ngclearn.components.neurons.graded package

Submodules

ngclearn.components.neurons.graded.bernoulliErrorCell module

class ngclearn.components.neurons.graded.bernoulliErrorCell.BernoulliErrorCell(*args, **kwargs)

Bases: JaxComponent

A simple (non-spiking) Bernoulli error cell - this is a fixed-point solution of a mismatch signal. Specifically, this cell operates as a factorized multivariate Bernoulli distribution.

— Cell Input Compartments: —

p - predicted probability (or logits) of positive trial (takes in external signals)

target - desired/goal value (takes in external signals)

modulator - modulation signal (takes in optional external signals)

mask - binary/gating mask to apply to error neuron calculations

— Cell Output Compartments: —

L - local loss function embodied by this cell

dp - derivative of L w.r.t. p (or logits, if p = sigmoid(logits))

dtarget - derivative of L w.r.t. target

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)

• input_logits – if True, treats compartment p as logits and will apply a sigmoidal link, i.e., _p = sigmoid(p), to obtain the param p for Bern(X=1; p)

advance_state(dt)

batched_reset(batch_size)

classmethod help()

reset()

ngclearn.components.neurons.graded.gaussianErrorCell module

class ngclearn.components.neurons.graded.gaussianErrorCell.GaussianErrorCell(*args, **kwargs)

Bases: JaxComponent

A simple (non-spiking) Gaussian error cell - this is a fixed-point calculation of a mismatch signal. Specifically, this error cell offers a configurable variance and calculates its local free energy (Gaussian log likelihood).

— Cell Input Compartments: —

mu - predicted value (takes in external signals)

Sigma - predicted covariance (takes in external signals), or, if just a scalar, then it’s sigma^2

target - desired/goal value (takes in external signals)

modulator - modulation signal (takes in optional external signals)

mask - binary/gating mask to apply to error neuron calculations

— Cell Output Compartments: —

L - local loss function embodied by this cell

dmu - derivative of L w.r.t. mu

dSigma - derivative of L w.r.t. Sigma

dtarget - derivative of L w.r.t. target

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)

• sigma – initial/fixed value for prediction covariance matrix (𝚺) in multivariate gaussian distribution; Note that if the compartment Sigma is never used, then this cell assumes that the covariance collapses to a constant/fixed sigma^2, i.e., Sigma = sigma^2, where sigma is a scalar standard deviation argument (Default: 1)

advance_state(dt)

batched_reset(batch_size)

classmethod help()

reset()

ngclearn.components.neurons.graded.laplacianErrorCell module

class ngclearn.components.neurons.graded.laplacianErrorCell.LaplacianErrorCell(*args, **kwargs)

Bases: JaxComponent

A simple (non-spiking) Laplacian error cell - this is a fixed-point solution of a mismatch/error signal.

— Cell Input Compartments: —

shift - predicted shift value (takes in external signals)

Scale - predicted scale (takes in external signals)

target - desired/goal value (takes in external signals)

modulator - modulation signal (takes in optional external signals)

mask - binary/gating mask to apply to error neuron calculations

— Cell Output Compartments: —

L - local loss function embodied by this cell

dshift - derivative of L w.r.t. shift

dScale - derivative of L w.r.t. Scale

dtarget - derivative of L w.r.t. target

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)

• scale – initial/fixed value for prediction scale matrix in multivariate laplacian distribution; Note that if the compartment Scale is never used, then this cell assumes that the scale collapses to a constant/fixed scale

advance_state(dt)

batched_reset(batch_size)

classmethod help()

reset()

ngclearn.components.neurons.graded.leakyNoiseCell module

class ngclearn.components.neurons.graded.leakyNoiseCell.LeakyNoiseCell(*args, **kwargs)

Bases: JaxComponent

A non-spiking cell driven by the gradient dynamics entailed by a continuous-time noisy, leaky recurrent state.

