Simulate using NEST¶

NEST is a simulation tool used in computational neuroscience for modeling and studying the behavior of large networks of point neurons.

To install the simulator, please refer to the installation guide.

The simulation block contains all the necessary details to run the simulation. In the configuration, you will need to specify a name and duration (in milliseconds). For example, the following creates a simulation named my_simulation_name with a duration of 1 second:

JSON

"simulations": {
    "my_simulation_name": {
      "simulator": "nest",
      "duration": 1000,
}

PYTHON

config.simulations.add("my_simulation_name",
  simulator="nest",
  duration=1000,
)

Cell Models¶

In the cell_models block, you specify the NEST representation for each cell type. Each key in the block can have the following attributes:

model: NEST neuron model, See the available models in the NEST documentation

constants: parameters that are defined in the NEST neuron model. Most models include the following parameters:

t_ref: refractory period duration [ms]

C_m: membrane capacitance [pF]

V_th: threshold potential [mV]

V_reset: reset potential [mV]

E_L: leakage potential [mV]

For example if you have a network with two cell types:

JSON

"cell_models": {
   "cell_type_one": {
     "model": "first_nest_model",
     "constants": { "t_ref": 2, "C_m": 15}
   },
   "cell_type_two": {
     "model": "another_nest_name",
     "constants": {"t_ref": 1}
   }
 },

PYTHON

config.simulations["my_simulation_name"].cell_models=dict(
  cell_type_one={"model":"first_nest_model",constants={ "t_ref": 2, "C_m": 15}},
  cell_type_two={"model":"another_nest_name",constants={ "t_ref": 1}}
)

For the first cell type, we assign the corresponding NEST neuron model named first_nest_model, with a refractory period of 2 ms and a membrane capacitance of 15 pF. For cell_type_two, we use the NEST model named another_nest_name, setting a different refractory period.

To select the most suitable model and its parameters, the NEST website provides a useful model list.

Connection Models¶

In this block, you need to define a NEST connection module for each ConnectivitySet in your network. The bsb-nest interface provides a NestConnection module that handles the set by passing arrays of pre-synaptic and post-synaptic cells, using a one_to_one connection. You will need to specify the synapse configuration using the key synapse, providing all required properties as a dictionary.

The available keys in the synapse specification dictionary include:

model : Specifies NEST synapse_model.

weight : float that defines a weight for the synapse (required).

delay : float that defines a delay (required).

receptor_type : int that identifies NEST receptor types. For more details see the receptors section .

constants : Any parameters specific to the selected synapse model.

JSON

"connection_models": {
   "connect_A_to_B": {
     "synapse" : {
       "model": "static_synapse",
       "weight": 0.5,
       "delay": 1
     }
   },
   "connect_B_to_A": {
     "synapse": {
       "model": "bernoulli_synapse",
       "weight": 1,
       "delay": 1,
       "constants":{"p_transmit":0.5}
     }
   }
 },

PYTHON

config.simulations["my_simulation_name"].connection_models=dict(
  connect_A_to_B=dict(synapse=dict(
      model="static_synapse",
      weight=0.5,
      delay=1
      )
  )
  connect_B_to_A=dict(synapse=dict(
      model="bernoulli_synapse",
      weight=1,
      delay=1,
      constants={"p_transmit":0.5}
      )
  )
)

In this example, for the ConnectivitySet named connect_A_to_B, we assign a static_synapse, while for the second set, a bernoulli_synapse is chosen. All available built-in synapse models are listed in the NEST guide.

Simulate using NEST¶

Cell Models¶

Connection Models¶

Devices¶

Stimulation devices¶

Create a DC Generator¶

Create a Poisson Generator¶

Recording Devices¶

Add a Spike Recorder¶

Add a Multimeter¶