ngclearn.components.input_encoders package

Submodules

ngclearn.components.input_encoders.bernoulliCell module

class ngclearn.components.input_encoders.bernoulliCell.BernoulliCell(*args, **kwargs)[source]

Bases: JaxComponent

A Bernoulli cell that produces spikes by sampling a Bernoulli distribution on-the-fly (to produce data-scaled Bernoulli spike trains).

— Cell Input Compartments: —
inputs - input (takes in external signals – should be probabilities w/ values in [0,1])
— Cell State Compartments: —
key - JAX PRNG key
— Cell Output Compartments: —
outputs - output
tols - time-of-last-spike
Parameters:

  • name – the string name of this cell

  • n_units – number of cellular entities (neural population size)

  • batch_size – batch size dimension of this cell (Default: 1)

advance_state(t)[source]
classmethod help()[source]
reset()[source]

ngclearn.components.input_encoders.latencyCell module

class ngclearn.components.input_encoders.latencyCell.LatencyCell(*args, **kwargs)[source]

Bases: JaxComponent

A (nonlinear) latency encoding (spike) cell; produces a time-lagged set of spikes on-the-fly.

— Cell Input Compartments: —
inputs - input (takes in external signals)
— Cell State Compartments: —
targ_sp_times - target-spike-time
mask - spike-ordering mask
key - JAX PRNG key
— Cell Output Compartments: —
outputs - output
tols - time-of-last-spike
Parameters:

  • name – the string name of this cell

  • n_units – number of cellular entities (neural population size)

  • tau – time constant for model used to calculate firing time (Default: 1 ms)

  • threshold – sensory input features below this threhold value will fire at final step in time of this latency coded spike train

  • first_spike_time – time of first allowable spike (ms) (Default: 0 ms)

  • linearize – should the linear latency encoding scheme be used? (otherwise, defaults to logarithmic latency encoding)

  • normalize –

    normalize the latency code such that final spike(s) occur a pre-specified number of simulation steps “num_steps”? (Default: False)

    Note:

    if this set to True, you will need to choose a useful value for the “num_steps” argument (>1), depending on how many steps simulated

  • clip_spikes – should values under threshold be removed/suppressed? (default: False)

  • num_steps – number of discrete time steps to consider for normalized latency code (only useful if “normalize” is set to True) (Default: 1)

  • batch_size – batch size dimension of this cell (Default: 1)

advance_state(t)[source]
calc_spike_times()[source]
classmethod help()[source]
reset()[source]

ngclearn.components.input_encoders.phasorCell module

class ngclearn.components.input_encoders.phasorCell.PhasorCell(*args, **kwargs)[source]

Bases: JaxComponent

A phasor cell that emits a pulse at a regular interval.

— Cell Input Compartments: —
inputs - input (takes in external signals)
— Cell State Compartments: —
key - JAX PRNG key
angles - current angle of phasor
— Cell Output Compartments: —
outputs - output of phasor cell
tols - time-of-last-spike
Parameters:

  • name – the string name of this cell

  • n_units – number of cellular entities (neural population size)

  • target_freq – maximum frequency (in Hertz) of this spike train (must be > 0.)

  • batch_size – batch size dimension of this cell (Default: 1)

advance_state(t, dt)[source]
classmethod help()[source]
reset()[source]
validate(dt=None, **validation_kwargs)[source]

ngclearn.components.input_encoders.poissonCell module

class ngclearn.components.input_encoders.poissonCell.PoissonCell(*args, **kwargs)[source]

Bases: JaxComponent

A Poisson cell that samples a homogeneous Poisson process on-the-fly to produce a spike train.

— Cell Input Compartments: —
inputs - input (takes in external signals)
— Cell State Compartments: —
key - JAX PRNG key
— Cell Output Compartments: —
outputs - output
tols - time-of-last-spike
Parameters:

  • name – the string name of this cell

  • n_units – number of cellular entities (neural population size)

  • target_freq – maximum frequency (in Hertz) of this Bernoulli spike train (must be > 0.)

  • batch_size – batch size dimension of this cell (Default: 1)

advance_state(t, dt)[source]
classmethod help()[source]
reset()[source]

Module contents