SpineML The Spiking Neural Mark-up Language

Tutorials

We have three tutorials to help new users get used to working with the idea of creating components. Components are used to specify the mathematical rules for the neural elements in a model. These components are used as the basis for the populations which are connected together in a network. Once you have a network, you can then create an experiment which allows you to simulate the model for a given amount of time and with a given set of inputs.

Creating a component in the component editor

Creating a network in the network editor

Creating experiments and graphing

Creating a new connectivity using a Python Script

Python scripts for generating connectivity are added to SpineCreator through the Settings/Preferences dialog, and once added appear in the list of connection types.

To allow non-programmers to use and configure extended connectivity types a set of formatted comments is added to the top of the Python script: these specify parameters to pass to the script, and define how SpineCreator should provide a graphical interface for these parameters. For example:

#PARNAME=sigma #LOC=1,1
#PARNAME=minimum_weight #LOC=2,1
#HASWEIGHT

def connectionFunc(srclocs,dstlocs,sigma,min_w):
  import math
  normterm = 1/(sigma*math.sqrt(2*math.pi))
  i = 0
  out = []
  for srcloc in srclocs:
    j = 0
    for dstloc in dstlocs:
      dist = math.sqrt(math.pow((srcloc[0] - dstloc[0]),2) + \
        math.pow((srcloc[1] - dstloc[1]),2) + \
        math.pow((srcloc[2] - dstloc[2]),2))
      gauss = normterm*math.exp(-0.5*math.pow(dist/sigma,2))
      if gauss > min_w:
        conn = (i,j,0,gauss)
        out.append(conn)
      j = j + 1
    i = i + 1
  return out

Adding parameters to the SpineCreator GUI is performed using the #PARNAME comment, which gives a name used as a label for the parameter, and a #LOC which describes the row and column where the parameter should be added in a grid for laying out the parameters. The order of the parameters in the code denotes the order they have in the corresponding Python function call, and allows the label to have a more descriptive name than the variable used in the function. In addition there are two more comments that are parsed; #HASWEIGHT and #HASDELAY, which inform SpineCreator if the script needs to generate a weight and/or a delay. If a weight is generated SpineCreator will provide a drop-down list of the corresponding Properties in the WeightUpdate Component, and the selected Property will have the weight values inserted when the connectivity is generated.

The function itself has arguments srclocs and dstlocs, which are Lists of Tuples, each Tuple containing the x, y, and z co-ordinates of the neuron at that index in the List.