nn.modules.LIFJax

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

Bases: JaxModule

A leaky integrate-and-fire spiking neuron model, with a Jax backend

This module implements the update equations:

\[ \begin{align}\begin{aligned}I_{syn} += S_{in}(t) + S_{rec} \cdot W_{rec}\\I_{syn} *= \exp(-dt / \tau_{syn})\\V_{mem} *= \exp(-dt / \tau_{mem})\\V_{mem} += I_{syn} + b + \sigma \zeta(t)\end{aligned}\end{align} \]

where \(S_{in}(t)\) is a vector containing 1 (or a weighed spike) for each input channel that emits a spike at time \(t\); \(b\) is a \(N\) vector of bias currents for each neuron; \(\sigma\zeta(t)\) is a Wiener noise process with standard deviation \(\sigma\) after 1s; and \(\tau_{mem}\) and \(\tau_{syn}\) are the membrane and synaptic time constants, respectively. \(S_{rec}(t)\) is a vector containing 1 for each neuron that emitted a spike in the last time-step. \(W_{rec}\) is a recurrent weight matrix, if recurrent weights are used. \(b\) is an optional bias current per neuron (default 0.).

On spiking:

When the membrane potential for neuron \(j\), \(V_{mem, j}\) exceeds the threshold voltage \(V_{thr}\), then the neuron emits a spike. The spiking neuron subtracts its own threshold on reset.

\[ \begin{align}\begin{aligned}V_{mem, j} > V_{thr} \rightarrow S_{rec,j} = 1\\V_{mem, j} = V_{mem, j} - V_{thr}\end{aligned}\end{align} \]

Neurons therefore share a common resting potential of 0, have individual firing thresholds, and perform subtractive reset of -V_{thr}.

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.
`n_synapses`	(int) Number of input synapses per neuron
`w_rec`	(Tensor) Recurrent weights `(Nout, Nin)`
`tau_mem`	(np.ndarray) Membrane time constants `(Nout,)` or `()`
`tau_syn`	(np.ndarray) Synaptic time constants `(Nout,)` or `()`
`bias`	(np.ndarray) Neuron bias currents `(Nout,)` or `()`
`threshold`	(np.ndarray) Firing threshold for each neuron `(Nout,)` or `()`
`dt`	(float) Simulation time-step in seconds
`noise_std`	(float) Noise injected on each neuron membrane per time-step
`spikes`	(np.ndarray) Spiking state of each neuron `(Nout,)`
`isyn`	(np.ndarray) Synaptic current of each neuron `(Nout, Nsyn)`
`vmem`	(np.ndarray) Membrane voltage of each neuron `(Nout,)`
`max_spikes_per_dt`	(float) Maximum number of events that can be produced in each time-step

Methods overview

`__init__`(shape[, tau_mem, tau_syn, bias, ...])	Instantiate an LIF module
`as_graph`()	Convert this module to a computational graph
`attributes_named`(name)	Search for attributes of this or submodules by time
`evolve`(input_data[, record])	param input_data: Input array of shape `(T, Nin)` to evolve over
`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)	Assign new attributes to this module and submodules
`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, dt, add_events])	Convert this module to a `TimedModule`
`tree_flatten`()	Flatten this module tree for Jax
`tree_unflatten`(aux_data, children)	Unflatten a tree of modules from Jax to Rockpool

Instantiate an LIF module

Parameters:

shape (tuple) – Either a single dimension (Nout,), which defines a feed-forward layer of LIF modules with equal amounts of synapses and neurons, or two dimensions (Nin, Nout), which defines a layer of Nin synapses and Nout LIF neurons.
tau_mem (Optional[np.ndarray]) – An optional array with concrete initialisation data for the membrane time constants. If not provided, 20ms will be used by default.
tau_syn (Optional[np.ndarray]) – An optional array with concrete initialisation data for the synaptic time constants. If not provided, 20ms will be used by default.
bias (Optional[np.ndarray]) – An optional array with concrete initialisation data for the neuron bias currents. If not provided, 0.0 will be used by default.
w_rec (Optional[np.ndarray]) – If the module is initialised in recurrent mode, you can provide a concrete initialisation for the recurrent weights, which must be a square matrix with shape (Nout, Nin).
has_rec (bool) – If True, module provides a recurrent weight matrix. Default: False, no recurrent connectivity.
weight_init_func (Optional[Callable[[Tuple], np.ndarray]) – The initialisation function to use when generating weights. Default: None (Kaiming initialisation)
threshold (FloatVector) – An optional array specifying the firing threshold of each neuron. If not provided, 1. will be used by default.
noise_std (float) – The std. dev. after 1s of the noise added to membrane state variables. Default: 0.0 (no noise).
max_spikes_per_dt (float) – The maximum number of events that will be produced in a single time-step. Default: 2**16.
dt (float) – The time step for the forward-Euler ODE solver. Default: 1ms
rng_key (Optional[Any]) – The Jax RNG seed to use on initialisation. By default, a new seed is generated.

_abc_impl = <_abc._abc_data object>

_auto_batch(data: Array, states: Tuple = (), target_shapes: Tuple | None = None) → Tuple[Array, Tuple[Array]]

Automatically replicate states over batches and verify input dimensions

Usage:

>>> 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.

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

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: Tuple | List | str | None = 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: str | None: Name of this module, if assigned

_register_attribute(name: str, val: 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)

Add a submodule to the module registry

Parameters:

name (str) – The name of the submodule, extracted from the assigned attribute name
mod (JaxModule) – The submodule to register

_reset_attribute(name: str) → 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)

_rockpool_pytree_registry = []: The internal registry of registered JaxModule s

_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(state_dict: dict, t_start: float = 0.0) → dict[source]

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() → 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: 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_ndarray: (np.ndarray) Neuron bias currents (Nout,) or ()

property class_name: str

Class name of self

Type:: str

dt: P_float: (float) Simulation time-step in seconds

evolve(input_data: Array, record: bool = False) → Tuple[Array, dict, dict][source]

Parameters:

input_data (np.ndarray) – Input array of shape (T, Nin) to evolve over
record (bool) – If True,

Returns:

output, new_state, record_state output is an array with shape (T, Nout) containing the output data produced by this module. new_state is a dictionary containing the updated module state following evolution. record_state will be a dictionary containing the recorded state variables for this evolution, if the record argument is True.

Return type:

(np.ndarray, dict, dict)

property full_name: str

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

Type:: str

isyn: P_ndarray: (np.ndarray) Synaptic current of each neuron (Nout, Nsyn)

max_spikes_per_dt: P_float: (float) Maximum number of events that can be produced in each time-step

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

n_synapses: (int) Number of input synapses per neuron

property name: str

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

Type:: str

noise_std: P_float: (float) Noise injected on each neuron membrane per time-step

parameters(family: Tuple | List | str | None = 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 — 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() → JaxModule

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: Iterable | MutableMapping | Mapping) → JaxModule

Assign new attributes to this module and submodules

Parameters:: new_attributes (Tree) – The tree of new attributes to assign to this module tree
Return type:: JaxModule

property shape: tuple

The shape of this module

Type:: tuple

simulation_parameters(family: Tuple | List | str | None = 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

spikes: P_ndarray: (np.ndarray) Spiking state of each neuron (Nout,)

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: Tuple | List | str | None = 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_mem: P_ndarray: (np.ndarray) Membrane time constants (Nout,) or ()

tau_syn: P_ndarray: (np.ndarray) Synaptic time constants (Nout,) or ()

threshold: P_ndarray: (np.ndarray) Firing threshold for each neuron (Nout,) or ()

timed(output_num: int = 0, dt: float | None = 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

tree_flatten() → Tuple[tuple, tuple]: Flatten this module tree for Jax

classmethod tree_unflatten(aux_data, children): Unflatten a tree of modules from Jax to Rockpool

vmem: P_ndarray: (np.ndarray) Membrane voltage of each neuron (Nout,)

w_rec: P_ndarray: (Tensor) Recurrent weights (Nout, Nin)