spikegen

Generate spike trains in pure Python with zero dependencies. Poisson, gamma, inverse-Gaussian and lognormal renewal, regular, Bernoulli, and inhomogeneous processes, returned as plain sorted lists of spike times, with explicit seeds for reproducibility.

Install

pip install spikegen

30-second example

from spikegen import homogeneous_poisson, regular, gamma_renewal, with_refractory

homogeneous_poisson(rate=50.0, duration=2.0, seed=0)   # Poisson spikes in [0, 2)
regular(rate=10.0, duration=1.0)                        # [0.0, 0.1, 0.2, ...]
gamma_renewal(rate=20.0, shape=2.0, duration=1.0, seed=0)  # more regular than Poisson

from spikegen import inverse_gaussian_renewal

# Inverse-Gaussian (Wald) renewal: ISIs ~ IG(mu, lam); the LIF first-passage law.
inverse_gaussian_renewal(mu=0.05, lam=0.2, duration=1.0, seed=0)

from spikegen import lognormal_renewal

# Lognormal renewal: ISIs are lognormal with mean 20 ms and CV 0.6 (common cortical ISI fit).
lognormal_renewal(mean=0.02, cv=0.6, duration=1.0, seed=0)

spikes = homogeneous_poisson(rate=80.0, duration=1.0, seed=0)
with_refractory(spikes, refractory=0.002)              # enforce a 2 ms refractory period

from spikegen import population
population(lambda s: homogeneous_poisson(rate=50.0, duration=2.0, seed=s), units=10, seed=0)

from spikegen import bernoulli, jitter

# Discrete-time Bernoulli process: 1 ms bins, 50 Hz rate over 1 second
bernoulli(rate=50.0, duration=1.0, dt=0.001, seed=0)

# Jitter: add Gaussian noise (sigma=2 ms) to each spike time, useful for surrogate data
spikes = homogeneous_poisson(rate=40.0, duration=1.0, seed=0)
jitter(spikes, sigma=0.002, seed=1)

Times are in the same units as 1 / rate (seconds if rate is in Hz). Seeded processes are reproducible: the same seed gives the same train.

Optional NumPy fast path

The core package has zero runtime dependencies. For long, high-rate Poisson trains there is an optional vectorized generator behind the [fast] extra:

pip install spikegen[fast]

from spikegen import homogeneous_poisson_numpy

# Same homogeneous Poisson process as homogeneous_poisson, but vectorized with NumPy.
homogeneous_poisson_numpy(rate=1000.0, duration=10.0, seed=0)

homogeneous_poisson_numpy(rate, duration, seed) draws exponential inter-spike intervals in batches and takes their cumulative sum with NumPy instead of looping in Python, which is much faster for long, high-rate trains. NumPy is imported lazily only inside this function, so the pure-Python homogeneous_poisson stays the default and the package still imports with no dependencies; calling homogeneous_poisson_numpy without [fast] installed raises an ImportError.

The fast path is reproducible for a fixed seed but is not bit-identical to homogeneous_poisson for the same seed: it uses NumPy's Generator (PCG64), a different random stream from the pure path's random.Random (Mersenne Twister). The two are statistically equivalent: both produce a homogeneous Poisson process with the same rate, so their spike counts, mean rate, and inter-spike-interval distribution agree.

Why this exists

Generating synthetic spike trains is a daily need, but the generators live inside heavy frameworks: elephant requires neo and quantities, pyspike is NumPy-based, and other options are old or partial. spikegen is a small, dependency-free generator that returns plain lists of floats, so reproducible spike trains are one import away. It pairs with spikedist: generate trains, then measure the distance between them.

Processes

• regular(rate, duration): evenly spaced spikes. Deterministic.
• homogeneous_poisson(rate, duration, seed): constant-rate Poisson process.
• inhomogeneous_poisson(rate_fn, max_rate, duration, seed): time-varying rate by thinning.
• gamma_renewal(rate, shape, duration, seed): gamma inter-spike intervals; shape 1 is Poisson, larger shape is more regular.
• inverse_gaussian_renewal(mu, lam, duration, seed): inverse-Gaussian (Wald) inter-spike intervals IG(mu, lam) with mean mu and variance mu**3 / lam, so the squared coefficient of variation is mu / lam. Large lam gives regular spiking, small lam gives irregular spiking. This is the first-passage-time distribution of a drift-diffusion (perfect integrate-and-fire) neuron, a principled companion to gamma_renewal. Intervals are sampled with the Michael-Schucany-Haas algorithm (Michael, Schucany, Haas 1976).
• lognormal_renewal(mean, cv, duration, seed): lognormal inter-spike intervals, the common empirical fit for cortical ISI distributions. Parameterized directly by the ISI mean and coefficient of variation cv: the underlying normal N(mu, sigma**2) follows from sigma**2 = ln(1 + cv**2) and mu = ln(mean) - sigma**2 / 2, and each interval is exp(mu + sigma * Z) with Z ~ N(0, 1). This gives E[ISI] = mean and CV[ISI] = cv exactly in expectation; the lognormal CV depends only on sigma, CV = sqrt(exp(sigma**2) - 1). Small cv gives nearly regular spiking, large cv gives bursty, irregular spiking. A natural companion to gamma_renewal and inverse_gaussian_renewal.
• bernoulli(rate, duration, dt, seed): discrete-time Bernoulli process. Time is divided into bins of width dt; each bin fires at its start time with probability rate * dt. Raises ValueError when rate * dt > 1.
• with_refractory(times, refractory): drop spikes within a minimum interval.
• jitter(times, sigma, seed): add independent Gaussian jitter (standard deviation sigma) to each spike time and return sorted results. Useful for surrogate or null datasets that destroy precise timing while preserving spike count. sigma = 0 sorts without change.
• population(make, units, seed): build a population of trains by calling make(seed) once per unit with independent, reproducible child seeds derived from the base seed.

All parameters after the first are keyword-only and explicit.

Testing

pip install -e ".[dev]"
pytest

Tests cover exact values for the deterministic generators, seeded reproducibility, the rate-bound and ordering invariants, and the validation paths, with property tests via Hypothesis.

Contributing

Issues and pull requests are welcome. See CONTRIBUTING.md.

License

MIT. See LICENSE.