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References for 'https://github.com/BlueBrain/nmodl/pull/1260'. #18

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References for 'https://github.com/BlueBrain/nmodl/pull/1260'. #18

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66580b3
References for 'https://github.com/BlueBrain/nmodl/pull/1260'.
May 14, 2024
9d427d0
References for 'https://github.com/BlueBrain/nmodl/pull/1260'.
May 15, 2024
ae21dcd
Merge branch 'main' into nmodl-1260
JCGoran May 15, 2024
975d622
References for 'https://github.com/BlueBrain/nmodl/pull/1260'.
May 15, 2024
1c2f295
References for 'https://github.com/BlueBrain/nmodl/pull/1260'.
May 15, 2024
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10 changes: 7 additions & 3 deletions hodgkin_huxley/neuron/hodhux.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -447,7 +447,11 @@ namespace neuron {
}


inline double nrn_current_hodhux(size_t id, hodhux_Instance& inst, hodhux_NodeData& node_data, double v) {
inline double nrn_current_hodhux(_nrn_model_sorted_token const& _sorted_token, NrnThread* _nt, Memb_list* _ml_arg, int _type, size_t id, hodhux_Instance& inst, hodhux_NodeData& node_data, double v) {
_nrn_mechanism_cache_range _lmr{_sorted_token, *_nt, *_ml_arg, _type};
auto* const _ml = &_lmr;
auto* _thread = _ml_arg->_thread;
auto* _ppvar = _ml_arg->pdata[id];
double current = 0.0;
inst.ina[id] = inst.gnabar[id] * inst.m[id] * inst.m[id] * inst.m[id] * inst.h[id] * (v - inst.ena[id]);
inst.ik[id] = inst.gkbar[id] * inst.n[id] * inst.n[id] * inst.n[id] * inst.n[id] * (v - inst.ek[id]);
Expand All @@ -472,10 +476,10 @@ namespace neuron {
double v = node_data.node_voltages[node_id];
inst.ena[id] = (*inst.ion_ena[id]);
inst.ek[id] = (*inst.ion_ek[id]);
double I1 = nrn_current_hodhux(id, inst, node_data, v+0.001);
double I1 = nrn_current_hodhux(_sorted_token, _nt, _ml_arg, _type, id, inst, node_data, v+0.001);
double dina = inst.ina[id];
double dik = inst.ik[id];
double I0 = nrn_current_hodhux(id, inst, node_data, v);
double I0 = nrn_current_hodhux(_sorted_token, _nt, _ml_arg, _type, id, inst, node_data, v);
double rhs = I0;
double g = (I1-I0)/0.001;
(*inst.ion_dinadv[id]) += (dina-inst.ina[id])/0.001;
Expand Down
10 changes: 7 additions & 3 deletions net_receive/neuron/snapsyn.cpp
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Expand Up @@ -220,7 +220,11 @@ namespace neuron {
}


inline double nrn_current_SnapSyn(size_t id, SnapSyn_Instance& inst, SnapSyn_NodeData& node_data, double v) {
inline double nrn_current_SnapSyn(_nrn_model_sorted_token const& _sorted_token, NrnThread* _nt, Memb_list* _ml_arg, int _type, size_t id, SnapSyn_Instance& inst, SnapSyn_NodeData& node_data, double v) {
_nrn_mechanism_cache_range _lmr{_sorted_token, *_nt, *_ml_arg, _type};
auto* const _ml = &_lmr;
auto* _thread = _ml_arg->_thread;
auto* _ppvar = _ml_arg->pdata[id];
double current = 0.0;
inst.i[id] = inst.g[id] * (v - inst.e[id]);
current += inst.i[id];
Expand All @@ -239,8 +243,8 @@ namespace neuron {
for (int id = 0; id < nodecount; id++) {
int node_id = node_data.nodeindices[id];
double v = node_data.node_voltages[node_id];
double I1 = nrn_current_SnapSyn(id, inst, node_data, v+0.001);
double I0 = nrn_current_SnapSyn(id, inst, node_data, v);
double I1 = nrn_current_SnapSyn(_sorted_token, _nt, _ml_arg, _type, id, inst, node_data, v+0.001);
double I0 = nrn_current_SnapSyn(_sorted_token, _nt, _ml_arg, _type, id, inst, node_data, v);
double rhs = I0;
double g = (I1-I0)/0.001;
double mfactor = 1.e2/(*inst.node_area[id]);
Expand Down
26 changes: 26 additions & 0 deletions nonspecific_current/coreneuron/leonhard.cpp
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Expand Up @@ -210,6 +210,29 @@ namespace coreneuron {
}


