norse.torch.functional.lif_mc module

norse.torch.functional.lif_mc.lif_mc_feed_forward_step(input_tensor, state, g_coupling, 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.)), dt=0.001)[source]

Computes a single euler-integration feed forward step of a LIF multi-compartment neuron-model.

Parameters
Return type

Tuple[Tensor, LIFFeedForwardState]

norse.torch.functional.lif_mc.lif_mc_step(input_tensor, state, input_weights, recurrent_weights, g_coupling, 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.)), dt=0.001)[source]

Computes a single euler-integration step of a LIF multi-compartment neuron-model.

Parameters

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

  • s (LIFState) – current state of the neuron

  • input_weights (torch.Tensor) – synaptic weights for incoming spikes

  • recurrent_weights (torch.Tensor) – synaptic weights for recurrent spikes

  • g_coupling (torch.Tensor) – conductances between the neuron compartments

  • p (LIFParameters) – neuron parameters

  • dt (float) – Integration timestep to use

Return type

Tuple[Tensor, LIFState]