CoreNEURON Compatibility

CoreNEURON can transparently handle all spiking network simulations, including gap junction coupling, with the fixed time step method. In this document we summarise NEURON features that are not supported by CoreNEURON and changes required to make a model compatible with CoreNEURON.

Unsupported Features

The below table summarises NEURON features that are presently not supported with CoreNEURON:

Feature	NEURON	CoreNEURON	Remarks *
Variable time step methods (i.e CVODE and IDA)	✔	✖
Extracellular Mechanism	✔	✖ *	If i_membrane is of sole interest, then you can switch from extracellular to cvode.use_fast_imem(1) and make use of i_membrane_
Linear Mechanism	✔	✖
RxD Module	✔	✖
Per-timestep interpreter calculations during simulation	✔	✖ *	Generally, Vector.record and Vector.play are ok, as are the display of GUI variable trajectories. Anything requiring callbacks into the interpreter (Python or HOC) are not implemented.

If you are using any of the above mentioned features in your model then CoreNEURON library can not be used for simulations. If you are interested in these features with CoreNEURON or performance improvement in general, feel free to provide feedback via a GitHub issue.

MOD File Compatibility

In order to use CoreNEURON, you need to build MOD files with CoreNEURON support i.e. use -coreneuron option as nrnivmodl -coreneuron <mod dir>. If this command fails during compilation or translating MOD files but nrnivmodl <mod dir> is working fine then most likely there are MOD files incompatibility issues. In this section we describe how you can address such incompatibility.

Thread Safe MOD Files

One of the important difference in CoreNEURON execution with respect to NEURON is that multiple instances of a specific channel or synapse are executed in parallel. This parallelism could be via threads or SIMD instructions on modern CPUs/GPUs. Most of the MOD files are thread safe for parallel execution but there are certain constructs like GLOBAL variables or VERBATIM blocks that are not compatible by default. In this case user has to make sure MOD files are thread safe. NEURON provides a script mkthreadsafe that can provide some help to make your MOD files are thread safe. You can find more information here. Here are some additional examples that will help you to make a MOD file thread safe:

• If you are using GLOBAL variables in a mod file, make sure those are not updated during execution (e.g. in INITIAL, BREAKPOINT or DERIVATIVE block etc). If GLOBAL variable is writen then those variables are now needed to be defined as RANGE. You can find information about GLOBAL and RANGE variables in NMODL specification.

  As an example, below MOD file defines minf variable as a GLOBAL and it is being updated in the DERIVATIVE block when PROCEDURE rates() is executed.

  NEURON {
    SUFFIX test
    GLOBAL minf
  }
  
  ...
  
  DERIVATIVE states {
    rates(v)
    m' = (minf-m)/1.5
  }
  
  PROCEDURE rates(v (mV)) {
    minf = minf+1
  }
  

  As GLOBAL variable is shared across multiple instances of a mechanism, parallel execution will result into a race condition when minf is updated. In order to avoid this we need to simply convert it to a RANGE variable:

  NEURON {
    SUFFIX test
    RANGE minf
  }
  ...
  

  Note that if you are accessing such variable via HOC or Python scripting interface then syntax for accessing GLOBAL is different than RANGE variable. So you need to update your code accordingly.

• If you are using VERBATIM blocks to overcome some limitations of NMODL language then such MOD file is by default treated as not thread safe. For example, in below case we are are using VERBATIM block to include some C header and return early from INITIAL block:

  NEURON {
    SUFFIX test
    RANGE minf
  }
  
  VERBATIM
  #include <stdlib.h>
  ENDVERBATIM
  
  ASSIGNED {
    v            (mV)
    minf
  }
  
  STATE {
    m
  }
  
  INITIAL {
    rate(v)
    m = minf
    VERBATIM
    return 0;
    ENDVERBATIM
  }
  
  ...
  

  Technically, this mod file is thread safe as we don’t have any race condition. But due to VERBATIM block this mod file is assumed non thread safe and hence we have to explicitly specify THREADSAFE keywork in the beginning of the NEURON block as:

  NEURON {
    THREADSAFE
    SUFFIX test
    RANGE minf
  }
  
  ...
  

  Also, note that NEURON block needs to be before any VERBATIM block in the MOD file. So its safer to keep NEURON block at the top of MOD file.

• Certain SOLVE methods like euler are not thread safe since the best practical methods are cnexp for HH-like equations and derivimplicit for all the others. If you have such a MOD file:

  SOLVE state METHOD euler
  

  then replace euler with cnexp.

TABLE Usage With GPU Execution

Currently TABLE constructs are not supported if you are building MOD files with GPU support. As TABLE constructs are used for efficiency reason (and not accuracy), you can safely comment out TABLE statement using : operator:

PROCEDURE rates(v(mV)) {

   : TABLE minf, mtau, hinf, htau, ninf, ntau DEPEND celsius FROM -100 TO 100 WITH 200

NEURON Only MOD Files

Certain MOD files are used for aspects like progress callbacks, reading inputs, etc. Often such MOD files are heavily depend on VERBATIM blocks and use internal data structure or functions provided by NEURON. Most likely such MOD files won’t be compiled by CoreNEURON as they are using internal, NEURON specific APIs in VERBATIM blocks. If such mod file is used for only a usability aspect like progress bar then you can exclude that from compilation. Other option is to conditionally compile all VERBATIM blocks using macro NRNBBCORE.

