nn.modules.Rate

class nn.modules.Rate(*args, **kwargs)[source]

Bases: rockpool.nn.modules.module.Module

Encapsulates a population of rate neurons, supporting feed-forward and recurrent modules

Examples

Instantiate a feed-forward module with 8 neurons:

>>> mod = Rate(8,)
RateEulerJax 'None' with shape (8,)

Instantiate a recurrent module with 12 neurons:

>>> mod_rec = Rate(12, has_rec = True)
RateEulerJax 'None' with shape (12,)

Instantiate a feed-forward module with defined time constants:

>>> mod = Rate(7, tau = np.arange(7,) * 10e-3)
RateEulerJax 'None' with shape (7,)

This module implements the update equations:

\[ \begin{align}\begin{aligned}\dot{X} = -X + i(t) + W_{rec} H(X) + bias + \sigma \zeta_t X = X + \dot{x} * dt / au\\H(x, t) = relu(x, t) = (x - t) * ((x - t) > 0)\end{aligned}\end{align} \]
__init__(shape: typing.Union[int, typing.Tuple[int, int], typing.Tuple[int]], tau: typing.Optional[typing.Union[float, numpy.ndarray, torch.Tensor]] = None, bias: typing.Optional[typing.Union[float, numpy.ndarray, torch.Tensor]] = None, threshold: typing.Optional[typing.Union[float, numpy.ndarray, torch.Tensor]] = None, w_rec: typing.Optional[numpy.ndarray] = None, weight_init_func: typing.Callable = <function unit_eigs>, has_rec: bool = False, activation_func: typing.Union[str, typing.Callable] = <function H_ReLU>, dt: float = 0.001, noise_std: float = 0.0, *args: list, **kwargs: dict)[source]

Instantiate a non-spiking rate module, either feed-forward or recurrent.

Parameters
  • shape (Tuple[int]) – A tuple containing the numer of this module.

  • tau (float) – A scalar or vector defining the initialisation time constants for the module. If a vector is provided, it must match the output size of the module. Default: 20ms for each unit

  • bias (float) – A scalar or vector defining the initialisation bias values for the module. If a vector is provided, it must match the output size of the module. Default: 0.

  • w_rec (np.ndarray) – An optional matrix defining the initialisation recurrent weights for the module.

  • weight_init_func (Callable) – A function used to initialise the recurrent weights, if used. Default: unit_eigs(); initialise such that recurrent feedback has eigenvalues distributed within the unit circle.

  • has_rec (bool) – A flag parameter indicating whether the module has recurrent connections or not. Default: False, no recurrent connections.

  • activation_func (Callable) – The activation function of the neurons. This can be provided as a string ['ReLU', 'sigmoid', 'tanh'], or as a function that accepts a vector of neural states and returns the vector of output activations. Default: 'ReLU'.

  • dt (float) – The Euler solver time-step. Default: 1e-3

  • noise_std (float) – The std. dev. of normally-distributed noise added to the neural state at each time step. Default: 0.

  • rng_key (Any) – A Jax PRNG key to initialise the module with. Default: not provided, the module PRNG will be initialised with a random number.

  • *args – Additional positional arguments

  • **kwargs – Additional keyword arguments

Attributes overview

class_name

Class name of self

full_name

The full name of this module (class plus module name)

name

The name of this module, or an empty string if None

shape

The shape of this module

size

(DEPRECATED) The output size of this module

size_in

The input size of this module

size_out

The output size of this module

spiking_input

If True, this module receives spiking input.

spiking_output

If True, this module sends spiking output.

x

A vector (N,) of the internal state of each neuron

w_rec

The recurrent weight matrix (N, N) for this module

tau

The vector (N,) of time constants \(\tau\) for each neuron

bias

The vector (N,) of bias currents for each neuron

threshold

(Tensor) Unit thresholds (Nout,) or ()

dt

The Euler solver time step for this module

noise_std

The std.

