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NMODL is a DSL for describing ion channel and synapse dynamics that is used by NEURON, which provides the mod2c compiler parses dynamics described in NMODL to generate C code that is called from NEURON.

Arbor has an NMODL compiler, modcc, that generates optimized code in C++ and CUDA, which is optimized for the target architecture. NMODL does not have a formal specification, and its semantics are often ambiguous. To manage this, Arbor uses its own dialect of NMODL that does not allow some constructions used in NEURON’s NMODL.


We hope to replace NMODL with a DSL that is well defined, and easier for both users and the Arbor developers to work with in the long term. Until then, please write issues on our GitHub with any questions that you have about getting your NMODL files to work in Arbor.

This page is a collection of NMODL rules for Arbor. It assumes that the reader already has a working knowledge of NMODL.


Arbor doesn’t support unit conversion in NMODL. This table lists the key NMODL quantities and their expected units.





v / v_peer








diameter (cross-sectional)



current_density (density mechanisms)

identifier defined using NONSPECIFIC_CURRENT


conductivity (density mechanisms)

identifier inferred from current_density equation e.g. in i = g*v g is the conductivity


current (point and junction mechanisms)

identifier defined using NONSPECIFIC_CURRENT


conductance (point and junction mechanisms)

identifier inferred from current equation e.g. in i = g*v g is the conductance


ion X current_density (density mechanisms)



ion X current (point and junction mechanisms)



ion X reversal potential



ion X internal concentration



ion X external concentration



ion X diffusive concentration




  • Arbor recognizes na, ca and k ions by default. Any new ions used in NMODL need to be explicitly added into Arbor along with their default properties and valence (this can be done in the recipe or on a single cell model). Simply specifying them in NMODL will not work.

  • The parameters and variables of each ion referenced in a USEION statement are available automatically to the mechanism. The exposed variables are: internal concentration Xi, external concentration Xo, reversal potential eX and current iX. It is an error to also mark these as PARAMETER, ASSIGNED or CONSTANT.

  • READ and WRITE permissions of Xi, Xo, eX and iX can be set in NMODL in the NEURON block. If a parameter is writable it is automatically readable and must not be specified as both.

  • If Xi, Xo, eX, iX, Xd are used in a PROCEDURE or FUNCTION, they need to be passed as arguments.

  • If Xi or Xo (internal and external concentrations) are written in the NMODL mechanism they need to be declared as STATE variables and their initial values have to be set in the INITIAL block in the mechanism. This transfers all responsibility for handling Xi / Xo to the mechanism and will lead to painted initial values to be ignored. If these quantities are not made STATE they may be written to, but their values will be reset to their initial values every time step.

  • The diffusive concentration Xd does not share this semantics. It will not be reset, even if not in STATE, and may freely be written. This comes at the cost of awkward treatment of ODEs for Xd, see the included decay.mod for an example.

  • Xd is present on all cables iff its associated diffusivity is set to a non-zero value.

Special variables

  • Arbor exposes some parameters from the simulation to the NMODL mechanisms. These include v, diam, celsius and t in addition to the previously mentioned ion parameters.

  • These special variables should not be ASSIGNED or CONSTANT, they are PARAMETER. This is different from NEURON where a built-in variable is declared ASSIGNED to make it accessible.

  • diam and celsius are set from the simulation side.

  • v is a reserved variable name and can be read but not written in NMODL.

  • dt is not exposed to NMODL mechanisms.

  • area is not exposed to NMODL mechanisms.

  • NONSPECIFIC_CURRENTS should not be PARAMETER, ASSIGNED or CONSTANT. They just need to be declared in the NEURON block.

Functions, procedures and blocks

  • SOLVE statements should be the first statement in the BREAKPOINT block.

  • The return variable of FUNCTION has to always be set. if without associated else can break that if users are not careful.

  • Any non-LOCAL variables used in a PROCEDURE or FUNCTION need to be passed as arguments.

Unsupported features

  • Unit conversion is not supported in Arbor (there is limited support for parsing units, which are just ignored).