As an example, the code in below #ifndef NRNBBCORE block will be only compiled for NEURON.

VERBATIM
#ifndef NRNBBCORE
<code block to be executed only by NEURON>
#endif
ENDVERBATIM

This way you can hide NEURON specific code from CoreNEURON compilation process.

Explicit ION Variables Update

In some old MOD files ion currents are explicitly initialized in INITIAL blocks using VERBATIM construct as:

VERBATIM
cai = _ion_cai;
Cai = _ion_Cai;
ENDVERBATIM

Since such ion variables are implicitely updated by the code from NMODL transpiler, VERBATIM blocks like above are not required and must be deleted from the MOD files.

Random Number Generators: Random123 vs MCellRan4

Pseudo-random numbers from a variety of distributions can be generated using NEURON’s Random class. CoreNEURON only supports Random123 generator.

Memory Management for POINTER Variables

User-allocated data managed in NMODL is a complex topic. Using POINTER variables, users can reference data that has been allocated in HOC or in VERBATIM blocks. Using this end users can built more advanced data-structures that are not natively supported in NMODL. Another commonly used example is point processes / synapses where POINTER variables used for holding random number generator object.

Since NEURON itself has no knowledge of the layout and size of this user allocated data, it cannot transfer POINTER data automatically to CoreNEURON. Furtheremore, in many cases there is no need to transfer the data between the two instances. In some cases, however, the programmer would like to transfer certain user-defined data into CoreNEURON. The most prominent example are Random123 random number stream parameters used in synapse mechanisms.

In order to inform NEURON that such POINTER variable needs to be transferred, the BBCOREPOINTER type was introduced. Variables that are declared as BBCOREPOINTER behave exactly the same as POINTER but are additionally taken into account when NEURON is transferring model to CoreNEURON for simulation. For NEURON to be able to write (and indeed CoreNEURON to be able to read) BBCOREPOINTER data, the programmer has to additionally provide two C functions that are called as part of the serialization/deserialization:

static void bbcore_write(double* x, int* d, int* d_offset, int* x_offset, _threadargsproto_);
static void bbcore_read(double* x, int* d, int* d_offset, int* x_offset, _threadargsproto_);

The implementation of bbcore_write and bbcore_read determines the serialization and deserialization of the per-instance mechanism data referenced through the various BBCOREPOINTER.

NEURON will call bbcore_write twice per mechanism instance. In a first sweep, the call is used to determine the required memory to be allocated on the serialization arrays. In the second sweep the call is used to fill in the data per mechanism instance.

Arguments to bbcore_read and bbcore_write.

Argument	Description
x	A double type array that will be allocated by NEURON to fill with real-valued data. In the first call, x is nullptr as it has not been allocated yet.
d	An int type array that will be allocated by NEURON to fill with integer-valued data. In the first call, d is nullptr as it has not been allocated yet.
x_offset	The offset in x at which the mechanism instance should write its real-valued BBCOREPOINTER data. In the first call this is an output argument that is expected to be updated by the per-instance size to be allocated.
d_offset	The offset in d at which the mechanism instance should write its integer-valued BBCOREPOINTER data. In the first call this is an output argument that is expected to be updated by the per-instance size to be allocated.
_threadargsproto_	A macro placeholder for NEURON/CoreNEURON data-structure parameters. They are typically only used through generated defines and not by the programmer. The macro is defined as follows: #define _threadargsproto_ int _iml, int _cntml_padded, double *_p, Datum *_ppvar, \ ThreadDatum *_thread, NrnThread *_nt, double _v

Putting all of this together, the following is a minimal MOD using BBCOREPOINTER:

TITLE A BBCOREPOINTER Example

NEURON {
  BBCOREPOINTER my_data : changed from POINTER
}

ASSIGNED {
  my_data
}

: Do something interesting with my_data ...
VERBATIM
static void bbcore_write(double* x, int* d, int* x_offset, int* d_offset, _threadargsproto_) {
  if (x) {
    double* x_i = x + *x_offset;
    x_i[0] = _p_my_data[0];
    x_i[1] = _p_my_data[1];
  }
  *x_offset += 2; // reserve 2 doubles on serialization buffer x
}

static void bbcore_read(double* x, int* d, int* x_offset, int* d_offset, _threadargsproto_) {
  assert(!_p_my_data);
  double* x_i = x + *x_offset;
  // my_data needs to be allocated somehow
  _p_my_data = (double*)malloc(sizeof(double)*2);
  _p_my_data[0] = x_i[0];
  _p_my_data[1] = x_i[1];
  *x_offset += 2;
}
ENDVERBATIM

If you have models with POINTER variables and user allocated memory then this requires due diligence. Below are some of the existing models adapted for CoreNEURON. These MOD files can act as a reference or you can simply reuse them if applicable:

• https://github.com/HumanBrainProject/olfactory-bulb-3d/tree/master/sim
• https://github.com/nrnhines/nrntraub/tree/master/mod
• https://github.com/suny-downstate-medical-center/M1_NEURON_paper/tree/main/mod
• https://github.com/neuronsimulator/testcorenrn/tree/master/mod
• https://github.com/neuronsimulator/reduced_dentate/tree/master/mechanisms

Have a question?

If you have any questions to make your model compatible with CoreNEURON, reach out to us via GitHub issue.