Methods overview

__init__(shape[,Β tau,Β bias,Β threshold,Β ...])

Instantiate a non-spiking rate module, either feed-forward or recurrent.

as_graph()

Convert this module to a computational graph

attributes_named(name)

Search for attributes of this or submodules by time

evolve(input_data[,Β record])

Evolve the state of this module over input data

modules()

Return a dictionary of all sub-modules of this module

parameters([family])

Return a nested dictionary of module and submodule Parameters

reset_parameters()

Reset all parameters in this module

reset_state()

Reset the state of this module

set_attributes(new_attributes)

Set the attributes and sub-module attributes from a dictionary

simulation_parameters([family])

Return a nested dictionary of module and submodule SimulationParameters

state([family])

Return a nested dictionary of module and submodule States

timed([output_num,Β dt,Β add_events])

Convert this module to a TimedModule

__init__(shape: typing.Union[int, typing.Tuple[int, int], typing.Tuple[int]], tau: typing.Optional[typing.Union[float, numpy.ndarray, torch.Tensor]] = None, bias: typing.Optional[typing.Union[float, numpy.ndarray, torch.Tensor]] = None, threshold: typing.Optional[typing.Union[float, numpy.ndarray, torch.Tensor]] = None, w_rec: typing.Optional[numpy.ndarray] = None, weight_init_func: typing.Callable = <function unit_eigs>, has_rec: bool = False, activation_func: typing.Union[str, typing.Callable] = <function H_ReLU>, dt: float = 0.001, noise_std: float = 0.0, *args: list, **kwargs: dict)[source]

Instantiate a non-spiking rate module, either feed-forward or recurrent.

Parameters
  • shape (Tuple[int]) – A tuple containing the numer of this module.

  • tau (float) – A scalar or vector defining the initialisation time constants for the module. If a vector is provided, it must match the output size of the module. Default: 20ms for each unit

  • bias (float) – A scalar or vector defining the initialisation bias values for the module. If a vector is provided, it must match the output size of the module. Default: 0.

  • w_rec (np.ndarray) – An optional matrix defining the initialisation recurrent weights for the module.

  • weight_init_func (Callable) – A function used to initialise the recurrent weights, if used. Default: unit_eigs(); initialise such that recurrent feedback has eigenvalues distributed within the unit circle.

  • has_rec (bool) – A flag parameter indicating whether the module has recurrent connections or not. Default: False, no recurrent connections.

  • activation_func (Callable) – The activation function of the neurons. This can be provided as a string ['ReLU', 'sigmoid', 'tanh'], or as a function that accepts a vector of neural states and returns the vector of output activations. Default: 'ReLU'.

  • dt (float) – The Euler solver time-step. Default: 1e-3

  • noise_std (float) – The std. dev. of normally-distributed noise added to the neural state at each time step. Default: 0.

  • rng_key (Any) – A Jax PRNG key to initialise the module with. Default: not provided, the module PRNG will be initialised with a random number.

  • *args – Additional positional arguments

  • **kwargs – Additional keyword arguments

_abc_impl = <_abc_data object>
_auto_batch(data: numpy.ndarray, states: typing.Tuple = (), target_shapes: typing.Optional[typing.Tuple] = None) -> (<class 'numpy.ndarray'>, typing.Tuple[numpy.ndarray])

Automatically replicate states over batches and verify input dimensions

Examples

>>> data, (state0, state1, state2) = self._auto_batch(data, (self.state0, self.state1, self.state2))

This will verify that data has the correct final dimension (i.e. self.size_in).

If data has only two dimensions (T, Nin), then it will be augmented to (1, T, Nin). The individual states will be replicated out from shape (a, b, c, ...) to (n_batches, a, b, c, ...) and returned.

If data has only a single dimension (T,), it will be expanded to (1, T, self.size_in).

state0, state1, state2 will be replicated out along the batch dimension.

>>> data, (state0,) = self._auto_batch(data, (self.state0,), ((10, -1, self.size_in),))

Attempt to replicate state0 to a specified size (10, -1, self.size_in).

Parameters
  • data (np.ndarray) – Input data tensor. Either (batches, T, Nin) or (T, Nin)

  • states (Tuple) – Tuple of state variables. Each will be replicated out over batches by prepending a batch dimension

  • target_shapes (Tuple) – A tuple of target size tuples, each corresponding to each state argument. The individual states will be replicated out to match the corresponding target sizes. If not provided (the default), then states will be only replicated along batches.

Returns

(np.ndarray, Tuple[np.ndarray]) data, states

_force_set_attributes

(bool) If True, do not sanity-check attributes when setting.

