"1. Whether to replace non-simulated areas by Poisson sources with the same global rate rate_ext ('hom_poisson_stat') or by specific rates ('het_poisson_stat') or by time-varying specific current ('het_current_nonstat'). In the two latter cases, the data to replace the cortico-cortical input is loaded from `replace_cc_input_source`\n",
"2. `g` controls the excitation to inhibition balance in network's activity.\n",
"3. Whether to apply the stabilization method of Schuecker, Schmidt et al. (2017). Default is None. Options are True to perform the stabilization or a string that specifies the name of a binary numpy file containing the connectivity matrix.\n",
"4. This scaling factor controls the cortico-cortical synaptic strength. If it is 1.0 then the inter-area synaptic strength is the same as the intra-areal. This factor changes the network activity from ground to metastable."
]
]
},
},
{
{
...
@@ -221,7 +228,7 @@
...
@@ -221,7 +228,7 @@
"source": [
"source": [
"| Parameter | Parameter description | Variable | Value | Value description |\n",
"| Parameter | Parameter description | Variable | Value | Value description |\n",
"Simulation parameters define the paramters that influence the process of simulation, inlcuding the simulation time, the number of processes and theads used to run the simulation.<br>"
"Simulation parameters define the paramters that influence the process of simulation, inlcuding the simulation time, the number of processes and theads used to run the simulation.<br>"
**Scaling factor** (scale_down_to) is the parameter which defines the the ratio of the full scale multi-area model being down-scaled to a model with fewer neurons and indegrees so as to be simulated on machines with lower computational ability and the simulation results can be obtained within relative shorter period of time.<br><br>
**Scaling factor** (scale_down_to) is the parameter which defines the the ratio of the full scale multi-area model being down-scaled to a model with fewer neurons and indegrees so as to be simulated on machines with lower computational ability and the simulation results can be obtained within relative shorter period of time.<br><br>
Neurons and indegrees are both scaled down to 0.5%, where the model can usually be simulated on a local machine.<br>**Warning**: This will not yield reasonable dynamical results from the network and is only meant to demonstrate the simulation workflow.**
Neurons and indegrees are both scaled down to 0.5%, where the model can usually be simulated on a local machine.<br>**Warning**: This will not yield reasonable dynamical results from the network and is only meant to demonstrate the simulation workflow.**
### 2. Model parameters <a class="anchor" id="section_4.2"></a>
### 2. Model parameters <a class="anchor" id="section_4.2"></a>
Model paramters are most important among all the paramters, it directly affect the model itself and thus have a great impact on the simulation results. Model paramters define the connection, input, neuron, and network charateristics of the model, and therefore fall into four categories: **Connection paramters**, **Input paramters**, **Neuron paramters**, and **Network paramters**.
Model paramters are most important among all the paramters, it directly affect the model itself and thus have a great impact on the simulation results. Model paramters define the connection, input, neuron, and network charateristics of the model, and therefore fall into four categories: **Connection paramters**, **Input paramters**, **Neuron paramters**, and **Network paramters**.
1. Whether to replace non-simulated areas by Poisson sources with the same global rate rate_ext ('hom_poisson_stat') or by specific rates ('het_poisson_stat') or by time-varying specific current ('het_current_nonstat'). In the two latter cases, the data to replace the cortico-cortical input is loaded from `replace_cc_input_source`
2.`g` controls the excitation to inhibition balance in network's activity.
3. Whether to apply the stabilization method of Schuecker, Schmidt et al. (2017). Default is None. Options are True to perform the stabilization or a string that specifies the name of a binary numpy file containing the connectivity matrix.
4. This scaling factor controls the cortico-cortical synaptic strength. If it is 1.0 then the inter-area synaptic strength is the same as the intra-areal. This factor changes the network activity from ground to metastable.
Simulation parameters define the paramters that influence the process of simulation, inlcuding the simulation time, the number of processes and theads used to run the simulation.<br>
Simulation parameters define the paramters that influence the process of simulation, inlcuding the simulation time, the number of processes and theads used to run the simulation.<br>
The connectivity and neuron numbers are stored in the attributes of the model class. Neuron numbers are stored in `M.N` as a dictionary (and in `M.N_vec` as an array), indegrees in `M.K` as a dictionary (and in `M.K_matrix` as an array). Number of synapses can also be access via `M.synapses` (and in `M.syn_matrix` as an array).
The connectivity and neuron numbers are stored in the attributes of the model class. Neuron numbers are stored in `M.N` as a dictionary (and in `M.N_vec` as an array), indegrees in `M.K` as a dictionary (and in `M.K_matrix` as an array). Number of synapses can also be access via `M.synapses` (and in `M.syn_matrix` as an array).
The following instructions will work when the `simulate` parameter is set to `True` during the creation of the MultiAreaModel object, and the `M.simulation.simulate()` method is executed.
The following instructions will work when the `simulate` parameter is set to `True` during the creation of the MultiAreaModel object, and the `M.simulation.simulate()` method is executed.
%% Cell type:code id:dc3b1820 tags:
%% Cell type:code id:dc3b1820 tags:
``` python
``` python
# Uncomment the lines in this code cell below to test if the number of synapses created by NEST matches the expected values
# Uncomment the lines in this code cell below to test if the number of synapses created by NEST matches the expected values
# """
# """
# Test if the correct number of synapses has been created.
# Test if the correct number of synapses has been created.
# syn = M.synapses[target_area.name][target_pop][source_area.name][source_pop]
# syn = M.synapses[target_area.name][target_pop][source_area.name][source_pop]
# assert(len(created_syn) == int(syn))
# assert(len(created_syn) == int(syn))
```
```
%% Cell type:markdown id:57401110 tags:
%% Cell type:markdown id:57401110 tags:
To obtain the connections information, you can extract the lists of connected sources and targets. Moreover, you can access additional synaptic details, such as synaptic weights and delays.
To obtain the connections information, you can extract the lists of connected sources and targets. Moreover, you can access additional synaptic details, such as synaptic weights and delays.
You can determine the area and subpopulation to which the neuron ID ranges belong by referring to the file `network_gids.txt`, which is automatically generated during network creation.
You can determine the area and subpopulation to which the neuron ID ranges belong by referring to the file `network_gids.txt`, which is automatically generated during network creation.
