Simulations¶
After building the scaffold models, simulations can be run using NEST or NEURON.
Simulations can be configured in the simulations dictionary of the root node of the
configuration file, specifying each simulation with its name, e.g. “first_simulation”, “second_simulation”:
{
"simulations": {
"first_simulation": {
},
"second_simulation": {
}
}
}
NEST¶
NEST is mainly used for simulations of Spiking Neural Networks, with point neuron models.
Configuration¶
NEST simulations in the scaffold can be configured setting the attribute simulator to nest.
The basic NEST simulation properties can be set through the attributes:
default_neuron_model: default model used for allcell_models, unless differently indicated in theneuron_modelattribute of a specific cell model.default_synapse_model: default model used for allconnection_models(e.g.static_synapse), unless differently indicated in thesynapse_modelattribute of a specific connection model.duration: simulation duration in [ms].modules: list of NEST extension modules to be installed.
Then, the dictionaries cell_models, connection_models, devices, entities specify the properties of each element of the simulation.
{
"simulations": {
"first_simulation": {
"simulator": "nest",
"default_neuron_model": "iaf_cond_alpha",
"default_synapse_model": "static_synapse",
"duration": 1000,
"modules": ["cerebmodule"],
"cell_models": {
},
"connection_models": {
},
"devices": {
},
"entities": {
}
},
"second_simulation": {
}
}
}
Cells¶
In the cell_models attribute, it is possible to specify simulation-specific properties for each cell type:
cell_model: NEST neuron model, if not using thedefault_neuron_model. Currently supported models areiaf_cond_alphaandeglif_cond_alpha_multisyn. Other available models can be found in the NEST documentationparameters: neuron model parameters that are common to the NEST neuron models that could be used, including: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]
Then, neuron model specific parameters can be indicated in the attributes corresponding to the model names:
iaf_cond_alpha:I_e: endogenous current [pA]tau_syn_ex: time constant of excitatory synaptic inputs [ms]tau_syn_in: time constant of inhibitory synaptic inputs [ms]g_L: leaky conductance [nS]
eglif_cond_alpha_multisyn:Vmin: minimum membrane potential [mV]Vinit: initial membrane potential [mV]lambda_0: escape rate parametertau_V: escape rate parametertau_m: membrane time constant [ms]I_e: endogenous current [pA]kadap: adaptive current coupling constantk1: spike-triggered current decayk2: adaptive current decayA1: spike-triggered current update [pA]A2: adaptive current update [pA]tau_syn1,tau_syn2,tau_syn3: time constants of synaptic inputs at the 3 receptors [ms]E_rev1,E_rev2,E_rev3: reversal potential for the 3 synaptic receptors (usually set to 0mV for excitatory and -80mV for inhibitory synapses) [mV]receptors: dictionary specifying the receptor number for each input cell to the current neuron
Example¶
Configuration example for a cerebellar Golgi cell. In the eglif_cond_alpha_multisyn neuron model, the 3 receptors are associated to synapses from glomeruli, Golgi cells and Granule cells, respectively.
{
"cell_models": {
"golgi_cell": {
"parameters": {
"t_ref": 2.0,
"C_m": 145.0,
"V_th": -55.0,
"V_reset": -75.0,
"E_L": -62.0
},
"iaf_cond_alpha": {
"I_e": 36.75,
"tau_syn_ex": 0.23,
"tau_syn_in": 10.0,
"g_L": 3.3
},
"eglif_cond_alpha_multisyn": {
"Vmin": -150.0,
"Vinit": -62.0,
"lambda_0": 1.0,
"tau_V":0.4,
"tau_m": 44.0,
"I_e": 16.214,
"kadap": 0.217,
"k1": 0.031,
"k2": 0.023,
"A1": 259.988,
"A2":178.01,
"tau_syn1":0.23,
"tau_syn2": 10.0,
"tau_syn3": 0.5,
"E_rev1": 0.0,
"E_rev2": -80.0,
"E_rev3": 0.0,
"receptors": {
"glomerulus": 1,
"golgi_cell": 2,
"granule_cell": 3
}
}
}
}
}
Connections¶
Simulations with plasticity¶
The default synapse model for connection models is usually set to static_synapse.
For plastic synapses, it is possible to choose between:
homosynaptic plasticity models (e.g.
stdp_synapse) where weight changes depend on pre- and postsynaptic spike timesheterosynaptic plasticity models (e.g.
stdp_synapse_sinexp), where spikes of an external teaching population trigger the weight change. In this case, a device called “volume transmitter” is created for each postsynaptic neuron, collecting the spikes from the teaching neurons.
For a full set of available synapse models, see the NEST documentation
For the plastic connections, specify the attributes as follows:
plastic: set totrue.hetero: set totrueif using an heterosynaptic plasticity model.teaching: Connection model name of the teaching connection for heterosynaptic plasticity models.synapse_model: the name of the NEST synapse model to be used. By default, it is the model specified in thedefault_synapse_modelattribute of the current simulation.synapse: specify the parameters for each one of the synapse models that could be used for that connection.
Note
If the synapse_model attribute is not specified, the default_synapse_model will
be used (static). Using synapse models without plasticity - such as static -
while setting the plastic attribute to true will lead to errors.
Example¶
{
"connection_models": {
"parallel_fiber_to_purkinje": {
"plastic": true,
"hetero": true,
"teaching": "io_to_purkinje",
"synapse_model": "stdp_synapse_sinexp",
"connection": {
"weight": 0.007,
"delay": 5.0
},
"synapse": {
"static_synapse": {},
"stdp_synapse_sinexp": {
"A_minus": 0.5,
"A_plus": 0.05,
"Wmin": 0.0,
"Wmax": 100.0
}
}
},
"purkinje_to_dcn": {
"plastic": true,
"synapse_model": "stdp_synapse",
"connection": {
"weight":-0.4,
"delay": 4.0
},
"synapse": {
"static_synapse": {},
"stdp_synapse": {
"tau_plus":30.0,
"alpha": 0.5,
"lambda": 0.1,
"mu_plus": 0.0,
"mu_minus": 0.0,
"Wmax": 100.0
}
}
}
}
}