pynn_brainscales.brainscales2.simulator

Classes

ADCRecording(times, values)	Times and values of a ADC recording.
BaseState()	Base class for simulator _State classes.
Connection(connection_from_outside, None] = None)	Wrapper for connection to hardware supporting both using a connection supplied by the user and constructing an executor from the environment.
GrenadeExperiment(calibration, None] = None, …)
ID(n)	Instead of storing ids as integers, we store them as ID objects, which allows a syntax like: p[3,4].tau_m = 20.0 where p is a Population object.
IDMixin()	Instead of storing ids as integers, we store them as ID objects, which allows a syntax like: p[3,4].tau_m = 20.0 where p is a Population object.
Population(size, cellclass[, cellparams, …])	A group of neurons all of the same type.
Projection(presynaptic_neurons, …[, …])	A container for all the connections of a given type (same synapse type and plasticity mechanisms) between two populations, together with methods to set the parameters of those connections, including the parameters of plasticity mechanisms.
Recording()	Save recording information as well as recorded data.
State()	Represent the simulator state.

Functions

pynn_brainscales.brainscales2.simulator.NamedTuple(typename, fields=None, /, **kwargs)

Typed version of namedtuple.

Usage:

class Employee(NamedTuple):
    name: str
    id: int

This is equivalent to:

Employee = collections.namedtuple('Employee', ['name', 'id'])

The resulting class has an extra __annotations__ attribute, giving a dict that maps field names to types. (The field names are also in the _fields attribute, which is part of the namedtuple API.) An alternative equivalent functional syntax is also accepted:

Employee = NamedTuple('Employee', [('name', str), ('id', int)])

pynn_brainscales.brainscales2.simulator.deepcopy(x, memo=None, _nil=[])

Deep copy operation on arbitrary Python objects.

See the module’s __doc__ string for more info.