Building networks: neurons¶

Cell types¶

In PyNN, the system of equations that defines a neuronal model is encapsulated in a CellType class. PyNN provides a library of “standard” cell types (see Standard models) which work the same across all backend simulators - an example is the IF_cond_exp model - an integrate-and-fire (I&F) neuron with conductance-based, exponential-decay synapses. For any given simulator, it is also possible to wrap a native model - a NEST or NEURON model, for example - in a CellType class so that it works in PyNN (see documentation for individual Backends).

It should be noted that these “cell types” are mathematical cell types. Two or more different biological cell types may be represented by the same mathematical cell type, but with different parameterizations. For example, in the thalamocortical model of Destexhe (2009), thalamocortical relay neurons and cortical neurons are both modelled with the adaptive exponential I&F neuron model (AdExp):

>>> refractory_period = RandomDistribution('uniform', [2.0, 3.0], rng=NumpyRNG(seed=4242))
>>> ctx_parameters = {
...     'cm': 0.25, 'tau_m': 20.0, 'v_rest': -60, 'v_thresh': -50, 'tau_refrac': refractory_period,
...     'v_reset': -60, 'v_spike': -50.0, 'a': 1.0, 'b': 0.005, 'tau_w': 600, 'delta_T': 2.5,
...     'tau_syn_E': 5.0, 'e_rev_E': 0.0, 'tau_syn_I': 10.0, 'e_rev_I': -80 }
>>> tc_parameters = ctx_parameters.copy()
>>> tc_parameters.update({'a': 20.0, 'b': 0.0})

>>> thalamocortical_type = EIF_cond_exp_isfa_ista(**tc_parameters)
>>> cortical_type = EIF_cond_exp_isfa_ista(**ctx_parameters)

(see Model parameters and initial values for more on specifying parameter values). To see the list of parameter names for a given cell type, use the get_parameter_names() method:

>>> IF_cond_exp.get_parameter_names()
['tau_refrac', 'cm', 'tau_syn_E', 'v_rest', 'tau_syn_I', 'tau_m', 'e_rev_E', 'i_offset', 'e_rev_I', 'v_thresh', 'v_reset']

while the default values for the parameters are in the default_parameters attribute:

>>> print(IF_cond_exp.default_parameters)
{'tau_refrac': 0.1, 'cm': 1.0, 'tau_syn_E': 5.0, 'v_rest': -65.0, 'tau_syn_I': 5.0, 'tau_m': 20.0, 'e_rev_E': 0.0, 'i_offset': 0.0, 'e_rev_I': -70.0, 'v_thresh': -50.0, 'v_reset': -65.0}

Note that what we have created here are neuron type objects. These can be regarded as templates, from which we will construct the actual neurons in our network.

Populations¶

Since PyNN is designed for modelling networks containing many neurons, the default level of abstraction in PyNN is not the single neuron but a population of neurons of a given type, represented by the Population class:

>>> tc_cells = Population(100, thalamocortical_type)
>>> ctx_cells = Population(500, cortical_type)

To create a Population, we need to specify at minimum the number of neurons and the cell type. Three additional arguments may optionally be specified:

the spatial structure of the population;

initial values for the neuron state variables;

a label.

>>> from pyNN.space import Grid2D, RandomStructure, Sphere
>>> tc_cells = Population(100, thalamocortical_type,
...                       structure=RandomStructure(boundary=Sphere(radius=200.0)),
...                       initial_values={'v': -70.0},
...                       label="Thalamocortical neurons")
>>> from pyNN.random import RandomDistribution
>>> v_init = RandomDistribution('uniform', (-70.0, -60.0))
>>> ctx_cells = Population(500, cortical_type,
...                        structure=Grid2D(dx=10.0, dy=10.0),
...                        initial_values={'v': v_init},
...                        label="Cortical neurons")

(see Representing spatial structure and calculating distances for more detail on spatial structure and Model parameters and initial values for more on specifying initial values.)

For backwards compatibility and for ease of transitioning from other simulator languages, the create() function is available as an alias for Population. The following two lines are equivalent:

>>> cells = create(my_cell_type, n=100)
>>> cells = Population(100, my_cell_type)

(Note the different argument order).

