Use t in Shape function twice.

[vogdb]

Hi! I'm trying to implement a dual exponential waveform of synaptic conductance that looks like:

some_constant * (exp(-t/tau1) - exp(-t/tau2))

I'm doing this in the equations block.

shape I_shape_in = pA * tau_syn_in * (exp(-1/tau_syn_in_fall*t) - exp(-1/tau_syn_in_rise*t))
function I_syn pA = convolve(I_shape_in, in_spikes) + convolve(I_shape_ex, ex_spikes) + I_e + currents

receive an error:

/home/vogdb/workspace/xxx/neuron-intfire-nestml/build/int_fire4.cpp: In member function ‘virtual void int_fire4::update(const nest::Time&, long int, long int)’:
/home/vogdb/workspace/xxx/neuron-intfire-nestml/build/int_fire4.cpp:272:7: error: ‘__t’ was not declared in this scope
       __t = 0;
       ^

The current solution I use is to split this declaration into two shape functions. Is it correct?

    shape I_shape_in_rise = pA * tau_syn_in * -exp(-1/tau_syn_in_rise*t)
    shape I_shape_in_fall = pA * tau_syn_in * exp(-1/tau_syn_in_fall*t)
    function I_syn pA = convolve(I_shape_in_rise, in_spikes) + convolve(I_shape_in_fall, in_spikes) + convolve(I_shape_ex, ex_spikes) + I_e + currents

Full model description:

neuron int_fire4:

  state:
    r integer # counts number of tick during the refractory period
  end

  initial_values:
    V_m mV = 0mV
  end

  equations:
    shape I_shape_ex = pA * exp(-1/tau_syn_ex*t)
    shape I_shape_in = pA * tau_syn_in * (exp(-1/tau_syn_in_fall*t) - exp(-1/tau_syn_in_rise*t))
    function I_syn pA = convolve(I_shape_in, in_spikes) + convolve(I_shape_ex, ex_spikes) + I_e + currents
    V_m' = -1/tau_m * V_m + 1/1pF * I_syn
  end

  parameters:
    tau_m            ms = 10ms # Membrane time constant.
    tau_syn_in_rise  ms = 2ms  # Time constant of inhibitory synaptic current. Rise phase.
    tau_syn_in_fall  ms = 2ms  # Time constant of inhibitory synaptic current. Fall phase.
    tau_syn_ex       ms = 2ms  # Time constant of excitatory synaptic current.
    t_ref            ms = 2ms  # Duration of refractory period
    V_reset          mV = 0mV  # reset value of the membrane potential
    V_threshold      mV = 1mV  # Threshold value of V_m for spike
    I_e              pA = 0pA  # External current.
  end

  internals:
    RefractoryCounts integer = steps(t_ref) # refractory time in steps
    tau_syn_in real = tau_syn_in_rise * tau_syn_in_fall / (tau_syn_in_fall - tau_syn_in_rise)
  end

  input:
    ex_spikes pA <- excitatory spike
    in_spikes pA <- inhibitory spike
    currents    <- current
  end

  output: spike

  update:
    if r == 0: # neuron not refractory
      integrate_odes()
    else: # neuron is absolute refractory
      r = r - 1
    end

    if V_m >= V_threshold: # threshold crossing
      r = RefractoryCounts
      V_m = V_reset
      emit_spike()
    end

  end

end

[kperun]

