File & data formats¶

.syn¶

ISF custom file format to store synapse locations onto a morphology. Only valid with an associated morphology .hoc file.

For each synapse, it provides the synapse type and location onto the morphology. Each row index corresponds to its synapse ID, providing a link to a corresponding .con file. The location is encoded as a section ID and x (a normalized distance along the section), to be consistent with NEURON syntax.

To create a functional network (i.e., known presynaptic origin), it must be used in conjunction with an associated .con file.

Example:

# Synapse distribution file
# corresponding to cell: morphology.hoc
# Type - section - section.x

VPM_E1  112     0.138046479525
VPM_E1  130     0.305058053119
VPM_E1  130     0.190509288017
VPM_E1  9       0.368760777084
VPM_E1  110     0.0
VPM_E1  11      0.120662910562
...

.con¶

ISF custom file format to store connectivity data. To be used in conjunction with an associated .syn file and morphology .hoc file. It numbers each synapse, and links it to its associated presynaptic cell type and ID. While a .syn file and .hoc file provide the anatomical realization of a morphology embedding into a network, the addition of a .con file makes possible to construct a functional realization, as it allows linking the synapses to presynaptic cells of a dense connectome model, which in turn allows to assign cell type specific activation patterns to each synapse. ISF’s workflow is designed to create these files in tandem, so they always co-exist.

Example:

# Anatomical connectivity realization file; only valid with synapse realization:
# synapse_ralization_file.syn
# Type - cell ID - synapse ID

L6cc_A3 0       0
L6cc_A3 1       1
L6cc_A3 2       2
L6cc_A3 3       3
L6cc_A3 4       4
L6cc_A3 4       5
...

.param¶

ISF custom file format to save JSON-like ASCII data for cell parameters, network parameters, and activity data. The .param format is valid Python code, but differs from JSON, as it allows trailing comma’s, single quotes, and tuples. JSON does not. All .param files can be read using single_cell_parser.build_parameters. However, specific files that use this format have more specialized readers, which provide additional for Cell parameters and Network parameters.

Both the Cell parameters and the Network parameters are used as inputs for multi-scale simulations using simrun.

See also:

simrun.parameters_to_cell to rerun a simulation from these parameterfiles.

Cell parameters¶

.param file to store biophysical parameters of a cell. Includes a reference to a .hoc morphology file, biophysical properties of the cell per cellular structure (i.e. soma, dendrite, axon initial segment …), and basic simulation parameters. Conductance densities are given in \(S/cm^2\), spatial coordinates and distances in \(\mu m\), and time in \(ms\). Simulation parameters are usually overridden by higher level modules, such as simrun.

To access different structures of a cell:

>>> cell_parameters.neuron.keys()
['Myelin', 'Soma', 'AIS', 'filename', 'Dendrite', 'ApicalDendrite']

Example:

{
    'info': {...},
    'neuron': {
        'filename': 'getting_started/example_data/anatomical_constraints/*.hoc',
        'Soma': {
            'properties': {
                'Ra': 100.0,
                'cm': 1.0,
                'ions': {'ek': -85.0, 'ena': 50.0}
                },
            'mechanisms': {
                'global': {},
                'range': {
                    'pas': {
                        'spatial': 'uniform',
                        'g': 3.26e-05,
                        'e': -90},
                    'Ca_LVAst': {
                        'spatial': 'uniform',
                        'gCa_LVAstbar': 0.00462},
                    'Ca_HVA': {...},
                    ...,}}},
        'Dendrite': {...},
        'ApicalDendrite': {...},
        'AIS': {...},
        'Myelin': {...},
        'cell_modify_functions': {
            'scale_apical': {'scale': 2.1}
        },
    'sim': {
        'Vinit': -75.0,
        'tStart': 0.0,
        'tStop': 250.0,
        'dt': 0.025,
        'T': 34.0,
        'recordingSites': ['getting_started/example_data/apical_proximal_distal_rec_sites.landmarkAscii']}
}

Network parameters¶

The .param format is used to store network parameters, describing the presynaptic cells and their synaptic activations. Only valid with an associated .hoc morphology file, .syn file, and .con file.