Views¶

It is common to work with only a subset of the neurons in a Population - to modify their parameters, make connections or record from them. Any subset of neurons in a population may be addressed using the usual Python indexing and slicing notation, for example:

>>> id = ctx_cells[47]           # the 48th neuron in a Population
>>> view = ctx_cells[:80]        # the first eighty neurons
>>> view = ctx_cells[::2]        # every second neuron
>>> view = ctx_cells[45, 91, 7]  # a specific set of neurons

It is also possible to address a random sample of neurons within a population using the sample() method:

>>> view = ctx_cells.sample(50, rng=NumpyRNG(seed=6538))  # select 50 neurons at random

In the first of these examples, the object that is returned is an ID object, representing a single neuron. ID objects are discussed below.

In all of these examples except the first, the object that is returned is a PopulationView. A PopulationView holds references to a subset of neurons in a Population, which means that any changes in the view are also reflected in the real population (and vice versa).

PopulationView objects behave in most ways as real Population objects; notably, they can be used in a Projection (see Building networks: connections) and combined with other Population or PopulationView objects to create an Assembly.

The parent attribute of a PopulationView has a reference to the Population that is being viewed, and the mask attribute contains the indices of the neurons that are in the view.

>>> view.parent.label
'Cortical neurons'
>>> view.mask
array([150, 181,  53, 149, 496, 499, 240, 444,  13, 100,  28,  19, 101,
       122, 143, 486, 467, 492, 406,  90, 136, 173,   8, 341,   5, 348,
       188,  63, 129, 416, 307, 298,  60, 180, 382,  47, 484, 370, 223,
       147,  72,  32, 261, 193, 249, 212,  58,  87,  86, 456])

Assemblies¶

As discussed above, a Population is a homogeneous collection of neurons, in the sense that all neurons have the same cell type. An Assembly is an aggregate of Population and PopulationView objects, and as such can represent a heterogeneous collection of neurons, of multiple cell types.

An Assembly can be created by adding together Population and PopulationView objects:

>>> all_cells = tc_cells + ctx_cells
>>> cells_for_plotting = tc_cells[:10] + ctx_cells[:50]

or by using the Assembly constructor:

>>> all_cells = Assembly(tc_cells, ctx_cells)

An assembly behaves in most ways like a Population, e.g. for setting and retrieving parameters, specifying which neurons to record from, etc. It can also be specified as the source or target of a Projection. In this case, all the neurons in the component populations are treated as identical for the purposes of the connection algorithm (note that if the post-synaptic receptor type is specified (with the receptor_type argument), an Exception will be raised if not all component neuron types possess that receptor type).

Individual populations within an Assembly may be accessed via their labels, e.g.:

>>> all_cells.get_population("Thalamocortical neurons")
Population(100, EIF_cond_exp_isfa_ista(<parameters>), structure=RandomStructure(origin=(0.0, 0.0, 0.0), boundary=Sphere(radius=200.0), rng=NumpyRNG(seed=None)), label='Thalamocortical neurons')

Iterating over an assembly returns individual IDs, ordered by population. Similarly, the size attribute of an Assembly gives the total number of neurons it contains. To iterate over or count populations, use the populations attribute:

>>> for p in all_cells.populations:
...    print("%-23s %4d %s" % (p.label, p.size, p.celltype.__class__.__name__))
Thalamocortical neurons  100 EIF_cond_exp_isfa_ista
Cortical neurons         500 EIF_cond_exp_isfa_ista

Inspecting and modifying parameter values and initial conditions¶

Although both parameter values and initial conditions may be specified when creating a Population (and this is generally the most efficient place to do it), it is also possible to modify them later.

The get() method of Population, PopulationView and Assembly returns the current value(s) of one or more parameters:

>>> ctx_cells.get('tau_m')
20.0
>>> all_cells[0:10].get('v_reset')
-60.0

If the parameter is homogeneous across the group, a single number will be returned, otherwise get() will return a NumPy array containing the parameter values for all neurons:

>>> ctx_cells.get('tau_refrac')
array([ 2.64655001,  2.15914942,  2.53500179, ...

It is also possible to ask for multiple parameters at once, in which case a list of values in the same order as the list of parameter names will be returned.

>>> ctx_cells.get(['tau_m', 'cm'])
[20.0, 0.25]

When running a distributed simulation using MPI, get() will by default return values for only those neurons that exist on the current MPI node. To get the values for all neurons, use get(parameter_name, gather=True).

To modify parameter values, use the set() method. To set the same value for all neurons, pass a single number as the parameter value:

>>> ctx_cells.set(a=2.0, b=0.2)

To set different values for different neurons there are several options - see Model parameters and initial values for more details.

