DoubleExponentialCurrent

class DoubleExponentialCurrent(shape: Sequence[int] | int, step_time: float, *, spike_charge: float, tc_decay: float, tc_rise: float, delay: float = 0.0, spike_interp_mode: Literal['nearest', 'previous'] = 'previous', interp_tol: float = 0.0, current_overbound: float | None = 0.0, spike_overbound: bool | None = False, batch_size: int = 1, inplace: bool = False)

Bases: SpikeMixin, InfernoSynapse

Exponentially applied exponentially decaying current-based synapse.

\[\begin{split}\begin{align*} I(t) &= I_d(t) - I_r(t) \\ I_d(t + \Delta t) &= I_d(t) \exp \left(-\frac{\Delta t}{\tau_d}\right) + \frac{Q}{\tau_d - \tau_r} \left[t = t_f\right] \\ I_r(t + \Delta t) &= I_r(t) \exp \left(-\frac{\Delta t}{\tau_r}\right) + \frac{Q}{\tau_d - \tau_r} \left[t = t_f\right] \end{align*}\end{split}\]

spike_

RecordTensor interface for spikes.

pos_current_

RecordTensor interface for added currents.

neg_current_

RecordTensor interface for subtracted currents.

Parameters:

• shape (Sequence[int] | int) – shape of the group of synapses being simulated.

• step_time (float) – length of a simulation time step, \(\Delta t\), in \(\text{ms}\).

• spike_charge (float) – charge carried by each presynaptic spike, \(Q\), in \(\text{pC}\).

• tc_decay (float) – exponential time constant for current decay, \(\tau_d\), in \(\text{ms}\).

• tc_rise (float) – exponential time constant for current rise, \(\tau_r\), in \(\text{ms}\).

• delay (float, optional) – maximum supported delay, in \(\text{ms}\). Defaults to 0.0.

• spike_interp_mode (Literal["nearest", "previous"], optional) – interpolation mode for spike selectors between observations. Defaults to "nearest".

• interp_tol (float, optional) – maximum difference in time from an observation to treat as co-occurring, in \(\text{ms}\). Defaults to 0.0.

• current_overbound (float | None, optional) – value to replace currents out of bounds, uses values at observation limits if None. Defaults to 0.0.

• spike_overbound (bool | None, optional) – value to replace spikes out of bounds, uses values at observation limits if None. Defaults to False.

• batch_size (int, optional) – size of input batches for simulation. Defaults to 1.

• inplace (bool) – if write operations on RecordTensor attributes should be performed with in-place operations. Defaults to False.

See also

For more details and references, visit Double Exponential in the zoo.

clear(**kwargs) → None

Resets synapses to their resting state.

property current: Tensor

Currents of the synapses at present, in nanoamperes.

Parameters:

value (torch.Tensor) – new synapse currents.

Returns:

present synaptic currents.

Return type:

torch.Tensor

Important

The setter for this property does nothing as current is a derived value. Use the pos_current and neg_current setters for this instead.

current_at(selector: Tensor) → Tensor

Retrieves previous synaptic currents, in nanoamperes.

Parameters:

selector (torch.Tensor) – time before present for which synaptic currents should be retrieved, in \(\text{ms}\).

Returns:

selected synaptic currents.

Return type:

torch.Tensor

Shape

selector:

\(B \times N_0 \times \cdots \times [D]\)

return:

\(B \times N_0 \times \cdots \times [D]\)

Where:

• \(B\) is the batch size.

• \(N_0 \times \cdots\) is the shape of the synapse.

• \(D\) is the number of selectors per synapse.

forward(*inputs: Tensor, **kwargs) → Tensor

Runs a simulation step of the synaptic dynamics.

Parameters:

*inputs (torch.Tensor) – input spikes to the synapse.

Returns:

synaptic currents after simulation step.

Return type:

torch.Tensor

property neg_current: Tensor

Negative component of currents of the synapses at present, in nanoamperes.

Parameters:

value (torch.Tensor) – new negative component of synapse currents.

Returns:

present negative component of synaptic currents.

Return type:

torch.Tensor

neg_current_at(selector: Tensor) → Tensor

Retrieves negative component of previous synaptic currents, in nanoamperes.

Parameters:

selector (torch.Tensor) – time before present for which negative component of synaptic currents should be retrieved, in \(\text{ms}\).

Returns:

selected negative component of synaptic currents.

Return type:

torch.Tensor

Shape

selector:

\(B \times N_0 \times \cdots \times [D]\)

return:

\(B \times N_0 \times \cdots \times [D]\)

Where:

• \(B\) is the batch size.

• \(N_0 \times \cdots\) is the shape of the synapse.

• \(D\) is the number of selectors per synapse.

classmethod partialconstructor(spike_charge: float, tc_decay: float, tc_rise: float, spike_interp_mode: Literal['nearest', 'previous'] = 'previous', interp_tol: float = 0.0, current_overbound: float | None = 0.0, spike_overbound: bool | None = False, inplace: bool = False)

Returns a function with a common signature for synapse construction.

Parameters:

• spike_charge (float) – charge carried by each presynaptic spike, in \(\text{pC}\).

• tc_decay (float) – exponential time constant for current decay, in \(\text{ms}\).

• tc_rise (float) – exponential time constant for current rise, in \(\text{ms}\).

• spike_interp_mode (Literal["nearest", "previous"], optional) – interpolation mode for spike selectors between observations. Defaults to "nearest".

• interp_tol (float, optional) – maximum difference in time from an observation to treat as co-occurring, in \(\text{ms}\). Defaults to 0.0.

• current_overbound (float | None, optional) – value to replace currents out of bounds, uses values at observation limits if None. Defaults to 0.0.

• spike_overbound (bool | None, optional) – value to replace spikes out of bounds, uses values at observation limits if None. Defaults to False.

• inplace (bool) – if write operations on RecordTensor attributes should be performed with in-place operations. Defaults to False.

Returns:

partial constructor for synapse.

Return type:

SynapseConstructor

property pos_current: Tensor

Positive component of currents of the synapses at present, in nanoamperes.

Parameters:

value (torch.Tensor) – new positive component of synapse currents.

Returns:

present positive component of synaptic currents.

Return type:

torch.Tensor

pos_current_at(selector: Tensor) → Tensor

Retrieves positive component of previous synaptic currents, in nanoamperes.

Parameters:

selector (torch.Tensor) – time before present for which positive component of synaptic currents should be retrieved, in \(\text{ms}\).

Returns:

selected positive component of synaptic currents.

Return type:

torch.Tensor

Shape

selector:

\(B \times N_0 \times \cdots \times [D]\)

return:

\(B \times N_0 \times \cdots \times [D]\)

Where:

• \(B\) is the batch size.

• \(N_0 \times \cdots\) is the shape of the synapse.

• \(D\) is the number of selectors per synapse.