ngclearn.components.input_encoders package

Submodules

ngclearn.components.input_encoders.bernoulliCell module

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

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)

classmethod help()

reset()

ngclearn.components.input_encoders.ganglionCell module

class ngclearn.components.input_encoders.ganglionCell.RetinalGanglionCell(*args, **kwargs)

Bases: JaxComponent

A group of retinal ganglion cell that sense input stimuli and send out filtered signals (as output). Note that these simulated cells employ internal generalized filters based on either Gaussian or difference-of-Gaussian kernels) to recover historical receptive field processing effects.

— Cell Input Compartments: —

inputs - input (takes in external signals)

— Cell State Compartments: —

filter - filter (function applied to input)

— Cell Output Compartments: —

outputs - output

Parameters:

• name – the string name of this cell

• filter_type – string name of filter function (Default: identity) :Note: supported filters include “gaussian”, “difference_of_gaussian”

• sigma – standard deviation of (gaussian) kernel

• area_shape – shape of receptive field area of ganglion cells in this module (all together)

• n_cells – number of ganglion cells in this module

• patch_shape – shape of each ganglion cell’s receptive field area

• step_shape – the non-overlapping area between each pair (two) of ganglion cells

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

advance_state(t)

classmethod help()

reset()

ngclearn.components.input_encoders.gridCell module

ngclearn.components.input_encoders.latencyCell module

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

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)

calc_spike_times()

classmethod help()

reset()

ngclearn.components.input_encoders.phasorCell module

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

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)

classmethod help()

reset()

validate(dt=None, **validation_kwargs)

ngclearn.components.input_encoders.placeCell module

ngclearn.components.input_encoders.poissonCell module

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

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)

classmethod help()

reset()

ngclearn.components.input_encoders.populationCoderCell module

Module contents