Source code for norse.torch.functional.tsodyks_makram

from typing import NamedTuple, Tuple

import torch

[docs] class TsodyksMakramState(NamedTuple): """State of the Tsodyks-Makram Model, note that we are tracking the input current state separately. Parameters: u (torch.Tensor): utilization parameter. x (torch.Tensor): fraction of remaining available resources. """ u: torch.Tensor x: torch.Tensor
[docs] class TsodyksMakramParameters(NamedTuple): """Parameters of the Tsodyks-Makram Model Parameters: tau_f_inv (float): facilitation time constant (in ms). tau_s_inv (float): synaptic time constant (in ms). tau_d_inv (float): depressing time constant (in ms). U (float): size of the jump in the utilization variable. """ tau_f_inv: float = 1 / (50.0e-3) tau_s_inv: float = 1 / (20.0e-3) tau_d_inv: float = 1 / (750.0e-3) U: float = 0.45
[docs] def stp_step( z: torch.Tensor, s: TsodyksMakramState, p: TsodyksMakramParameters, dt: float = 0.001, ) -> Tuple[torch.Tensor, TsodyksMakramState]: """Euler integration step for Tsodyks Makram model of STP. Reference: Parameters: z (torch.Tensor): Input spikes s (TsodyksMakramState): State of the Tsodyks-Makram model p (TsodyksMakramParameters): Parameters of the Tsodyks-Makram model dt (float): Euler integration timestep """ du = -p.tau_f_inv * s.u u_p = s.u + dt * du + p.U * (1 - s.u) * z dx = p.tau_d_inv * (1 - s.x) x_p = s.x + dt * dx - u_p * s.x * z return u_p * s.x * z, TsodyksMakramState(u=u_p, x=x_p)