For each presynaptic cell type in the network, this following information is provided:

The synapses key of each presynaptic cell type contains the following information:

Each synapses.receptors.<receptor type> in synapses.receptors contains the following information:

See also:

The receptor parameters are used for NEURON’s NetCon.

Example:

{
"info": {
  "date": "11Feb2015",
  "name": "evoked_activity",
  "author": "name",
},
"network": {
  "cell_type_1": {
    "cellNr": 20
    "celltype": {
      'pointcell': {
        'distribution': 'PSTH_poissontrain',
        'intervals': [(0, 274.7), (274.7, 295), (295, 945), (945, 1145)],
        'offset': 0.0,
        'rates': [1.4357770278148136, 1.0890981376857083, 1.7588271630292192, 1.4357770278148136]
      }
    },
    "synapses": {
      "receptors": {
        'glutamate_syn': {
          'delay': 0.0,
          'parameter': {
            'decayampa': 1.0,
            'decaynmda': 1.0,
            'facilampa': 0.0,
            'facilnmda': 0.0,
            'tau1': 26.0,
            'tau2': 2.0,
            'tau3': 2.0,
            'tau4': 0.1},
          'threshold': 0.0,
          'weight': [1.5480934081344324, 1.5480934081344324]
          }
      },
      "releaseProb": 0.5,
      "connectionFile": "presyn_cells.con",
      "distributionFile": "syn_locations.syn"
    }
  },
  "cell_type_2": {...},
  ...
}

Activity data¶

.param files are used to store activity data covering spike times and time bins for specific cell types in response to a stimulus, as seen in e.g. getting_started/example_data/functional_constraints/evoked_activity/

Example:

{
"L4ss_B1": {
"distribution": "PSTH",
"intervals": [(0.0,1.0),(1.0,2.0),(2.0,3.0),(3.0,4.0),(4.0,5.0),(5.0,6.0),(6.0,7.0),(7.0,8.0),(8.0,9.0),(9.0,10.0),(10.0,11.0),(11.0,12.0),(12.0,13.0),(13.0,14.0),(14.0,15.0),(15.0,16.0),(16.0,17.0),(17.0,18.0),(18.0,19.0),(19.0,20.0),(20.0,21.0),(21.0,22.0),(22.0,23.0),(23.0,24.0),(24.0,25.0),(25.0,26.0),(26.0,27.0),(27.0,28.0),(28.0,29.0),(29.0,30.0),(30.0,31.0),(31.0,32.0),(32.0,33.0),(33.0,34.0),(34.0,35.0),(35.0,36.0),(36.0,37.0),(37.0,38.0),(38.0,39.0),(39.0,40.0),(40.0,41.0),(41.0,42.0),(42.0,43.0),(43.0,44.0),(44.0,45.0),(45.0,46.0),(46.0,47.0),(47.0,48.0),(48.0,49.0),(49.0,50.0)],
"probabilities": [-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,0.0062,0.0062,-0.0004,0.0129,0.0062,-0.0004,-0.0004,0.0062,-0.0004,-0.0004,-0.0004,0.0062,0.0062,-0.0004,-0.0004,-0.0004],
},
"L4ss_B2": {
"distribution": "PSTH",
"intervals": [(0.0,1.0),(1.0,2.0),(2.0,3.0),(3.0,4.0),(4.0,5.0),(5.0,6.0),(6.0,7.0),(7.0,8.0),(8.0,9.0),(9.0,10.0),(10.0,11.0),(11.0,12.0),(12.0,13.0),(13.0,14.0),(14.0,15.0),(15.0,16.0),(16.0,17.0),(17.0,18.0),(18.0,19.0),(19.0,20.0),(20.0,21.0),(21.0,22.0),(22.0,23.0),(23.0,24.0),(24.0,25.0),(25.0,26.0),(26.0,27.0),(27.0,28.0),(28.0,29.0),(29.0,30.0),(30.0,31.0),(31.0,32.0),(32.0,33.0),(33.0,34.0),(34.0,35.0),(35.0,36.0),(36.0,37.0),(37.0,38.0),(38.0,39.0),(39.0,40.0),(40.0,41.0),(41.0,42.0),(42.0,43.0),(43.0,44.0),(44.0,45.0),(45.0,46.0),(46.0,47.0),(47.0,48.0),(48.0,49.0),(49.0,50.0)],
"probabilities": [-0.0004,0.0062,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,0.0062,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004,0.0062,-0.0004,-0.0004,0.0129,0.0062,0.0062,-0.0004,-0.0004,-0.0004,-0.0004,0.0062,-0.0004,-0.0004,0.0062,-0.0004,-0.0004,-0.0004,-0.0004,-0.0004],
},
...
}

Dataframes¶

The output format of various simulation pipelines are usually a dataframe. below, you find common formats used throughout ISF.