  • Unit declaration is not supported (ex: FARADAY = (faraday)  (10000 coulomb)). They can be replaced by declaring them and setting their values in CONSTANT.

  • FROM - TO clamping of variables is not supported. The tokens are parsed and ignored. However, CONSERVE statements are supported.

  • TABLE is not supported, calculations are exact.

  • derivimplicit solving method is not supported, use cnexp instead.

  • VERBATIM blocks are not supported.

  • LOCAL variables outside blocks are not supported.

  • INDEPENDENT variables are not supported.

Arbor-specific features

  • It is required to explicitly pass ‘magic’ variables like v into procedures. It makes things more explicit by eliding shared and implicit global state. However, this is only partially true, as having PARAMETER v brings it into scope, but only in BREAKPOINT.

  • Arbor’s NMODL dialect supports the most widely used features of NEURON. It also has some features unavailable in NEURON such as the POST_EVENT procedure block. This procedure has a single argument representing the time since the last spike on the cell. In the event of multiple detectors on the cell, and multiple spikes on the detectors within the same integration period, the times of each of these spikes will be processed by the POST_EVENT block. Spikes are processed only once and then cleared.

    Example of a POST_EVENT procedure, where g is a STATE parameter representing the conductance:

    POST_EVENT(t) {
       g = g + (0.1*t)
  • Arbor allows a gap-junction mechanism to access the membrane potential at the peer site of a gap-junction connection as well as the local site. The peer membrane potential is made available through the v_peer variable while the local membrane potential is available through v, as usual.


Many mechanisms make use of the reversal potential of an ion (eX for ion X). A popular equation for determining the reversal potential during the simulation is the Nernst equation. Both Arbor and NEURON make use of nernst. Arbor implements it as a mechanism and NEURON implements it as a built-in method. However, the conditions for using the nernst equation to change the reversal potential of an ion differ between the two simulators.

1. In Arbor, the reversal potential of an ion remains equal to its initial value (which has to be set by the user) over the entire course of the simulation, unless another mechanism which alters that reversal potential (such as nernst) is explicitly selected for the entire cell. (see Reversal potential dynamics for details).

2. In NEURON, there is a rule which is evaluated (under the hood) per section of a given cell to determine whether or not the reversal potential of an ion remains constant or is calculated using nernst. The rule is documented here and can be summarized as follows:

Examining all mechansims on a given section, if the internal or external concentration of an ion is written, and its reversal potential is read but not written, then the nernst equation is used continuously during the simulation to update the reversal potential of the ion. And if the internal or external concentration of an ion is read, and its reversal potential is read but not written, then the nernst equation is used once at the beginning of the simulation to caluclate the reversal potential of the ion, and then remains constant. Otherwise, the reversal potential is set by the user and remains constant.

One of the main consequences of this difference in behavior is that in Arbor, a mechanism modifying the reversal potential (for example nernst) can only be applied (for a given ion) at a global level on a given cell. While in Neuron, different mechanisms can be used for calculating the reversal potential of an ion on different parts of the morphology. This is due to the different methods Arbor and NEURON use for discretising the morphology. (A region in Arbor may include part of a CV, where as in NEURON, a section can only contain full segments).

Modelers are encouraged to verify the expected behavior of the reversal potentials of ions as it can lead to vastly different model behavior.

Tips for Faster NMODL


If you are looking for help with NMODL in the context of NEURON this guide might not help.

NMODL is a language without formal specification and many unexpected characteristics (many of which are not supported in Arbor), which results in existing NMODL files being treated as difficult to understand and best left as-is. This in turn leads to sub-optimal performance, especially since mechanisms take up a large amount of the simulations’ runtime budget. With some understanding of the subject matter, however, it is quite straightforward to obtain clean and performant NMODL files. We regularly have seen speed-ups factors of roughly three from optimising NMODL.

First, let us discuss how NMODL becomes part of an Arbor simulation. NMODL mechanisms are given in .mod files, whose layout and syntax has been discussed above. These are compiled by modcc into a series of callbacks as specified by the Mechanism ABI. These operate on data held in Arbor’s internal storage. But, modcc does not generate machine code, it goes through C++ (and/or CUDA) as an intermediary which is processed by a standard C++ compiler like GCC (or nvcc) to produce either a shared object (for external catalogues) and code directly linked into Arbor (the built-in catalogues).