inline int func_leonhard(int id, int pnodecount, leonhard_Instance* inst, double* data, const Datum* indexes, ThreadDatum* thread, NrnThread* nt, double v);
inline int func_with_v_leonhard(int id, int pnodecount, leonhard_Instance* inst, double* data, const Datum* indexes, ThreadDatum* thread, NrnThread* nt, double v, double arg_v);
inline int func_with_other_leonhard(int id, int pnodecount, leonhard_Instance* inst, double* data, const Datum* indexes, ThreadDatum* thread, NrnThread* nt, double v, double q);


inline int func_leonhard(int id, int pnodecount, leonhard_Instance* inst, double* data, const Datum* indexes, ThreadDatum* thread, NrnThread* nt, double v) {
int ret_func = 0;
return ret_func;
}


inline int func_with_v_leonhard(int id, int pnodecount, leonhard_Instance* inst, double* data, const Datum* indexes, ThreadDatum* thread, NrnThread* nt, double v, double arg_v) {
int ret_func_with_v = 0;
return ret_func_with_v;
}


inline int func_with_other_leonhard(int id, int pnodecount, leonhard_Instance* inst, double* data, const Datum* indexes, ThreadDatum* thread, NrnThread* nt, double v, double q) {
int ret_func_with_other = 0;
return ret_func_with_other;
}


/** initialize channel */
void nrn_init_leonhard(NrnThread* nt, Memb_list* ml, int type) {
int nodecount = ml->nodecount;
Expand Down Expand Up @@ -239,6 +262,9 @@ namespace coreneuron {

inline double nrn_current_leonhard(int id, int pnodecount, leonhard_Instance* inst, double* data, const Datum* indexes, ThreadDatum* thread, NrnThread* nt, double v) {
double current = 0.0;
func_leonhard(id, pnodecount, inst, data, indexes, thread, nt, v);
func_with_v_leonhard(id, pnodecount, inst, data, indexes, thread, nt, v, v);
func_with_other_leonhard(id, pnodecount, inst, data, indexes, thread, nt, v, inst->c[id]);
inst->il[id] = inst->c[id] * (v - 1.5);
current += inst->il[id];
return current;
Expand Down
147 changes: 144 additions & 3 deletions nonspecific_current/neuron/leonhard.cpp
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Expand Up @@ -147,6 +147,9 @@ namespace neuron {
hoc_retpushx(1.);
}
/* Mechanism procedures and functions */
inline int func_leonhard(_nrn_mechanism_cache_range* _ml, leonhard_Instance& inst, size_t id, Datum* _ppvar, Datum* _thread, NrnThread* _nt);
inline int func_with_v_leonhard(_nrn_mechanism_cache_range* _ml, leonhard_Instance& inst, size_t id, Datum* _ppvar, Datum* _thread, NrnThread* _nt, double v);
inline int func_with_other_leonhard(_nrn_mechanism_cache_range* _ml, leonhard_Instance& inst, size_t id, Datum* _ppvar, Datum* _thread, NrnThread* _nt, double q);


/** connect global (scalar) variables to hoc -- */
Expand All @@ -162,15 +165,146 @@ namespace neuron {


