pynn_brainscales.brainscales2.populations.Population

class pynn_brainscales.brainscales2.populations.Population(size, cellclass, cellparams=None, structure=None, initial_values={}, label=None)

Bases: pyNN.common.populations.Population

A group of neurons all of the same type. “Population” is used as a generic term intended to include layers, columns, nuclei, etc., of cells.

Arguments:

size:: number of cells in the Population. For backwards-compatibility, size may also be a tuple giving the dimensions of a grid, e.g. size=(10,10) is equivalent to size=100 with structure=Grid2D().
cellclass:: a cell type (a class inheriting from pyNN.models.BaseCellType).
cellparams:: a dict, or other mapping, containing parameters, which is passed to the neuron model constructor.
structure:: a pyNN.space.Structure instance, used to specify the positions of neurons in space.
initial_values:: a dict, or other mapping, containing initial values for the neuron state variables.
label:: a name for the population. One will be auto-generated if this is not supplied.

__init__(size, cellclass, cellparams=None, structure=None, initial_values={}, label=None): Create a population of neurons all of the same type.

Methods

`describe`(args, *kwargs)	Returns a human-readable description of the population.
`find_units`(variable)	Returns units of the specified variable or parameter, as a string.
`get_plasticity_data`(observable)	Get recorded observable data for this population from plasticity rule. :raises RuntimeError: On no plasticity rule or requested observable name present :param observable: Observable name to get data for :returns: Observable data per plasticity rule execution period per neuron.
`record`(variables[, to_file, …])	Record the specified variable or variables for all cells in the Population or view.

Attributes

`actual_hwparams`
`calib_hwparams`
`calib_target`
`changed_since_last_run`

property actual_hwparams

all_cells: numpy.ndarray

property calib_hwparams

property calib_target

changed_since_last_run = True

describe(*args, **kwargs)

Returns a human-readable description of the population.

The output may be customized by specifying a different template together with an associated template engine (see pyNN.descriptions).

If template is None, then a dictionary containing the template context will be returned.

find_units(variable): Returns units of the specified variable or parameter, as a string. Works for all the recordable variables and neuron parameters of all standard models.

get_plasticity_data(observable: str)

Get recorded observable data for this population from plasticity rule. :raises RuntimeError: On no plasticity rule or requested observable

name present

Parameters: observable – Observable name to get data for
Returns: Observable data per plasticity rule execution period per neuron

record(variables: Union[str, List[str]], to_file: Optional[Union[str, neo.io.nixio.NixIO]] = None, sampling_interval: Optional[float] = None, locations: Optional[List[str]] = None, *, device: str = 'madc') → None

Record the specified variable or variables for all cells in the Population or view.

Parameters

variables – may be either a single variable name or a list of variable names. For a given celltype class, celltype.recordable contains a list of variables that can be recorded for that celltype. To reset recording of PopulationView or Population call record with “None” as argument.
to_file – If specified, variables are written to file when pynn.end() is called. For more details see :py:function::pynn.population.write_data or pyNN-Doc <https://neuralensemble.org/docs/PyNN/data_handling.html>
sampling_interval – Feature not yet implemented.
locations – Defines where the variables should be recorded. Used with multi-compartment neurons where location is defined by label.
device – Configures device which is used to record recordables. Default is set to “madc”. Use as device “pad_[0/1]_[unbuffered/buffered]” to connect recordables of neurons to pads. Each of the 2 pads can be connected to one neuron. For limitations see documentation.