@vogdb I think you are using the Java implementation of the toolchain? I have tried to use your model with PyNestML and I get weird errors which seem to be related to Sympy (?). @PTraeder and @DimitriPlotnikov here the log:
kperun@yogapad:~/Dropbox/PyNestMlAtGithub$ python2 PyNestML.py -path /home/kperun/Dropbox/mod.nestml -dev [4,int_fire4, ERROR, [13:28;13:28]]: Variable 'tau_syn_in' used before declaration! Traceback (most recent call last): File "PyNestML.py", line 29, in <module> main(sys.argv[1:]) File "/home/kperun/Dropbox/PyNestMlAtGithub/pynestml/frontend/PyNestMLFrontend.py", line 69, in main nest_generator.analyseAndGenerateNeurons(neurons) File "/home/kperun/Dropbox/PyNestMlAtGithub/pynestml/codegeneration/NestCodeGenerator.py", line 127, in analyseAndGenerateNeurons self.analyseAndGenerateNeuron(neuron) File "/home/kperun/Dropbox/PyNestMlAtGithub/pynestml/codegeneration/NestCodeGenerator.py", line 111, in analyseAndGenerateNeuron workingVersion = self.solveOdesAndShapes(workingVersion) File "/home/kperun/Dropbox/PyNestMlAtGithub/pynestml/codegeneration/NestCodeGenerator.py", line 265, in solveOdesAndShapes workingCopy = EquationsBlockProcessor.solveOdeWithShapes(_neuron) File "/home/kperun/Dropbox/PyNestMlAtGithub/pynestml/solver/EquationsBlockProcessor.py", line 54, in solveOdeWithShapes output = SymPySolver.solveOdeWithShapes(deep_copy.get_equations_blocks()) File "/home/kperun/Dropbox/PyNestMlAtGithub/pynestml/solver/SymPySolver.py", line 42, in solveOdeWithShapes output = OdeAnalyzer.compute_solution(input_json.toJSON()) File "/home/kperun/Dropbox/PyNestMlAtGithub/pynestml/solver/OdeAnalyzer.py", line 194, in compute_solution shape_functions) File "/home/kperun/Dropbox/PyNestMlAtGithub/pynestml/solver/OdeAnalyzer.py", line 207, in compute_exact_solution function_definitions) File "/home/kperun/Dropbox/PyNestMlAtGithub/pynestml/solver/prop_matrix.py", line 90, in ode_to_prop_matrices prop_matrices.append(simplify(exp(A * h))) File "/home/kperun/.local/lib/python2.7/site-packages/sympy/core/cache.py", line 93, in wrapper retval = cfunc(*args, **kwargs) File "/home/kperun/.local/lib/python2.7/site-packages/sympy/core/compatibility.py", line 794, in wrapper return user_function(*args, **kwds) File "/home/kperun/.local/lib/python2.7/site-packages/sympy/core/function.py", line 427, in __new__ result = super(Function, cls).__new__(cls, *args, **options) File "/home/kperun/.local/lib/python2.7/site-packages/sympy/core/cache.py", line 93, in wrapper retval = cfunc(*args, **kwargs) File "/home/kperun/.local/lib/python2.7/site-packages/sympy/core/compatibility.py", line 794, in wrapper return user_function(*args, **kwds) File "/home/kperun/.local/lib/python2.7/site-packages/sympy/core/function.py", line 250, in __new__ evaluated = cls.eval(*args) File "/home/kperun/.local/lib/python2.7/site-packages/sympy/functions/elementary/exponential.py", line 302, in eval return arg.exp() File "/home/kperun/.local/lib/python2.7/site-packages/sympy/matrices/matrices.py", line 2478, in exp ret = P * eJ * P.inv() File "/home/kperun/.local/lib/python2.7/site-packages/sympy/matrices/matrices.py", line 2781, in inv return self._eval_inverse(**kwargs) File "/home/kperun/.local/lib/python2.7/site-packages/sympy/matrices/dense.py", line 260, in _eval_inverse rv = M.inverse_GE(iszerofunc=iszerofunc) File "/home/kperun/.local/lib/python2.7/site-packages/sympy/matrices/matrices.py", line 2696, in inverse_GE raise ValueError("Matrix det == 0; not invertible.") ValueError: Matrix det == 0; not invertible.
Do you have any ideas what could lead to this? Unfortunately I am not even remotely familiar with the ODE toolbox.
Moreover, why does this problem occur in PyNestML and not in Java?

[vogdb]

@vogdb I think you are using the Java implementation of the toolchain?

Yep.

All other questions seem to be related to the contributors. I don't know answers to them :(

[clinssen]

I cannot reproduce this issue and will assume it has been fixed in the mean time. If you still run into it, please let me know!