/* declaration of user functions */
static void _hoc_func(void);
static void _hoc_func_with_v(void);
static void _hoc_func_with_other(void);
static double _npy_func(Prop*);
static double _npy_func_with_v(Prop*);
static double _npy_func_with_other(Prop*);


/* connect user functions to hoc names */
static VoidFunc hoc_intfunc[] = {
{"setdata_leonhard", _hoc_setdata},
{"func_leonhard", _hoc_func},
{"func_with_v_leonhard", _hoc_func_with_v},
{"func_with_other_leonhard", _hoc_func_with_other},
{0, 0}
};
static NPyDirectMechFunc npy_direct_func_proc[] = {
{"func", _npy_func},
{"func_with_v", _npy_func_with_v},
{"func_with_other", _npy_func_with_other},
};
static void _hoc_func(void) {
double _r{};
Datum* _ppvar;
Datum* _thread;
NrnThread* _nt;
Prop* _local_prop = _prop_id ? _extcall_prop : nullptr;
_nrn_mechanism_cache_instance _ml_real{_local_prop};
auto* const _ml = &_ml_real;
size_t const id{};
_ppvar = _local_prop ? _nrn_mechanism_access_dparam(_local_prop) : nullptr;
_thread = _extcall_thread.data();
_nt = nrn_threads;
auto inst = make_instance_leonhard(_ml_real);
_r = 1.;
func_leonhard(_ml, inst, id, _ppvar, _thread, _nt);
hoc_retpushx(_r);
}
static double _npy_func(Prop* _prop) {
double _r{};
Datum* _ppvar;
Datum* _thread;
NrnThread* _nt;
_nrn_mechanism_cache_instance _ml_real{_prop};
auto* const _ml = &_ml_real;
size_t const id{};
_ppvar = _nrn_mechanism_access_dparam(_prop);
_thread = _extcall_thread.data();
_nt = nrn_threads;
auto inst = make_instance_leonhard(_ml_real);
_r = 1.;
func_leonhard(_ml, inst, id, _ppvar, _thread, _nt);
return(_r);
}
static void _hoc_func_with_v(void) {
double _r{};
Datum* _ppvar;
Datum* _thread;
NrnThread* _nt;
Prop* _local_prop = _prop_id ? _extcall_prop : nullptr;
_nrn_mechanism_cache_instance _ml_real{_local_prop};
auto* const _ml = &_ml_real;
size_t const id{};
_ppvar = _local_prop ? _nrn_mechanism_access_dparam(_local_prop) : nullptr;
_thread = _extcall_thread.data();
_nt = nrn_threads;
auto inst = make_instance_leonhard(_ml_real);
_r = 1.;
func_with_v_leonhard(_ml, inst, id, _ppvar, _thread, _nt, *getarg(1));
hoc_retpushx(_r);
}
static double _npy_func_with_v(Prop* _prop) {
double _r{};
Datum* _ppvar;
Datum* _thread;
NrnThread* _nt;
_nrn_mechanism_cache_instance _ml_real{_prop};
auto* const _ml = &_ml_real;
size_t const id{};
_ppvar = _nrn_mechanism_access_dparam(_prop);
_thread = _extcall_thread.data();
_nt = nrn_threads;
auto inst = make_instance_leonhard(_ml_real);
_r = 1.;
func_with_v_leonhard(_ml, inst, id, _ppvar, _thread, _nt, *getarg(1));
return(_r);
}
static void _hoc_func_with_other(void) {
double _r{};
Datum* _ppvar;
Datum* _thread;
NrnThread* _nt;
Prop* _local_prop = _prop_id ? _extcall_prop : nullptr;
_nrn_mechanism_cache_instance _ml_real{_local_prop};
auto* const _ml = &_ml_real;
size_t const id{};
_ppvar = _local_prop ? _nrn_mechanism_access_dparam(_local_prop) : nullptr;
_thread = _extcall_thread.data();
_nt = nrn_threads;
auto inst = make_instance_leonhard(_ml_real);
_r = 1.;
func_with_other_leonhard(_ml, inst, id, _ppvar, _thread, _nt, *getarg(1));
hoc_retpushx(_r);
}
static double _npy_func_with_other(Prop* _prop) {
double _r{};
Datum* _ppvar;
Datum* _thread;
NrnThread* _nt;
_nrn_mechanism_cache_instance _ml_real{_prop};
auto* const _ml = &_ml_real;
size_t const id{};
_ppvar = _nrn_mechanism_access_dparam(_prop);
_thread = _extcall_thread.data();
_nt = nrn_threads;
auto inst = make_instance_leonhard(_ml_real);
_r = 1.;
func_with_other_leonhard(_ml, inst, id, _ppvar, _thread, _nt, *getarg(1));
return(_r);
}