Now, we turn to a series of tips we found helpful in producing fast NMODL mechanisms. In terms of performance of variable declaration, the hierarchy is from slowest to fastest:

  1. RANGE ASSIGNED – mutable array

  2. RANGE PARAMETER – configurable array

  3. ASSIGNED – mutable

  4. PARAMETER – configurable

  5. CONSTANT – inlined constant


Parameters and ASSIGNED variables marked as RANGE will be stored as an array with one entry per CV in Arbor. Reading and writing these incurs a memory access and thus affects cache and memory utilisation metrics. It is often more efficient to use LOCAL variables instead, even if that means foregoing the ability to re-use a computed value. Compute is so much faster than memory on modern hardware that re-use at the expense of memory accesses is seldom profitable, except for the most complex terms. LOCAL variables become just that in the generated code: a local variable that is likely residing in a register and used only as long as needed.


Prefer FUNCTION over PROCEDURE. The latter require ASSIGNED RANGE variables to return values and thus stress the memory system, which, as noted above, is not most efficient on current hardware. Also, they may not be inlined, as opposed to a FUNCTION.


PARAMETER should only be used for values that must be set by the simulator. All fixed values should be CONSTANT instead. These will be inlined by modcc and propagated through the computations which can uncover more optimisation potential.

Sharing Expressions Between INITIAL and BREAKPOINT or DERIVATIVE

This is often done using a PROCEDURE, which we now know is inefficient. On top, this PROCEDURE will likely compute more outputs than strictly needed to accomodate both blocks. DRY code is a good idea nevertheless, so use a series of FUNCTION instead to compute common expressions.

This leads naturally to a common optimisation in H-H style ion channels. If you heeded the advice above, you will likely see this patter emerge:

na   = n_alpha()
nb   = n_beta()
ntau = 1/(na + nb)
ninf = na*ntau

n' = (ninf - n)/ntau

Written out in this explicit way it becomes obvious that this can be expressed compactly as

na   = n_alpha()
nb   = n_beta()
nrho = na + nb

n' = na - n*nrho

The latter code is faster, but neither modcc nor the external C++ compiler will perform this optimisation 1. This is less easy to see when partially hidden in a PROCEDURE.


GCC/Clang might attempt it if asked to relax floating point accuracy with -ffast-math or -Ofast. However, Arbor refrains from using this option when compiling mechanism code.

Complex Expressions in Current Computation

modcc, Arbor’s NMODL compiler, applies symbolic differentiation to the current expression to find the conductance as g = d I/d U which are then used to compute the voltage update. g is thus computed multiple times every timestep and if the corresponding expression is inefficient, it will cost more time than needed. The differentiation implementation quite naive and will not optimise the resulting expressions. This is an internal detail of Arbor and might change in the future, but for now this particular optimisation can help to produce better performing code. Here is an example

: BAD, will compute m^4 * h every step
i = m^4 * h * (v - e)

: GOOD, will just use a constant value of g
g = m^4 * h
i = g * (v - e)

Note that we do not lose accuracy here, since Arbor does not support higher-order ODEs and thus will treat g as a constant across a single timestep even if g actually depends on v.

Specialised Functions

Another common pattern is the use of a guarded exponential of the form

if (x != 1) {
  r = x*exp(1 - x)
} else {
  r = x

This incurs some extra cost on most platforms. However, it can be written in Arbor’s NMODL dialect as


which is more efficient and has the same guarantees. NMODL files originating from NEURON often use this or related functions, e.g. vtrap(x, y) = y*exprelr(x/y).

Small Tips and Micro-Optimisations

  • Divisions cost a bit more than multiplications and additions.

  • m * m is more efficient than m^2. This holds for higher powers as well and if you want to squeeze out the utmost of performance use exponentiation-by-squaring. (Although GCC does this for you. Most of the time.)