To modify the initial values of model variables, use the initialize() method:

>>> ctx_cells.initialize(v=RandomDistribution('normal', (-65.0, 2.0)),
...                      w=0.0)

The default initial values may be inspected as follows:

>>> ctx_cells.celltype.default_initial_values
{'gsyn_exc': 0.0, 'gsyn_inh': 0.0, 'w': 0.0, 'v': -70.6}

Injecting current into neurons¶

Static or time-varying currents may be injected into neurons using either the inject_into() method of the CurrentSource:

>>> pulse = DCSource(amplitude=0.5, start=20.0, stop=80.0)
>>> pulse.inject_into(tc_cells)

or the inject() method of the Population, PopulationView or Assembly:

>>> import numpy
>>> times = numpy.arange(0.0, 100.0, 1.0)
>>> amplitudes = 0.1*numpy.sin(times*numpy.pi/100.0)
>>> sine_wave = StepCurrentSource(times=times, amplitudes=amplitudes)
>>> ctx_cells[80:90].inject(sine_wave)

See Injecting current for more about injecting currents.

Recording variables and retrieving recorded data¶

Just as each cell type has a well-defined set of parameters (whose values are constant over time), so it has a well-defined set of state variables, such as the membrane potential, whose values change over the course of a simulation. The recordable attribute of a CellType class contains a list of these variables, as well as the ‘spikes’ variable, which is used to record the times of action potentials:

>>> ctx_cells.celltype.recordable
['spikes', 'v', 'w', 'gsyn_exc', 'gsyn_inh']

The record() method specifies which variables should be recorded:

>>> all_cells.record('spikes')
>>> ctx_cells.sample(10).record(('v', 'w')) #, sampling_interval=0.2)

Note that the sampling interval must be an integer multiple of the simulation time step (except for simulators which allow use of variable time-step integration methods).

Todo

discuss specifying filename in record()

At the end of a simulation, the recorded data can be retrieved using the get_data() method:

>>> t = run(0.2)
>>> data_block = all_cells.get_data()

or written to file using write_data():

>>> from neo.io import NeoHdf5IO
>>> h5file = NeoHdf5IO("my_data.h5")
>>> ctx_cells.write_data(h5file)
>>> h5file.close()

get_data() returns a Neo Block object. For more information on Neo see the documentation at http://packages.python.org/neo. Here, it will suffice to note that a Block is the top-level container, and contains one or more Segments. Each Segment is a container for data that share a common time basis, and can contain lists of AnalogSignal, AnalogSignalArray and SpikeTrain objects. These data objects inherit from NumPy array, and so can be treated in further processing (analysis, visualization, etc.) in exactly the same way as plain arrays, but in addition they carry metadata about units, sampling interval, etc.

write_data() also makes use of Neo, and allows writing to any of the several output file formats supported by Neo. Note that as a short-cut, you can just give a filename to write_data(); the output format will then be determined based on the filename extension (‘.h5’ for HDF5, ‘.txt’ for ASCII, etc.) if possible, otherwise the default file format (determined by the value of pyNN.recording.DEFAULT_FILE_FORMAT) will be used.

For more details, see Data handling.

Working with individual neurons¶

Although it is usually more convenient and more efficient to work with populations of neurons, it is occasionally convienient to work with individual neurons, represented as an ID object:

>>> tc_cells[47]
709

For the simulator backends shipped with PyNN, the ID class is a subclass of int, and in the case of NEURON and NEST matches the global ID (gid) used internally by the simulator. There is no requirement that IDs be integers, however, nor that they have the same value across different simulators.

The parent attribute contains a reference to the parent population:

>>> a_cell = tc_cells[47]
>>> a_cell.parent.label
'Thalamocortical neurons'

To recover the index of a neuron within its parent given the ID, use Population.id_to_index(), e.g.:

>>> tc_cells.id_to_index(a_cell)
47

The ID object allows direct access to the parameters of individual neurons, e.g.:

>>> a_cell.tau_m
20.0

To change several parameters at once for a single neuron, use the set_parameters() method:

>>> a_cell.set_parameters(tau_m=10.0, cm=0.5)
>>> a_cell.tau_m
10.0
>>> a_cell.cm
0.5

Building networks: neurons¶

Cell types¶

Populations¶

Views¶

Assemblies¶

Inspecting and modifying parameter values and initial conditions¶

Injecting current into neurons¶

Recording variables and retrieving recorded data¶

Working with individual neurons¶

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