The simrun package produces output files in .csv or .npz format. many of these files need to be created for each individual simulation trial. These raw output files are usually parsed into single dataframes for further analysis using a db_initializers submodule (see e.g. load_simrun_general).

Synapse activation¶

The raw output of the simrun package contains .csv files containing the synaptic activations onto a post-synaptic cell for each individual simulation trial. Each file contains the following information for each synapse during a particular simulation trial:

• type

• ID (for identifying the corresponding presynaptic cell)

• location (section ID, section pt ID, soma distance)

• dendrite label (e.g. "ApicalDendrite")

• activation times

These individual files are usually gathered and parsed into a single dataframe containing all trials for further analysis: An example of the raw and parsed format is shown below:

Raw simrun output¶

Parsed dataframe¶

Presynaptic spike times¶

The raw output of the simrun package contains .csv files containing the spike times of presynaptic cells for each individual simulation trial. Each file contains the following information for each synapse during a particular simulation trial:

• type

• ID (for identifying the corresponding synapse, and cell location)

• activation times

These individual files are usually gathered and parsed into a single dataframe containing all trials for further analysis An example of the raw and parsed format is shown below:

Raw simrun output¶

Parsed dataframe¶

Writers:

• write_presynaptic_spike_file() is used by simrun and synanalysis

  to write raw output data.

• data_base.db_initializers.load_simrun_general.init() parses these files into a pandas dataframe.

Voltage traces¶

The raw output of the simrun package contains .npz or .csv files containing the voltage traces of the postsynaptic cells. Unlike the synapse activations and spike times, it is possible for one such file to contain multiple trials.

Voltage trace .csv¶

Voltage trace .npz¶

vm_all_traces.npz:

array([[100.0, 100.025, 100.05, 100.075],
       [-61.4607218758, -61.4665809176, -61.4735021526, -61.4814187507],
       [-55.1366909604, -55.1294343391, -55.1223216173, -55.1153403448],
       [-67.1747143695, -67.1580037786, -67.1424366078, -67.1279980017]])

Voltage trace dataframe¶

The parsed dataframe is usually created by the data_base.db_initializers.load_simrun_general.init() function.

.hoc¶

NEURON [7] file format for neuron morphologies. refer to the NEURON hoc documentation for more info. The morphology is specified as a tree structure: the different sections of a neuron (pieces separated between branching points or branches endings) are connected in order. Each section is formed by a set of points, defined by their 3D coordinates and the diameter of the neuron structure at that point (pt3dadd -> x, y, z, diameter).

Readers:

• cell_parser

• read_hoc_file()

Example:

{create soma}
{access soma}
{nseg = 1}
{pt3dclear()}
{pt3dadd(1.933390,221.367004,-450.045990,12.542000)}
{pt3dadd(2.321820,221.046997,-449.989990,13.309400)}
...
{pt3dadd(13.961900,210.149002,-447.901001,3.599700)}

{create BasalDendrite_1_0}
{connect BasalDendrite_1_0(0), soma(0.009696)}
{access BasalDendrite_1_0}
{nseg = 1}
{pt3dclear()}
{pt3dadd(6.369640, 224.735992, -452.399994, 2.040000)}
{pt3dadd(6.341550, 222.962997, -451.906006, 2.040000)}
...

.mod¶

NEURON [7] file format for neuron mechanisms. refer to the NEURON NMODL documentation for more info. Used to define channel and synapse dynamics in NEURON simulations. See the folder mechanisms in the project source.

.am¶

The Amira proprietary VTK-like file format. Refer to the amira documentation for more information. This flexible format can be used to store 3D scalar meshes, 3D neuron morphology reconstructions, slice image data etc.

Readers:

• read_scalar_field

• read_landmark_file