hxtorch.spiking.functional

Modules

hxtorch.spiking.functional.dropout	Custom BatchDropout function
hxtorch.spiking.functional.iaf	Integrate and fire neurons
hxtorch.spiking.functional.li	Leaky-integrate neurons
hxtorch.spiking.functional.lif	Leaky-integrate and fire neurons
hxtorch.spiking.functional.linear(input, weight)	Wrap linear to allow signature inspection
hxtorch.spiking.functional.refractory	Refractory update for neurons with refractory behaviour
hxtorch.spiking.functional.spike_source	Define different input spike sources
hxtorch.spiking.functional.superspike	Surrograte gradient for SuperSpike.
hxtorch.spiking.functional.threshold(input, …)	Selection of the used threshold function. :param input: Input tensor to threshold function. :param method: The string indicator of the the threshold function. Currently supported: ‘super_spike’. :param alpha: Parameter controlling the slope of the surrogate derivative in case of ‘superspike’. :return: Returns the tensor of the threshold function.
hxtorch.spiking.functional.unterjubel	Autograd function to ‘unterjubel’ (german for ‘inject’) hardware observables and allow correct gradient back-propagation.

Classes

CUBAIAFParams(tau_mem_inv, tau_syn_inv, …)	Parameters for IAF integration and backward path
CUBALIFParams(tau_mem_inv, tau_syn_inv, …)	Parameters for CUBA LIF integration and backward path
CUBALIParams(tau_mem_inv, tau_syn_inv, v_leak)	Parameters for CUBA LI integration and backward path
SuperSpike(*args, **kwargs)	Define Surrogate Gradient ‘SuperSpike’ (negative side of Fast Sigmoid) See: https://arxiv.org/abs/1705.11146

Functions

hxtorch.spiking.functional.batch_dropout(input: torch.Tensor, mask: torch.Tensor) → torch.Tensor

Applies a dropout mask to a batch of inputs.

Parameters

• input – The input tensor to apply dropout to.

• mask – The dropout mask. Entires in the mask which are False will disable their corresponding entry in input.

Returns

The input tensor with dropout mask applied.

hxtorch.spiking.functional.cuba_iaf_integration(input: torch.Tensor, params: NamedTuple, hw_data: Optional[torch.Tensor] = None, dt: float = 1e-06) → Tuple[torch.Tensor, torch.Tensor]

Leaky-integrate and fire neuron integration for realization of simple spiking neurons with exponential synapses. Integrates according to:

v^{t+1} = dt / au_{men} * (v_l - v^t + i^t) + v^t i^{t+1} = i^t * (1 - dt / au_{syn}) + x^t z^{t+1} = 1 if v^{t+1} > params.v_th v^{t+1} = v_reset if z^{t+1} == 1

Assumes i^0, v^0 = 0., v_reset :note: One dt synaptic delay between input and output :param input: Input spikes in shape (batch, time, neurons). :param params: LIFParams object holding neuron parameters. :return: Returns the spike trains in shape and membrane trace as a tuple.

Both tensors are of shape (batch, time, neurons).

hxtorch.spiking.functional.cuba_li_integration(input: torch.Tensor, params: hxtorch.spiking.functional.li.CUBALIParams, hw_data: Optional[torch.Tensor] = None, dt: float = 1e-06) → torch.Tensor

Leaky-integrate neuron integration for realization of readout neurons with exponential synapses. Integrates according to:

v^{t+1} = dt / au_{mem} * (v_l - v^t + i^t) + v^t i^{t+1} = i^t * (1 - dt / au_{syn}) + x^t

Assumes i^0, v^0 = 0. :note: One dt synaptic delay between input and output :param input: Input spikes in shape (batch, time, neurons). :param params: LIParams object holding neuron parameters. :param dt: Integration step width

Returns

Returns the membrane trace in shape (batch, time, neurons).

hxtorch.spiking.functional.cuba_lif_integration(input: torch.Tensor, params: hxtorch.spiking.functional.lif.CUBALIFParams, hw_data: Optional[torch.Tensor] = None, dt: float = 1e-06) → Tuple[torch.Tensor, torch.Tensor]

Leaky-integrate and fire neuron integration for realization of simple spiking neurons with exponential synapses. Integrates according to:

i^{t+1} = i^t * (1 - dt / au_{syn}) + x^t v^{t+1} = dt / au_{men} * (v_l - v^t + i^t) + v^t z^{t+1} = 1 if v^{t+1} > params.v_th v^{t+1} = params.v_reset if z^{t+1} == 1

Assumes i^0, v^0 = 0, v_leak :note: One dt synaptic delay between input and output

TODO: Issue 3992

Parameters

• input – Input spikes in shape (batch, time, neurons).

• params – LIFParams object holding neuron parameters.

• dt – Step width of integration.

Returns

Returns the spike trains in shape and membrane trace as a tuple. Both tensors are of shape (batch, time, neurons).

hxtorch.spiking.functional.cuba_refractory_iaf_integration(input: torch.Tensor, params: NamedTuple, hw_data: Optional[torch.Tensor] = None, dt: float = 1e-06) → Tuple[torch.Tensor, torch.Tensor]

Integrate and fire neuron integration for realization of simple spiking neurons with exponential synapses and refractory period. Integrates according to:

v^{t+1} = dt / au_{men} * i^t + v^t i^{t+1} = i^t * (1 - dt / au_{syn}) + x^t z^{t+1} = 1 if v^{t+1} > params.v_th v^{t+1} = params.v_reset if z^{t+1} == 1 or ref^t > 0 ref^{t+1} -= 1 ref^{t+1} = params.tau_ref if z^{t+1} == 1

Assumes i^0, v^0 = 0., v_reset :note: One dt synaptic delay between input and output :param input: Input spikes in shape (batch, time, neurons). :param params: LIFParams object holding neuron parameters. :return: Returns the spike trains in shape and membrane trace as a tuple.

Both tensors are of shape (batch, time, neurons).

hxtorch.spiking.functional.input_neuron(input: torch.Tensor, hw_data: Optional[torch.Tensor] = None) → torch.Tensor

Input neuron, forwards spikes without modification in non-hardware runs but injects loop-back recorded spikes if available.

Parameters

• input – Input spike tensor.

• hw_data – Loop-back spikes, if available.

Returns

Returns the input spike tensor.

hxtorch.spiking.functional.linear(input: torch.Tensor, weight: torch.nn.parameter.Parameter, bias: Optional[torch.nn.parameter.Parameter] = None) → torch.Tensor

Wrap linear to allow signature inspection

hxtorch.spiking.functional.linear_sparse(input: torch.Tensor, weight: torch.nn.parameter.Parameter, connections: Optional[torch.Tensor] = None, bias: Optional[torch.nn.parameter.Parameter] = None) → torch.Tensor

Wrap linear to allow signature inspection. Disable inactive connections in weight tensor.

Parameters

• input – The input to be multiplied with the params tensor weight.

• weight – The weight parameter tensor. This tensor is expected to be dense since pytorch, see issue: 4039.

• bias – The bias of the linear operation.

• connections – A dense boolean connection mask indicating active connections. If None, the weight tensor remains untouched.

hxtorch.spiking.functional.threshold(input: torch.Tensor, method: str, alpha: float) → torch.Tensor

Selection of the used threshold function. :param input: Input tensor to threshold function. :param method: The string indicator of the the threshold function.

Currently supported: ‘super_spike’.

Parameters

alpha – Parameter controlling the slope of the surrogate derivative in case of ‘superspike’.

Returns

Returns the tensor of the threshold function.