norse.torch.functional.coba_lif module

class norse.torch.functional.coba_lif.CobaLIFFeedForwardState(v: torch.Tensor, g_e: torch.Tensor, g_i: torch.Tensor)[source]

Bases: tuple

State of a conductance based feed forward LIF neuron.

Parameters

Create new instance of CobaLIFFeedForwardState(v, g_e, g_i)

g_e: torch.Tensor

Alias for field number 1

g_i: torch.Tensor

Alias for field number 2

v: torch.Tensor

Alias for field number 0

class norse.torch.functional.coba_lif.CobaLIFParameters(tau_syn_exc_inv: torch.Tensor = tensor(0.2000), tau_syn_inh_inv: torch.Tensor = tensor(0.2000), c_m_inv: torch.Tensor = tensor(5.), g_l: torch.Tensor = tensor(0.2500), e_rev_I: torch.Tensor = tensor(- 100), e_rev_E: torch.Tensor = tensor(60), v_rest: torch.Tensor = tensor(- 20), v_reset: torch.Tensor = tensor(- 70), v_thresh: torch.Tensor = tensor(- 10), method: str = 'super', alpha: float = 100.0)[source]

Bases: tuple

Parameters of conductance based LIF neuron.

Parameters

  • tau_syn_exc_inv (torch.Tensor) – inverse excitatory synaptic input time constant

  • tau_syn_inh_inv (torch.Tensor) – inverse inhibitory synaptic input time constant

  • c_m_inv (torch.Tensor) – inverse membrane capacitance

  • g_l (torch.Tensor) – leak conductance

  • e_rev_I (torch.Tensor) – inhibitory reversal potential

  • e_rev_E (torch.Tensor) – excitatory reversal potential

  • v_rest (torch.Tensor) – rest membrane potential

  • v_reset (torch.Tensor) – reset membrane potential

  • v_thresh (torch.Tensor) – threshold membrane potential

  • method (str) – method to determine the spike threshold (relevant for surrogate gradients)

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

Create new instance of CobaLIFParameters(tau_syn_exc_inv, tau_syn_inh_inv, c_m_inv, g_l, e_rev_I, e_rev_E, v_rest, v_reset, v_thresh, method, alpha)

alpha: float

Alias for field number 10

c_m_inv: torch.Tensor

Alias for field number 2

e_rev_E: torch.Tensor

Alias for field number 5

e_rev_I: torch.Tensor

Alias for field number 4

g_l: torch.Tensor

Alias for field number 3

method: str

Alias for field number 9

tau_syn_exc_inv: torch.Tensor

Alias for field number 0

tau_syn_inh_inv: torch.Tensor

Alias for field number 1

v_reset: torch.Tensor

Alias for field number 7

v_rest: torch.Tensor

Alias for field number 6

v_thresh: torch.Tensor

Alias for field number 8

class norse.torch.functional.coba_lif.CobaLIFState(z: torch.Tensor, v: torch.Tensor, g_e: torch.Tensor, g_i: torch.Tensor)[source]

Bases: tuple

State of a conductance based LIF neuron.

Parameters

Create new instance of CobaLIFState(z, v, g_e, g_i)

g_e: torch.Tensor

Alias for field number 2

g_i: torch.Tensor

Alias for field number 3

v: torch.Tensor

Alias for field number 1

z: torch.Tensor

Alias for field number 0

norse.torch.functional.coba_lif.coba_lif_feed_forward_step(input_tensor, state, p=CobaLIFParameters(tau_syn_exc_inv=tensor(0.2000), tau_syn_inh_inv=tensor(0.2000), c_m_inv=tensor(5.), g_l=tensor(0.2500), e_rev_I=tensor(- 100), e_rev_E=tensor(60), v_rest=tensor(- 20), v_reset=tensor(- 70), v_thresh=tensor(- 10), method='super', alpha=100.0), dt=0.001)[source]

Euler integration step for a conductance based LIF neuron.

Parameters
Return type

Tuple[Tensor, CobaLIFFeedForwardState]

norse.torch.functional.coba_lif.coba_lif_step(input_tensor, state, input_weights, recurrent_weights, p=CobaLIFParameters(tau_syn_exc_inv=tensor(0.2000), tau_syn_inh_inv=tensor(0.2000), c_m_inv=tensor(5.), g_l=tensor(0.2500), e_rev_I=tensor(- 100), e_rev_E=tensor(60), v_rest=tensor(- 20), v_reset=tensor(- 70), v_thresh=tensor(- 10), method='super', alpha=100.0), dt=0.001)[source]

Euler integration step for a conductance based LIF neuron.

Parameters

  • input_tensor (torch.Tensor) – the input spikes at the current time step

  • s (CobaLIFState) – current state of the neuron

  • input_weights (torch.Tensor) – input weights (sign determines contribution to inhibitory / excitatory input)

  • recurrent_weights (torch.Tensor) – recurrent weights (sign determines contribution to inhibitory / excitatory input)

  • p (CobaLIFParameters) – parameters of the neuron

  • dt (float) – Integration time step

Return type

Tuple[Tensor, CobaLIFState]