_get_attribute_family(type_name: str, family: Optional[Union[Tuple, List, str]] = None) dict

Search for attributes of this module and submodules that match a given family

This method can be used to conveniently get all weights for a network; or all time constants; or any other family of parameters. Parameter families are defined simply by a string: "weights" for weights; "taus" for time constants, etc. These strings are arbitrary, but if you follow the conventions then future developers will thank you (that includes you in six month’s time).

Parameters
  • type_name (str) – The class of parameters to search for. Must be one of ["Parameter", "SimulationParameter", "State"] or another future subclass of ParameterBase

  • family (Union[str, Tuple[str]]) – A string or list or tuple of strings, that define one or more attribute families to search for

Returns

A nested dictionary of attributes that match the provided type_name and family

Return type

dict

_get_attribute_registry() Tuple[Dict, Dict]

Return or initialise the attribute registry for this module

Returns

registered_attributes, registered_modules

Return type

(tuple)

_has_registered_attribute(name: str) bool

Check if the module has a registered attribute

Parameters

name (str) – The name of the attribute to check

Returns

True if the attribute name is in the attribute registry, False otherwise.

Return type

bool

_in_Module_init

(bool) If exists and True, indicates that the module is in the __init__ chain.

_name: Optional[str]

Name of this module, if assigned

_register_attribute(name: str, val: rockpool.parameters.ParameterBase)

Record an attribute in the attribute registry

Parameters
  • name (str) – The name of the attribute to register

  • val (ParameterBase) – The ParameterBase subclass object to register. e.g. Parameter, SimulationParameter or State.

_register_module(name: str, mod: rockpool.nn.modules.module.ModuleBase)

Register a sub-module in the module registry

Parameters
  • name (str) – The name of the module to register

  • mod (ModuleBase) – The ModuleBase object to register

_reset_attribute(name: str) rockpool.nn.modules.module.ModuleBase

Reset an attribute to its initialisation value

Parameters

name (str) – The name of the attribute to reset

Returns

For compatibility with the functional API

Return type

self (Module)

_shape

The shape of this module

_spiking_input: bool

Whether this module receives spiking input

_spiking_output: bool

Whether this module produces spiking output

_submodulenames: List[str]

Registry of sub-module names

_wrap_recorded_state(recorded_dict: dict, t_start: float) Dict[str, rockpool.timeseries.TimeSeries]

Convert a recorded dictionary to a TimeSeries representation

This method is optional, and is provided to make the timed() conversion to a TimedModule work better. You should override this method in your custom Module, to wrap each element of your recorded state dictionary as a TimeSeries

Parameters
  • state_dict (dict) – A recorded state dictionary as returned by evolve()

  • t_start (float) – The initial time of the recorded state, to use as the starting point of the time series

Returns

The mapped recorded state dictionary, wrapped as TimeSeries objects

Return type

Dict[str, TimeSeries]

as_graph() rockpool.graph.graph_base.GraphModuleBase[source]

Convert this module to a computational graph

Returns

The computational graph corresponding to this module

Return type

GraphModuleBase

Raises

NotImplementedError – If as_graph() is not implemented for this subclass

attributes_named(name: Union[Tuple[str], List[str], str]) dict

Search for attributes of this or submodules by time

Parameters

name (Union[str, Tuple[str]) – The name of the attribute to search for

Returns

A nested dictionary of attributes that match name

Return type

dict

bias: P_tensor

The vector (N,) of bias currents for each neuron

property class_name: str

Class name of self

Type

str

dt: P_float

The Euler solver time step for this module

evolve(input_data: numpy.ndarray, record: bool = False)[source]

Evolve the state of this module over input data

NOTE: THIS MODULE CLASS DOES NOT PROVIDE DOCUMENTATION FOR ITS EVOLVE METHOD. PLEASE UPDATE THE DOCUMENTATION FOR THIS MODULE.

Parameters
  • input_data – The input data with shape (T, size_in) to evolve with

  • record (bool) – If True, the module should record internal state during evolution and return the record. If False, no recording is required. Default: False.

Returns

(output, new_state, record)

output (np.ndarray): The output response of this module with shape (T, size_out) new_state (dict): A dictionary containing the updated state of this and all submodules after evolution record (dict): A dictionary containing recorded state of this and all submodules, if requested using the record argument

Return type

tuple

property full_name: str

The full name of this module (class plus module name)

Type

str

modules() Dict

Return a dictionary of all sub-modules of this module

Returns

A dictionary containing all sub-modules. Each item will be named with the sub-module name.

