesting patterns when the leakage is randomized so that pacemaker neurons do not all
// fire synchronously.
n.Leakage = 256 + rnd.Next(256);
/*
* if (i == 0)
* {
* pnlScope.AddProbe(n, Color.LightBlue);
* // Pick the first connecting neuron for a second trace.
* pnlScope.AddProbe(n.Connections[0].Neuron, Color.Red);
* }
*/
Cursor = Cursors.Arrow;
}
pnlNetwork.SetPlots(networkNeuronPlots);
/*
* pacemakerNeuron = new Neuron();
* pacemakerNeuron.Leakage = 2 << 8;
* pacemakerNeuron.Integration = 0;
*
* receiverNeuron = new Neuron();
*
* pacemakerNeuron.AddConnection(new Connection(receiverNeuron));
*
* neuronPlots.Add(new NeuronPlot() { Neuron = pacemakerNeuron, Location = new Point(pnlNetwork.Width / 2, pnlNetwork.Height / 2) });
* neuronPlots.Add(new NeuronPlot() { Neuron = receiverNeuron, Location = new Point(pnlNetwork.Width / 2 + 20, pnlNetwork.Height / 2) });
*
* pnlScope.AddProbe(pacemakerNeuron, Color.LightBlue);
* pnlScope.AddProbe(receiverNeuron, Color.Red);
*/
}