Reference: https://pmc.ncbi.nlm.nih.gov/articles/PMC4771709/

The specific differential equation that characterizes this cell is (for adjusting x) is:

tau_x * dx/dt = -x + j_rec + j_in + sqrt(2 alpha (sigma_pre)^2) * eps; and,

r = f(x) + (eps * sigma_post).

where j_in is the set of incoming input signals

and j_rec is the set of recurrent input signals

and eps is a sample of unit Gaussian noise, i.e., eps ~ N(0, 1)

and f(x) is the rectification function

and sigma_pre is the pre-rectification noise applied to membrane x

and sigma_post is the post-rectification noise applied to rates f(x)

— Cell Input Compartments: —

j_input - input (bottom-up) electrical/stimulus current (takes in external signals)

j_recurrent - recurrent electrical/stimulus pressure

— Cell State Compartments —

x - noisy rate activity / current value of state

— Cell Output Compartments: —

r - post-rectified activity, e.g., fx(x) = relu(x)

r_prime - post-rectified temporal derivative, e.g., dfx(x) = d_relu(x)

Parameters:

• name – the string name of this cell

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

• tau_x – state membrane time constant (milliseconds)

• act_fx – rectification function (Default: “relu”)

• output_scale – factor to multiply output of nonlinearity of this cell by (Default: 1.)

• integration_type –

  type of integration to use for this cell’s dynamics; current supported forms include “euler” (Euler/RK-1 integration) and “midpoint” or “rk2” (midpoint method/RK-2 integration) (Default: “euler”)

  Note:

  setting the integration type to the midpoint method will increase the accuracy of the estimate of the cell’s evolution at an increase in computational cost (and simulation time)

• sigma_pre – pre-rectification noise scaling factor / standard deviation (Default: 0.1)

• sigma_post – post-rectification noise scaling factor / standard deviation (Default: 0.)

• leak_scale – degree to which membrane leak should be scaled (Default: 1)

advance_state(t, dt)

batched_reset(batch_size)

classmethod help()

reset()

ngclearn.components.neurons.graded.rateCell module

class ngclearn.components.neurons.graded.rateCell.RateCell(*args, **kwargs)

Bases: JaxComponent

A non-spiking cell driven by the gradient dynamics of neural generative coding-driven predictive processing.

The specific differential equation that characterizes this cell is (for adjusting v, given current j, over time) is:

tau_m * dz/dt = lambda * prior(z) + (j + j_td)

where j is the set of general incoming input signals (e.g., message-passed signals)

and j_td is taken to be the set of top-down pressure signals

— Cell Input Compartments: —

j - input pressure (takes in external signals)

j_td - input/top-down pressure input (takes in external signals)

— Cell State Compartments —

z - rate activity

— Cell Output Compartments: —

zF - post-activation function activity, i.e., fx(z)

Parameters:

• name – the string name of this cell

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

• tau_m – membrane/state time constant (milliseconds)

• prior –

  a kernel for specifying the type of centered scale-shift distribution to impose over neuronal dynamics, applied to each neuron or dimension within this component (Default: (“gaussian”, 0)); this is a tuple with 1st element containing a string name of the distribution one wants to use while the second value is a leak rate scalar that controls the influence/weighting that this distribution has on the dynamics; for example, (“laplacian, 0.001”) means that a centered laplacian distribution scaled by 0.001 will be injected into this cell’s dynamics ODE each step of simulated time

  Note:

  supported scale-shift distributions include “laplacian”, “cauchy”, “exp”, and “gaussian”

• act_fx – string name of activation function/nonlinearity to use

• output_scale – factor to multiply output of nonlinearity of this cell by (Default: 1.)

• integration_type –

  type of integration to use for this cell’s dynamics; current supported forms include “euler” (Euler/RK-1 integration) and “midpoint” or “rk2” (midpoint method/RK-2 integration) (Default: “euler”)

  Note:

  setting the integration type to the midpoint method will increase the accuray of the estimate of the cell’s evolution at an increase in computational cost (and simulation time)

• resist_scale – a scaling factor applied to incoming pressure j (default: 1)

advance_state(dt)

batched_reset(batch_size)

classmethod help()

reset()

ngclearn.components.neurons.graded.rewardErrorCell module

class ngclearn.components.neurons.graded.rewardErrorCell.RewardErrorCell(*args, **kwargs)

Bases: JaxComponent

A reward prediction error (RPE) cell.

— Cell Input Compartments: —

reward - current reward signal at time t

accum_reward - current accumulated episodic reward signal

— Cell Output Compartments: —

mu - current moving average prediction of reward at time t

rpe - current reward prediction error (RPE) signal

accum_reward - current accumulated episodic reward signal (IF online predictor not used)

Parameters:

• name – the string name of this cell

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

• alpha – decay factor to apply to (exponential) moving average prediction

• ema_window_len – exponential moving average window length – for use only in evolve step for updating episodic reward signals; (default: 10)

• use_online_predictor – use online prediction of reward signal (default: True) – if set to False, then reward prediction will only occur upon a call to this cell’s evolve function

advance_state(dt)

evolve(dt)

classmethod help()

reset()

Module contents