Population
Population(
self,
geometry,
neuron,
name=None,
stop_condition=None,
storage_order='post_to_pre',
copied=False,
net_id=0,
)Population of neurons.
The object is returned by Network.create() and should not be created directly.
net = ann.Network()
pop = net.create(100, neuron=ann.Izhikevich, name="Excitatory population")Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| geometry | tuple | int | population geometry as tuple. If an integer is given, it is the size of the population. | required |
| neuron | Neuron | Neuron instance. It can be user-defined or a built-in model. |
required |
| name | str | unique name of the population (optional). | None |
| stop_condition | str | a single condition on a neural variable which can stop the simulation whenever it is true. | None |
Attributes
| Name | Description |
|---|---|
| geometry | Geometry of the population. |
| width | Width of the population. |
| height | Height of the population. |
| depth | Depth of the population. |
| dimension | Number of dimensions of the population. |
| size | Size of the population (total number of neurons). |
| ranks | Array of ranks in the population (between 0 and size - 1). |
| parameters | List of parameter names. |
| variables | List of variable names. |
| attributes | List of attribute names (parameters + variables). |
| functions | List of functions defined by the neuron model. |
| targets | List of connected targets. |
| refractory | Refractory period (in ms). |
| neurons | Returns iteratively each neuron in the population. |
Methods
| Name | Description |
|---|---|
| reset | Resets all parameters and variables of the population to the value they had before the call to net.compile(). |
| clear | Clears all spiking events previously emitted (history of spikes, delayed spikes). |
| enable | (Re)-enables computations in this population, after they were disabled by the disable() method. |
| disable | Temporarily disables computations in this population (including the projections leading to it). |
| set | Sets the value of neural variables and parameters from a dictionary. |
| get | Returns the value of a neural variable or parameter based on its name. |
| sum | Returns the array of weighted sums corresponding to the target: |
| compute_firing_rate | Tells spiking neurons in the population to compute their mean firing rate over the given window and store the values in the variable r. |
| neuron | Returns an IndividualNeuron object wrapping the neuron with the provided rank or coordinates. |
| rank_from_coordinates | Returns the rank of a neuron based on coordinates. |
| coordinates_from_rank | Returns the coordinates of a neuron based on its rank. |
| normalized_coordinates_from_rank | Returns normalized coordinates of a neuron based on its rank. |
| save | Saves all information about the population (structure, current value of parameters and variables) into a file. |
| load | Load the saved state of the population by Population.save(). |
reset
reset(attributes=None)Resets all parameters and variables of the population to the value they had before the call to net.compile().
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| attributes | list | list of attributes (parameter or variable) which should be reinitialized. Default: all attributes. | None |
clear
clear()Clears all spiking events previously emitted (history of spikes, delayed spikes).
Can be useful if you do not want to totally reset a population (i.e. all variables), only to clear the spiking history between two trials.
Note: does nothing for rate-coded networks.
enable
enable()(Re)-enables computations in this population, after they were disabled by the disable() method.
The status of the population is accessible through the enabled flag.
disable
disable()Temporarily disables computations in this population (including the projections leading to it).
You can re-enable it with the enable() method.
set
set(values)Sets the value of neural variables and parameters from a dictionary.
Example:
pop.set({'tau': 20.0, 'r': np.random.rand((8,8)) } )Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| values | dict | dictionary of attributes to be updated. | required |
get
get(name)Returns the value of a neural variable or parameter based on its name.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| name | str | attribute name as a string. | required |
sum
sum(target)Returns the array of weighted sums corresponding to the target:
excitatory = pop.sum('exc')For spiking networks, this is equivalent to accessing the conductances directly:
excitatory = pop.g_excIf no incoming projection has the given target, the method returns zeros.
Note: it is not possible to distinguish the original population when the same target is used.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| target | str | the desired projection target. | required |
compute_firing_rate
compute_firing_rate(window)Tells spiking neurons in the population to compute their mean firing rate over the given window and store the values in the variable r.
This method has an effect on spiking neurons only.
If this method is not called, r will always be 0.0. r can of course be accessed and recorded as any other variable.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| window | float | window in ms over which the spikes will be counted. | required |
neuron
neuron(*coord)Returns an IndividualNeuron object wrapping the neuron with the provided rank or coordinates.
rank_from_coordinates
rank_from_coordinates(coord)Returns the rank of a neuron based on coordinates.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| coord | tuple | coordinate tuple, can be multidimensional. | required |
coordinates_from_rank
coordinates_from_rank(rank)Returns the coordinates of a neuron based on its rank.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| rank | int | rank of the neuron. | required |
normalized_coordinates_from_rank
normalized_coordinates_from_rank(rank, norm=1.0)Returns normalized coordinates of a neuron based on its rank.
The geometry of the population is mapped to the hypercube [0, 1]^d
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| rank | int | rank of the neuron. | required |
| norm | float | norm of the hypercube (default = 1.0). | 1.0 |
save
save(filename)Saves all information about the population (structure, current value of parameters and variables) into a file.
- If the file name is ‘.npz’, the data will be saved and compressed using
np.savez_compressed(recommended). - If the file name ends with ‘.gz’, the data will be pickled into a binary file and compressed using gzip.
- If the file name is ‘.mat’, the data will be saved as a Matlab 7.2 file. Scipy must be installed.
- Otherwise, the data will be pickled into a simple binary text file using pickle.
Warning: The ‘.mat’ data will not be loadable by ANNarchy, it is only for external analysis purpose.
Example:
pop.save('pop1.npz')
pop.save('pop1.txt')
pop.save('pop1.txt.gz')
pop.save('pop1.mat')Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| filename | str | Filename, may contain relative or absolute path. | required |
load
load(filename, pickle_encoding=None)Load the saved state of the population by Population.save().
Warning: Matlab data can not be loaded.
Example:
pop.load('pop1.npz')
pop.load('pop1.txt')
pop.load('pop1.txt.gz')Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| filename | the filename with relative or absolute path. | required |