inline int func_leonhard(_nrn_mechanism_cache_range* _ml, leonhard_Instance& inst, size_t id, Datum* _ppvar, Datum* _thread, NrnThread* _nt) {
int ret_func = 0;
auto v = inst.v_unused[id];
return ret_func;
}


inline int func_with_v_leonhard(_nrn_mechanism_cache_range* _ml, leonhard_Instance& inst, size_t id, Datum* _ppvar, Datum* _thread, NrnThread* _nt, double v) {
int ret_func_with_v = 0;
return ret_func_with_v;
}


inline int func_with_other_leonhard(_nrn_mechanism_cache_range* _ml, leonhard_Instance& inst, size_t id, Datum* _ppvar, Datum* _thread, NrnThread* _nt, double q) {
int ret_func_with_other = 0;
auto v = inst.v_unused[id];
return ret_func_with_other;
}


void nrn_init_leonhard(_nrn_model_sorted_token const& _sorted_token, NrnThread* _nt, Memb_list* _ml_arg, int _type) {
Expand All @@ -190,8 +324,15 @@ namespace neuron {
}


inline double nrn_current_leonhard(size_t id, leonhard_Instance& inst, leonhard_NodeData& node_data, double v) {
inline double nrn_current_leonhard(_nrn_model_sorted_token const& _sorted_token, NrnThread* _nt, Memb_list* _ml_arg, int _type, size_t id, leonhard_Instance& inst, leonhard_NodeData& node_data, double v) {
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Setting up _lmr should happen outside the loop. nrn_current_* is a function that's only called in the loop body of nrn_cur_*.

It might be better to pass it in.

_nrn_mechanism_cache_range _lmr{_sorted_token, *_nt, *_ml_arg, _type};
auto* const _ml = &_lmr;
auto* _thread = _ml_arg->_thread;
auto* _ppvar = _ml_arg->pdata[id];
double current = 0.0;
func_leonhard(_ml, inst, id, _ppvar, _thread, _nt);
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This seems to be a sensible set of stuff to also pass into nrn_current_leonhard:

_ml, inst, id, _ppvar, _thread, _nt

(plus specific things).

func_with_v_leonhard(_ml, inst, id, _ppvar, _thread, _nt, v);
func_with_other_leonhard(_ml, inst, id, _ppvar, _thread, _nt, inst.c[id]);
inst.il[id] = inst.c[id] * (v - 1.5);
current += inst.il[id];
return current;
Expand All @@ -209,8 +350,8 @@ namespace neuron {
for (int id = 0; id < nodecount; id++) {
int node_id = node_data.nodeindices[id];
double v = node_data.node_voltages[node_id];
double I1 = nrn_current_leonhard(id, inst, node_data, v+0.001);
double I0 = nrn_current_leonhard(id, inst, node_data, v);
double I1 = nrn_current_leonhard(_sorted_token, _nt, _ml_arg, _type, id, inst, node_data, v+0.001);
double I0 = nrn_current_leonhard(_sorted_token, _nt, _ml_arg, _type, id, inst, node_data, v);
double rhs = I0;
double g = (I1-I0)/0.001;
node_data.node_rhs[node_id] -= rhs;
Expand Down