norse.torch.module.lif_ex module

class norse.torch.module.lif_ex.LIFEx(p=LIFExParameters(delta_T=tensor(0.5000), 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=100.0), **kwargs)[source]

Bases: norse.torch.module.snn.SNN

A neuron layer that wraps a LIFExCell in time such that the layer keeps track of temporal sequences of spikes. After application, the layer returns a tuple containing

(spikes from all timesteps, state from the last timestep).

Example

>>> data = torch.zeros(10, 5, 2) # 10 timesteps, 5 batches, 2 neurons
>>> l = LIFEx()
>>> l(data) # Returns tuple of (Tensor(10, 5, 2), LIFExState)
Parameters

  • p (LIFExParameters) – The neuron parameters as a torch Module, which allows the module to configure neuron parameters as optimizable. Defaults to None.

  • dt (float) – Time step to use in integration. Defaults to 0.001.

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

initial_state(input_tensor)[source]
Return type

LIFExFeedForwardState

training: bool
class norse.torch.module.lif_ex.LIFExCell(p=LIFExParameters(delta_T=tensor(0.5000), 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=100.0), **kwargs)[source]

Bases: norse.torch.module.snn.SNNCell

Computes a single euler-integration step of a recurrent exponential LIF neuron-model (without recurrence) adapted from https://neuronaldynamics.epfl.ch/online/Ch5.S2.html. More specifically it implements one integration step of the following ODE

\[\begin{split}\begin{align*} \dot{v} &= 1/\tau_{\text{mem}} \left(v_{\text{leak}} - v + i + \Delta_T exp\left({{v - v_{\text{th}}} \over {\Delta_T}}\right)\right) \\ \dot{i} &= -1/\tau_{\text{syn}} i \end{align*}\end{split}\]

together with the jump condition

\[z = \Theta(v - v_{\text{th}})\]

and transition equations

\[\begin{split}\begin{align*} v &= (1-z) v + z v_{\\text{reset}} \end{align*}\end{split}\]

where \(z_{\text{rec}}\) and \(z_{\text{in}}\) are the recurrent and input spikes respectively.

Parameters

  • input_size (int) – Size of the input.

  • hidden_size (int) – Size of the hidden state.

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

  • dt (float) – Time step to use.

  • autapses (bool) – Allow self-connections in the recurrence? Defaults to False.

Examples

>>> batch_size = 16
>>> lif_ex = LIFExCell(10, 20)
>>> input = torch.randn(batch_size, 10)
>>> output, s0 = lif_ex(input)

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

initial_state(input_tensor)[source]
Return type

LIFExFeedForwardState

training: bool
class norse.torch.module.lif_ex.LIFExRecurrent(input_size, hidden_size, p=LIFExParameters(delta_T=tensor(0.5000), 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=100.0), **kwargs)[source]

Bases: norse.torch.module.snn.SNNRecurrent

A neuron layer that wraps a LIFExRecurrentCell in time such that the layer keeps track of temporal sequences of spikes. After application, the module returns a tuple containing

(spikes from all timesteps, state from the last timestep).

Example

>>> data = torch.zeros(10, 5, 2) # 10 timesteps, 5 batches, 2 neurons
>>> l = LIFExRecurrent(2, 4)
>>> l(data) # Returns tuple of (Tensor(10, 5, 4), LIFExState)
Parameters

  • input_size (int) – The number of input neurons

  • hidden_size (int) – The number of hidden neurons

  • p (LIFExParameters) – The neuron parameters as a torch Module, which allows the module to configure neuron parameters as optimizable. Defaults to None.

  • input_weights (torch.Tensor) – Weights used for input tensors. Defaults to a random matrix normalized to the number of hidden neurons.

  • recurrent_weights (torch.Tensor) – Weights used for input tensors. Defaults to a random matrix normalized to the number of hidden neurons.

  • autapses (bool) – Allow self-connections in the recurrence? Defaults to False. Will also remove autapses in custom recurrent weights, if set above.

  • dt (float) – Time step to use in integration. Defaults to 0.001.

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

initial_state(input_tensor)[source]
Return type

LIFExState

training: bool
class norse.torch.module.lif_ex.LIFExRecurrentCell(input_size, hidden_size, p=LIFExParameters(delta_T=tensor(0.5000), 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=100.0), **kwargs)[source]

Bases: norse.torch.module.snn.SNNRecurrentCell

Computes a single euler-integration step of a recurrent exponential LIFEx neuron-model (with recurrence) adapted from https://neuronaldynamics.epfl.ch/online/Ch5.S2.html. More specifically it implements one integration step of the following ODE

\[\begin{split}\begin{align*} \dot{v} &= 1/\tau_{\text{mem}} \left(v_{\text{leak}} - v + i + \Delta_T exp\left({{v - v_{\text{th}}} \over {\Delta_T}}\right)\right) \\ \dot{i} &= -1/\tau_{\text{syn}} i \end{align*}\end{split}\]

together with the jump condition

\[z = \Theta(v - v_{\text{th}})\]

and transition equations

\[\begin{split}\begin{align*} v &= (1-z) v + z v_{\\text{reset}} \\ i &= i + w_{\text{input}} z_{\text{in}} \\ i &= i + w_{\text{rec}} z_{\text{rec}} \end{align*}\end{split}\]

where \(z_{\text{rec}}\) and \(z_{\text{in}}\) are the recurrent and input spikes respectively.

Parameters

  • input_size (int) – Size of the input.

  • hidden_size (int) – Size of the hidden state.

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

  • dt (float) – Time step to use.

  • a (bool) – Allow self-connections in the recurrence? Defaults to False.

Examples

>>> batch_size = 16
>>> lif_ex = LIFExRecurrentCell(10, 20)
>>> input = torch.randn(batch_size, 10)
>>> output, s0 = lif_ex(input)

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

initial_state(input_tensor)[source]
Return type

LIFExState

training: bool