Hi,

I have a small network of 3 neurons and I’m using OrdinaryDiffEq to solve the ode system describing their interconnected dynamics. Since this problem is stiff, I’m using an adaptive solver that can handle it and this works just fine.

Now, say I don’t care too much about good accuracy because all I’m interested in is the behaviour which can be roughly eyeballed from the activity of the neurons. I want to use a fixed timestep solver, hoping that this might (?) speed up the solution.

However using Euler, even with a small timestep like with dt = 0.0001, gives an instability warning.

So my **question** is: How to disable the instability checking option if I want to grind through this regardless?

Here’s the working code

```
using NeuronBuilder, OrdinaryDiffEq
const Area = 0.0628
const Cm = 10.0
conv = Cm / Area
synaptic_conv = 1e-3 / Area^2
defaults = Dict(Voltage => BasicVoltageDynamics(),
Calcium => Prinz.CalciumDynamics(200.0, 0.05, 14.96 * 0.0628),
Reversal{Calcium} => Prinz.CaReversalDynamics())
somatic_parameters = Dict(
Reversal{Sodium} => 50.0,
Reversal{Potassium} => -80.0,
Reversal{Leak} => -50.0,
Reversal{Proton} => -20.0,
Voltage => -60.0,
Calcium => 0.05,
Reversal{Calcium} => 0.0)
function AB_Neuron(num_connections)
AB2_ch = [
Prinz.Na(100.0 * conv),
Prinz.CaS(6.0 * conv),
Prinz.CaT(2.5 * conv),
Prinz.H(0.01 * conv),
Prinz.Ka(50.0 * conv),
Prinz.KCa(5.0 * conv),
Prinz.Kdr(100.0 * conv),
Prinz.leak(0.0 * conv)
]
b = BasicNeuron(NoGeometry(Cm), defaults, somatic_parameters, AB2_ch, :AB)
return b(incoming_connections=num_connections)
end
function PY_Neuron(num_connections)
PY1_ch = [Prinz.Na(100.0 * conv),
Prinz.CaS(2.0 * conv),
Prinz.CaT(2.4 * conv),
Prinz.H(0.05 * conv),
Prinz.Ka(50.0 * conv),
Prinz.KCa(0.0 * conv),
Prinz.Kdr(125.0 * conv),
Prinz.leak(0.01 * conv)
]
somatic_parameters[Voltage] = -55.0
b = BasicNeuron(NoGeometry(Cm), defaults, somatic_parameters, PY1_ch, :PY)
return b(incoming_connections=num_connections)
end
function LP_Neuron(num_connections)
LP4_ch = [Prinz.Na(100.0 * conv),
Prinz.CaS(4.0 * conv),
Prinz.CaT(0.0 * conv),
Prinz.H(0.05 * conv),
Prinz.Ka(20.0 * conv),
Prinz.KCa(0.0 * conv),
Prinz.Kdr(25.0 * conv),
Prinz.leak(0.03 * conv)
]
somatic_parameters[Voltage] = -65.0
b = BasicNeuron(NoGeometry(Cm), defaults, somatic_parameters, LP4_ch, :LP)
return b(incoming_connections=num_connections)
end
AB = AB_Neuron(1)
LP = LP_Neuron(3)
PY = PY_Neuron(3)
neurons = [AB, PY, LP]
ABLP_chol = directed_synapse(AB, LP, Chol(30.0 * synaptic_conv))
ABPY_chol = directed_synapse(AB, PY, Chol(3.0 * synaptic_conv))
ABLP_glut = directed_synapse(AB, LP, Glut(30.0 * synaptic_conv))
ABPY_glut = directed_synapse(AB, PY, Glut(10.0 * synaptic_conv))
LPAB_glut = directed_synapse(LP, AB, Glut(30.0 * synaptic_conv))
LPPY_glut = directed_synapse(LP, PY, Glut(1.0 * synaptic_conv))
PYLP_glut = directed_synapse(PY, LP, Glut(30.0 * synaptic_conv))
connections = [LPAB_glut, ABPY_chol, ABPY_glut, LPPY_glut, ABLP_chol, ABLP_glut, PYLP_glut]
stg = add_all_connections(neurons, connections)
prob = ODEProblem(stg, [], (0.0, 10000.0), []; jac=true)
@time sol = solve(prob, AutoTsit5(Rosenbrock23()))
```

The output of this block of code with the AutoTsit5(Rosenbrock23()) algorithm is `1.683045 seconds (253.82 k allocations: 78.925 MiB, 1.97% gc time)`

.

But changing the last line to `@time sol = solve(prob, Euler(), dt=0.0001)`

gives

```
┌ Warning: Instability detected. Aborting
└ @ SciMLBase ~/.julia/packages/SciMLBase/OHiiA/src/integrator_interface.jl:351
```

Any help is greatly appreciated!

Cheers.