www.neuron.yale.edu

Without looking closely at this or directly answering your question, my immediate thought is to wonder if lm_callback gets a meaningful value of y? You can avoid doing callbacks at every time step (and thus have a faster simulation) by using a StateTransitionEvent to do an action whenever a variable...

For (1): That's a statement about the variable step solver and does not directly apply to using the fixed step solver. However: above the example, there's also a statement about the fixed step solver, which LinearMechanism forces to use a matrix solver by Kundert (because adding arbitrary equations...

There are two ways to define 3D points. The more object-oriented way is to use sections, e.g. soma.pt3dadd(x, y, z, d) Unfortunately, that way requires numbers for the arguments. The older (and, apparently, more flexible in that it supports Vectors) way uses h and requires a sec= argument; in partic...

You can also do this using the LinearMechanism class. LinearMechanism solves vector ODEs of the form: c y' + gy = b In particular, let's consider the simple firing rate model: tau * y' + decay * y = f(I(t)) For this example, we suppose that I(t) and hence f(I(t)) are known in advance; this allows us...

Okay, so the fundamental situation is as follows: dend00 and dend11 both have the pump as defined by a mod file: dend00.insert('pump') ... dend11.insert('pump') This means that x and the pump dynamics must exist in both of these sections. cyt is defined only on dend00: cyt = rxd.Region(dend00, name=...

I made a quick attempt to reproduce this, but was unsuccessful (in the sense that no mass was created). Can you compare your version to mine and tell me how we differ? from neuron import h, rxd from neuron.units import mV, ms h.load_file('stdrun.hoc') soma = h.Section(name='soma') dend0 = h.Section(...

CVode is an adaptive algorithm that chooses time steps to give results within a specified error tolerance . Thus it will use small timesteps when things are changing rapidly (e.g. synaptic input, during an action potential) and larger timesteps where they are not. To get the same accuracy out of fix...

I suspect if you look at t, v, etc, you would find that they're lists of Vectors (or maybe but probably not strings), whereas the code you found on the NEURON site assumes them to be lists/vectors of floats. i.e. what is returned in t_, v_, etc is likely Vectors showing the full time course of the v...

There's also a HOC version of the programmer's reference at: https://www.neuron.yale.edu/neuron/stat ... index.html

That's very weird because there's nothing wrong with h('{sw4=new PlotShape(0)}') by itself. There's no way there's a weird character in there, is there? (Non-breaking space, that sort of thing?) Otherwise, my guess is that something badly destabilized NEURON before you got that far. If you're willin...

Given that you're trying to make a Python version of the HOC... you could change the HOC to do it right using for (x,0), and then validate your Python against that.

The short version, as you've figured out, is that your Python code and your HOC code are not equivalent ; they do different things, but both languages support both types of loops . Density mechanisms (like hh) exist on segments of positive surface area not on the 0 and 1 end points (which by definit...

h.finitialize() resets a model (if the mod files, etc were setup right) but it does not reset the simulator . importlib.reload will have no useful effect. It will not cause Sections to disappear, it will not reset the temperature, etc. It's basically only useful for modules that are themselves writ...

Your specific code doesn't work because you are evaluating the function foo(ns) at the time of your callback (it returns None) and that's what's being set as your callback. (By the order of operations, we evaluate foo(ns) before calling set_callback.) You could fix this by passing in a lambda that e...

Think of these things with [] as an array of sections; what that means is that the object you getattr is the array, not any individual section. i.e. do something like this: >>> from neuron import h >>> h('create axon[6]') 1 >>> getattr(h, 'axon')[5] axon[5] >>> sec = getattr(h, 'axon')[5] >>> ptr = ...