LIFExParameters

class norse.torch.functional.lif_ex.LIFExParameters(delta_T: torch.Tensor = tensor(0.5000), tau_syn_inv: torch.Tensor = tensor(200.), tau_mem_inv: torch.Tensor = tensor(100.), v_leak: torch.Tensor = tensor(0.), v_th: torch.Tensor = tensor(1.), v_reset: torch.Tensor = tensor(0.), method: str = 'super', alpha: float = 100.0)[source]

Parametrization of an Exponential Leaky Integrate and Fire neuron

Parameters:

delta_T (torch.Tensor): sharpness or speed of the exponential growth in mV tau_syn_inv (torch.Tensor): inverse synaptic time

constant (\(1/\tau_\text{syn}\)) in 1/ms

tau_mem_inv (torch.Tensor): inverse membrane time

constant (\(1/\tau_\text{mem}\)) in 1/ms

v_leak (torch.Tensor): leak potential in mV v_th (torch.Tensor): threshold potential in mV v_reset (torch.Tensor): reset potential in mV method (str): method to determine the spike threshold

(relevant for surrogate gradients)

alpha (float): hyper parameter to use in surrogate gradient computation

alpha: float

Alias for field number 7

count(value, /)

Return number of occurrences of value.

delta_T: torch.Tensor

Alias for field number 0

index(value, start=0, stop=9223372036854775807, /)

Return first index of value.

Raises ValueError if the value is not present.

method: str

Alias for field number 6

tau_mem_inv: torch.Tensor

Alias for field number 2

tau_syn_inv: torch.Tensor

Alias for field number 1

v_leak: torch.Tensor

Alias for field number 3

v_reset: torch.Tensor

Alias for field number 5

v_th: torch.Tensor

Alias for field number 4