norse.torch.module.lif module¶
A very popular neuron model that combines a norse.torch.module.leaky_integrator with
spike thresholds to produce events (spikes).
See norse.torch.functional.lif for more information.
- class norse.torch.module.lif.LIF(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.)), **kwargs)[source]¶
Bases:
norse.torch.module.snn.SNNA neuron layer that wraps a
LIFCellin 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 = LIF() >>> l(data) # Returns tuple of (Tensor(10, 5, 2), LIFState)
- Parameters
p (LIFParameters) – The neuron parameters as a torch Module, which allows the module to configure neuron parameters as optimizable.
sparse (bool) – Whether to apply sparse activation functions (True) or not (False). Defaults to False.
dt (float) – Time step to use in integration. Defaults to 0.001.
Initializes internal Module state, shared by both nn.Module and ScriptModule.
- class norse.torch.module.lif.LIFCell(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.)), **kwargs)[source]¶
Bases:
norse.torch.module.snn.SNNCellModule that computes a single euler-integration step of a leaky integrate-and-fire (LIF) neuron-model without recurrence and without time.
More specifically it implements one integration step of the following ODE
\[\begin{align*} \dot{v} &= 1/\tau_{\text{mem}} (v_{\text{leak}} - v + i) \ \dot{i} &= -1/\tau_{\text{syn}} i \end{align*}\]together with the jump condition
\[z = \Theta(v - v_{\text{th}})\]and transition equations
\[\begin{align*} v &= (1-z) v + z v_{\text{reset}} \end{align*}\]Example
>>> data = torch.zeros(5, 2) # 5 batches, 2 neurons >>> l = LIFCell(2, 4) >>> l(data) # Returns tuple of (Tensor(5, 4), LIFState)
- Parameters
p (LIFParameters) – Parameters of the LIF neuron model.
sparse (bool) – Whether to apply sparse activation functions (True) or not (False). Defaults to False.
dt (float) – Time step to use. Defaults to 0.001.
Initializes internal Module state, shared by both nn.Module and ScriptModule.
- class norse.torch.module.lif.LIFRecurrent(input_size, hidden_size, 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.)), **kwargs)[source]¶
Bases:
norse.torch.module.snn.SNNRecurrentA neuron layer that wraps a
LIFRecurrentCellin 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 = LIFRecurrent(2, 4) >>> l(data) # Returns tuple of (Tensor(10, 5, 4), LIFState)
- Parameters
input_size (int) – The number of input neurons
hidden_size (int) – The number of hidden neurons
p (LIFParameters) – The neuron parameters as a torch Module, which allows the module to configure neuron parameters as optimizable.
sparse (bool) – Whether to apply sparse activation functions (True) or not (False). Defaults to False.
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.
- class norse.torch.module.lif.LIFRecurrentCell(input_size, hidden_size, 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.)), **kwargs)[source]¶
Bases:
norse.torch.module.snn.SNNRecurrentCellModule that computes a single euler-integration step of a leaky integrate-and-fire (LIF) neuron-model with recurrence but without time. More specifically it implements one integration step of the following ODE
\[\begin{align*} \dot{v} &= 1/\tau_{\text{mem}} (v_{\text{leak}} - v + i) \ \dot{i} &= -1/\tau_{\text{syn}} i \end{align*}\]together with the jump condition
\[z = \Theta(v - v_{\text{th}})\]and transition equations
\[\begin{align*} v &= (1-z) v + z v_{\text{reset}} \ i &= i + w_{\text{input}} z_{\text{in}} \ i &= i + w_{\text{rec}} z_{\text{rec}} \end{align*}\]where \(z_{\text{rec}}\) and \(z_{\text{in}}\) are the recurrent and input spikes respectively.
Example
>>> data = torch.zeros(5, 2) # 5 batches, 2 neurons >>> l = LIFRecurrentCell(2, 4) >>> l(data) # Returns tuple of (Tensor(5, 4), LIFState)
- Parameters
input_size (int) – Size of the input. Also known as the number of input features.
hidden_size (int) – Size of the hidden state. Also known as the number of input features.
p (LIFParameters) – Parameters of the LIF neuron model.
sparse (bool) – Whether to apply sparse activation functions (True) or not (False). Defaults to False.
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.
Initializes internal Module state, shared by both nn.Module and ScriptModule.
