Neuron types

These neuron types can be used in any place that Nengo neuron types can be used.

class nengo_extras.neurons.SoftLIFRate(sigma=1.0, **lif_args)

LIF neuron with smoothing around the firing threshold.

This is a rate version of the LIF neuron whose tuning curve has a continuous first derivative, due to the smoothing around the firing threshold. It can be used as a substitute for LIF neurons in deep networks during training, and then replaced with LIF neurons when running the network [R1313].

Parameters:

sigma : float

Amount of smoothing around the firing threshold. Larger values mean more smoothing.

amplitude : float

Scaling factor on the output. If 1 (default), output rates correspond to LIF neuron model rates.

tau_rc : float

Membrane RC time constant, in seconds. Affects how quickly the membrane voltage decays to zero in the absence of input (larger = slower decay).

tau_ref : float

Absolute refractory period, in seconds. This is how long the membrane voltage is held at zero after a spike.

References

[R1313]

(1, 2) E. Hunsberger & C. Eliasmith (2015). Spiking Deep Networks with LIF Neurons. arXiv Preprint, 1510. https://arxiv.org/abs/1510.08829

step_math(dt, J, output)

Compute rates in Hz for input current (incl. bias)

class nengo_extras.neurons.FastLIF(tau_rc=0.02, tau_ref=0.002, min_voltage=0, amplitude=1)

Faster version of the leaky integrate-and-fire (LIF) neuron model.

This neuron model is faster than LIF but does not produce the ideal firing rate for larger dt due to linearization of the tuning curves.

Utilities

nengo_extras.neurons.spikes2events(t, spikes)

Return an event-based representation of spikes (i.e. spike times)

nengo_extras.neurons.rates_isi(t, spikes, midpoint=False, interp='zero')

Estimate firing rates from spikes using ISIs.

Parameters:

t : (M,) array_like

The times at which raw spike data (spikes) is defined.

spikes : (M, N) array_like

The raw spike data from N neurons.

midpoint : bool, optional

If true, place interpolation points at midpoints of ISIs. Otherwise, the points are placed at the beginning of ISIs.

interp : string, optional

Interpolation type, passed to scipy.interpolate.interp1d as the kind parameter.

Returns:

rates : (M, N) array_like

The estimated neuron firing rates.

nengo_extras.neurons.rates_kernel(t, spikes, kind='gauss', tau=0.04)

Estimate firing rates from spikes using a kernel.

Parameters:

t : (M,) array_like

The times at which raw spike data (spikes) is defined.

spikes : (M, N) array_like

The raw spike data from N neurons.

kind : str {‘expon’, ‘gauss’, ‘expogauss’, ‘alpha’}, optional

The type of kernel to use. ‘expon’ is an exponential kernel, ‘gauss’ is a Gaussian (normal) kernel, ‘expogauss’ is an exponential followed by a Gaussian, and ‘alpha’ is an alpha function kernel.

tau : float

The time constant for the kernel. The optimal value will depend on the firing rate of the neurons, with a longer tau preferred for lower firing rates. The default value of 0.04 works well across a wide range of firing rates.