Fix third-factor plasticity buffering and add third factor plasticity…

… tutorial

pynestml/codegeneration/resources_nest/point_neuron/common/SynapseHeader.h.jinja2

@@ -79,7 +79,7 @@ along with NEST. If not, see <http://www.gnu.org/licenses/>.
 **/
 
 // uncomment the next line to enable printing of detailed debug information
-// #define DEBUG
+#define DEBUG
 
 namespace nest
 {
@@ -740,13 +740,6 @@ void get_entry_from_continuous_variable_history(double t,
 
 {%- if paired_neuron_name is not none and paired_neuron_name|length > 0 %}
  double timestep = 0;
- /**
- * Updates due to post-synaptic spikes since last pre-synaptic spike.
- *
- * t_lastspike_ contains the point in time of the last spike. So we read the history (t_last_spike - dendritic_delay, ..., __t_spike - dendritic_delay]
- *
- * Note that this also increases the access counter for these entries which is used to prune the history.
- **/
  std::deque< histentry__{{ paired_neuron_name }} >::iterator start;
  std::deque< histentry__{{ paired_neuron_name }} >::iterator finish;
  {
@@ -758,14 +751,12 @@ void get_entry_from_continuous_variable_history(double t,
  * Note that this also increases the access counter for these entries which is used to prune the history.
  **/
 
- // get spike history in relevant range (t1, t2] from post-synaptic neuron
- std::deque< histentry__{{ paired_neuron_name }} >::iterator start;
- std::deque< histentry__{{ paired_neuron_name }} >::iterator finish;
 {%- if vt_ports is defined and vt_ports|length > 0 %}
  double t0 = t_last_update_;
 {%- endif %}
 
 {%- if paired_neuron_name is not none and paired_neuron_name|length > 0 and paired_neuron.state_vars_that_need_continuous_buffering | length > 0 and continuous_state_buffering_method == "continuous_time_buffer" %}
+ // get continuous-time history entries in relevant range (t1, t2] from post-synaptic neuron
  std::deque< continuous_variable_histentry_{{ paired_neuron_name }} >::iterator continuous_history_start;
  std::deque< continuous_variable_histentry_{{ paired_neuron_name }} >::iterator continuous_history_finish;
 
@@ -775,6 +766,7 @@ void get_entry_from_continuous_variable_history(double t,
  &continuous_history_finish );
 {%- endif %}
 
+ // get spike history entries in relevant range (t1, t2] from post-synaptic neuron
  __target->get_history__( t_lastspike_ - __dendritic_delay,
  __t_spike - __dendritic_delay,
  &start,
@@ -802,7 +794,6 @@ void get_entry_from_continuous_variable_history(double t,
 {%- endfor %}
 {%- endif %}
 
-
 {#- continuous time based: grab the entry from the postsynaptic continuous time history buffer #}
 {%- if continuous_state_buffering_method == "continuous_time_buffer" %}
 #ifdef DEBUG
@@ -907,13 +898,13 @@ void get_entry_from_continuous_variable_history(double t,
 {{ printer._expression_printer._simple_expression_printer._variable_printer.set_getter_string("((post_neuron_t*)(__target))->get_%s(_tr_t)") }}
 {%- for pre_port in pre_ports %}
 
-  /**
-  * NESTML generated onReceive code block for presynaptic port "{{pre_port}}" begins here!
-  **/
-{%- if synapse.get_on_receive_block(pre_port) %}
+ /**
+ * NESTML generated onReceive code block for presynaptic port "{{ pre_port }}" begins here!
+ **/
+{%  if synapse.get_on_receive_block(pre_port) %}
 {%- set dynamics = synapse.get_on_receive_block(pre_port) %}
 {%- with ast = dynamics.get_block() %}
-{%- filter indent(8, True) %}
+{%- filter indent(6, True) %}
 {%- include "directives_cpp/Block.jinja2" %}
 {%- endfilter %}
 {%- endwith %}
@@ -926,7 +917,7 @@ void get_entry_from_continuous_variable_history(double t,
  * update all convolutions with pre spikes
  **/
 
-{%- for spike_updates_for_port in spike_updates.values() %}
+{%  for spike_updates_for_port in spike_updates.values() %}
 {%- for spike_update in spike_updates_for_port %}
  {{ printer.print(spike_update.get_variable()) }} += 1.; // XXX: TODO: increment with initial value instead of 1
 {%- endfor %}
@@ -939,9 +930,9 @@ void get_entry_from_continuous_variable_history(double t,
 
  if (pre_before_post_flag)
  {
-  auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model
+ auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model
 
-{%- if paired_neuron_name is not none and paired_neuron_name|length > 0 and paired_neuron.state_vars_that_need_continuous_buffering | length > 0 %}
+{%  if paired_neuron_name is not none and paired_neuron_name|length > 0 and paired_neuron.state_vars_that_need_continuous_buffering | length > 0 %}
 
  /**
  * grab state variables from the postsynaptic neuron (at the "current" simulation time, which corresponds to t_pre)

pynestml/codegeneration/resources_nest/point_neuron/directives_cpp/PredefinedFunction_emit_spike.jinja2

@@ -4,11 +4,8 @@
 #}
 {%- if tracing %}/* generated by {{self._TemplateReference__context.name}} */ {% endif %}
 
-/**
- * generated code for emit_spike() function
-**/
-
-{% if ast.get_args() | length == 0 %}
+// begin generated code for emit_spike() function
+{% if ast.get_args() | length == 0 %}
 {#- no parameters -- emit_spike() called from within neuron #}
 #ifdef DEBUG
 std::cout << "Emitting a spike at t = " << nest::Time(nest::Time::step(origin.get_steps() + lag + 1)).get_ms() << "\n";
@@ -28,3 +25,4 @@ e.set_delay_steps( get_delay_steps() );
 e.set_rport( get_rport() );
 e();
 {%- endif %}
+// end generated code for emit_spike() function