norse.torch.module.encode module

Stateful encoders as torch modules.

class norse.torch.module.encode.ConstantCurrentLIFEncoder(seq_length, p=LIFParameters(tau_syn_inv=tensor(200.), tau_mem_inv=tensor(100.), v_leak=tensor(0.), v_th=tensor(1.), v_reset=tensor(0.), method='super', alpha=tensor(100.)), dt=0.001)[source]

Bases: torch.nn.modules.module.Module

Encodes input currents as fixed (constant) voltage currents, and simulates the spikes that occur during a number of timesteps/iterations (seq_length).

Example

>>> data = torch.as_tensor([2, 4, 8, 16])
>>> seq_length = 2 # Simulate two iterations
>>> constant_current_lif_encode(data, seq_length)
(tensor([[0.2000, 0.4000, 0.8000, 0.0000],   # State in terms of membrane voltage
        [0.3800, 0.7600, 0.0000, 0.0000]]),
tensor([[0., 0., 0., 1.],                   # Spikes for each iteration
        [0., 0., 1., 1.]]))
Parameters
  • seq_length (int) – The number of iterations to simulate

  • p (LIFParameters) – Initial neuron parameters.

  • dt (float) – Time delta between simulation steps

forward(input_currents)[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool
class norse.torch.module.encode.PoissonEncoder(seq_length, f_max=100, dt=0.001)[source]

Bases: torch.nn.modules.module.Module

Encodes a tensor of input values, which are assumed to be in the range [0,1] (if not signed, [-1,1] if signed) into a tensor of one dimension higher of binary values, which represent input spikes.

Parameters
  • input_values (torch.Tensor) – Input data tensor with values assumed to be in the interval [0,1].

  • sequence_length (int) – Number of time steps in the resulting spike train.

  • f_max (float) – Maximal frequency (in Hertz) which will be emitted.

  • dt (float) – Integration time step (should coincide with the integration time step used in the model)

forward(x)[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool
class norse.torch.module.encode.PopulationEncoder(out_features, scale=None, kernel=<function gaussian_rbf>, distance_function=<function euclidean_distance>)[source]

Bases: torch.nn.modules.module.Module

Encodes a set of input values into population codes, such that each singular input value is represented by a list of numbers (typically calculated by a radial basis kernel), whose length is equal to the out_features.

Population encoding can be visualised by imagining a number of neurons in a list, whose activity increases if a number gets close to its “receptive field”.

https://upload.wikimedia.org/wikipedia/commons/thumb/a/a1/PopulationCode.svg/1920px-PopulationCode.svg.png

Gaussian curves representing different neuron “receptive fields”. Image credit: Andrew K. Richardson.

super(PopulationEncoder, self).__init__()mons.wikimedia.org/wiki/File:PopulationCode.svg

Example

>>> data = torch.as_tensor([0, 0.5, 1])
>>> out_features = 3
>>> PopulationEncoder(out_features).forward(data)
tensor([[1.0000, 0.8825, 0.6065],
        [0.8825, 1.0000, 0.8825],
        [0.6065, 0.8825, 1.0000]])
Parameters
  • out_features (int) – The number of output per input value

  • scale (torch.Tensor) – The scaling factor for the kernels. Defaults to the maximum value of the input. Can also be set for each individual sample.

  • kernel (Callable[[Tensor], Tensor]) – A function that takes two inputs and returns a tensor. The two inputs represent the center value (which changes for each index in the output tensor) and the actual data value to encode respectively.z Defaults to gaussian radial basis kernel function.

  • distance_function (Callable[[Tensor, Tensor], Tensor]) – A function that calculates the distance between two numbers. Defaults to euclidean.

forward(input_tensor)[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool
class norse.torch.module.encode.SignedPoissonEncoder(seq_length, f_max=100, dt=0.001)[source]

Bases: torch.nn.modules.module.Module

Encodes a tensor of input values, which are assumed to be in the range [-1,1] (if not signed, [-1,1] if signed) into a tensor of one dimension higher of binary values, which represent input spikes.

Parameters
  • sequence_length (int) – Number of time steps in the resulting spike train.

  • f_max (float) – Maximal frequency (in Hertz) which will be emitted.

  • dt (float) – Integration time step (should coincide with the integration time step used in the model)

forward(x)[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool
class norse.torch.module.encode.SpikeLatencyEncoder[source]

Bases: torch.nn.modules.module.Module

For all neurons, remove all but the first spike. This encoding basically measures the time it takes for a neuron to spike first. Assuming that the inputs are constant, this makes sense in that strong inputs spikes fast.

See R. Van Rullen & S. J. Thorpe (2001): Rate Coding Versus Temporal Order Coding: What the Retinal Ganglion Cells Tell the Visual Cortex.

Spikes are identified by their unique position in the input array.

Example

>>> data = torch.as_tensor([[0, 1, 1], [1, 1, 1]])
>>> encoder = torch.nn.Sequential(
                ConstantCurrentLIFEncoder()
                SpikeLatencyEncoder()
                )
>>> encoder(data)
tensor([[0, 1, 1],
        [1, 0, 0]])

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input_spikes)[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool
class norse.torch.module.encode.SpikeLatencyLIFEncoder(seq_length, p=LIFParameters(tau_syn_inv=tensor(200.), tau_mem_inv=tensor(100.), v_leak=tensor(0.), v_th=tensor(1.), v_reset=tensor(0.), method='super', alpha=tensor(100.)), dt=0.001)[source]

Bases: torch.nn.modules.module.Module

Encodes an input value by the time the first spike occurs. Similar to the ConstantCurrentLIFEncoder, but the LIF can be thought to have an infinite refractory period.

Parameters
  • sequence_length (int) – Number of time steps in the resulting spike train.

  • p (LIFParameters) – Parameters of the LIF neuron model.

  • dt (float) – Integration time step (should coincide with the integration time step used in the model)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input_current)[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool