# norse.torch.module.lif_adex.LIFAdExCell#

class norse.torch.module.lif_adex.LIFAdExCell(p: LIFAdExParameters = (tensor(4), tensor(0.0200), tensor(0.5000), tensor(2.), tensor(200.), tensor(100.), tensor(0.), tensor(1.), tensor(0.), 'super', 100.0), **kwargs)[source]#

Computes a single euler-integration step of a feed-forward exponential LIF neuron-model without recurrence, adapted from http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model. It takes as input the input current as generated by an arbitrary torch module or function. 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 \\ \dot{a} &= 1/\tau_{\text{ada}} \left( a_{current} (V - v_{\text{leak}}) - a \right) \end{align*}\end{split}

together with the jump condition

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

and transition equations

$i = i + i_{\text{in}}$

where $$i_{\text{in}}$$ is meant to be the result of applying an arbitrary pytorch module (such as a convolution) to input spikes.

Parameters:

p (LIFAdExParameters): Parameters of the LIFEx neuron model. dt (float): Time step to use.

Examples:
>>> batch_size = 16
>>> lif_ex = LIFAdExCell()
>>> data = torch.randn(batch_size, 20, 30)
>>> output, s0 = lif_ex(data)

__init__(p: LIFAdExParameters = (tensor(4), tensor(0.0200), tensor(0.5000), tensor(2.), tensor(200.), tensor(100.), tensor(0.), tensor(1.), tensor(0.), 'super', 100.0), **kwargs)[source]#

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

Methods

 __init__([p]) Initialize internal Module state, shared by both nn.Module and ScriptModule. add_module(name, module) Add a child module to the current module. apply(fn) Apply fn recursively to every submodule (as returned by .children()) as well as self. bfloat16() Casts all floating point parameters and buffers to bfloat16 datatype. buffers([recurse]) Return an iterator over module buffers. children() Return an iterator over immediate children modules. compile(*args, **kwargs) Compile this Module's forward using torch.compile(). cpu() Move all model parameters and buffers to the CPU. cuda([device]) Move all model parameters and buffers to the GPU. double() Casts all floating point parameters and buffers to double datatype. eval() Set the module in evaluation mode. extra_repr() Set the extra representation of the module. float() Casts all floating point parameters and buffers to float datatype. forward(input_tensor[, state]) Define the computation performed at every call. get_buffer(target) Return the buffer given by target if it exists, otherwise throw an error. get_extra_state() Return any extra state to include in the module's state_dict. get_parameter(target) Return the parameter given by target if it exists, otherwise throw an error. get_submodule(target) Return the submodule given by target if it exists, otherwise throw an error. half() Casts all floating point parameters and buffers to half datatype. initial_state(x) ipu([device]) Move all model parameters and buffers to the IPU. load_state_dict(state_dict[, strict, assign]) Copy parameters and buffers from state_dict into this module and its descendants. modules() Return an iterator over all modules in the network. named_buffers([prefix, recurse, ...]) Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself. named_children() Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself. named_modules([memo, prefix, remove_duplicate]) Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself. named_parameters([prefix, recurse, ...]) Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself. parameters([recurse]) Return an iterator over module parameters. register_backward_hook(hook) Register a backward hook on the module. register_buffer(name, tensor[, persistent]) Add a buffer to the module. register_forward_hook(hook, *[, prepend, ...]) Register a forward hook on the module. register_forward_pre_hook(hook, *[, ...]) Register a forward pre-hook on the module. register_full_backward_hook(hook[, prepend]) Register a backward hook on the module. register_full_backward_pre_hook(hook[, prepend]) Register a backward pre-hook on the module. register_load_state_dict_post_hook(hook) Register a post hook to be run after module's load_state_dict is called. register_module(name, module) Alias for add_module(). register_parameter(name, param) Add a parameter to the module. register_state_dict_pre_hook(hook) Register a pre-hook for the state_dict() method. requires_grad_([requires_grad]) Change if autograd should record operations on parameters in this module. set_extra_state(state) Set extra state contained in the loaded state_dict. share_memory() state_dict(*args[, destination, prefix, ...]) Return a dictionary containing references to the whole state of the module. to(*args, **kwargs) Move and/or cast the parameters and buffers. to_empty(*, device[, recurse]) Move the parameters and buffers to the specified device without copying storage. train([mode]) Set the module in training mode. type(dst_type) Casts all parameters and buffers to dst_type. xpu([device]) Move all model parameters and buffers to the XPU. zero_grad([set_to_none]) Reset gradients of all model parameters.

Attributes

 T_destination call_super_init dump_patches training