Return type

dict

property name: str

The name of this module, or an empty string if None

Type

str

noise_std: P_float

The std. dev. \(\sigma\) of noise added to internal neuron states at each time step

parameters(family: Optional[Union[Tuple, List, str]] = None) Dict

Return a nested dictionary of module and submodule Parameters

Use this method to inspect the Parameters from this and all submodules. The optional argument family allows you to search for Parameters in a particular family β€” for example "weights" for all weights of this module and nested submodules.

Although the family argument is an arbitrary string, reasonable choises are "weights", "taus" for time constants, "biases" for biases…

Examples

Obtain a dictionary of all Parameters for this module (including submodules):

>>> mod.parameters()
dict{ ... }

Obtain a dictionary of Parameters from a particular family:

>>> mod.parameters("weights")
dict{ ... }
Parameters

family (str) – The family of Parameters to search for. Default: None; return all parameters.

Returns

A nested dictionary of Parameters of this module and all submodules

Return type

dict

reset_parameters()

Reset all parameters in this module

Returns

The updated module is returned for compatibility with the functional API

Return type

Module

reset_state() rockpool.nn.modules.module.ModuleBase

Reset the state of this module

Returns

The updated module is returned for compatibility with the functional API

Return type

Module

set_attributes(new_attributes: dict) rockpool.nn.modules.module.ModuleBase

Set the attributes and sub-module attributes from a dictionary

This method can be used with the dictionary returned from module evolution to set the new state of the module. It can also be used to set multiple parameters of a module and submodules.

Examples

Use the functional API to evolve, obtain new states, and set those states:

>>> _, new_state, _ = mod(input)
>>> mod = mod.set_attributes(new_state)

Obtain a parameter dictionary, modify it, then set the parameters back:

>>> params = mod.parameters()
>>> params['w_input'] *= 0.
>>> mod.set_attributes(params)
Parameters

new_attributes (dict) – A nested dictionary containing parameters of this module and sub-modules.

property shape: tuple

The shape of this module

Type

tuple

simulation_parameters(family: Optional[Union[Tuple, List, str]] = None) Dict

Return a nested dictionary of module and submodule SimulationParameters

Use this method to inspect the SimulationParameters from this and all submodules. The optional argument family allows you to search for SimulationParameters in a particular family.

Examples

Obtain a dictionary of all SimulationParameters for this module (including submodules):

>>> mod.simulation_parameters()
dict{ ... }
Parameters

family (str) – The family of SimulationParameters to search for. Default: None; return all SimulationParameter attributes.

Returns

A nested dictionary of SimulationParameters of this module and all submodules

Return type

dict

property size: int

(DEPRECATED) The output size of this module

Type

int

property size_in: int

The input size of this module

Type

int

property size_out: int

The output size of this module

Type

int

property spiking_input: bool

If True, this module receives spiking input. If False, this module expects continuous input.

Type

bool

property spiking_output

If True, this module sends spiking output. If False, this module sends continuous output.

Type

bool

state(family: Optional[Union[Tuple, List, str]] = None) Dict

Return a nested dictionary of module and submodule States

Use this method to inspect the States from this and all submodules. The optional argument family allows you to search for States in a particular family.

Examples

Obtain a dictionary of all States for this module (including submodules):

>>> mod.state()
dict{ ... }
Parameters

family (str) – The family of States to search for. Default: None; return all State attributes.

Returns

A nested dictionary of States of this module and all submodules

Return type

dict

tau: P_tensor

The vector (N,) of time constants \(\tau\) for each neuron

threshold: P_tensor

(Tensor) Unit thresholds (Nout,) or ()

timed(output_num: int = 0, dt: Optional[float] = None, add_events: bool = False)

Convert this module to a TimedModule

Parameters
  • output_num (int) – Specify which output of the module to take, if the module returns multiple output series. Default: 0, take the first (or only) output.

  • dt (float) – Used to provide a time-step for this module, if the module does not already have one. If self already defines a time-step, then self.dt will be used. Default: None

  • add_events (bool) – Iff True, the TimedModule will add events occurring on a single timestep on input and output. Default: False, don’t add time steps.

Returns: TimedModule: A timed module that wraps this module

w_rec: P_tensor

The recurrent weight matrix (N, N) for this module

x: Union[np.ndarray, State]

A vector (N,) of the internal state of each neuron