ngclearn.components.synapses.hebbian package

Submodules

ngclearn.components.synapses.hebbian.BCMSynapse module

class ngclearn.components.synapses.hebbian.BCMSynapse.BCMSynapse(*args, **kwargs)

Bases: DenseSynapse

A synaptic cable that adjusts its efficacies in accordance with BCM (Bienenstock-Cooper-Munro) theory.

Mathematically, a synaptic update performed according to BCM theory is: | tau_w d(W_{ij})/dt = -w_decay W_{ij} + x_j * [y_i * (y_i - theta_i)] / theta_i | tau_theta d(theta_i)/dt = -theta_i + <(y_i)^2>_{batch} | where x_j is the pre-synaptic input, y_i is the post-synaptic output

Note that, in most literature related to BCM, the average value used for threshold theta can be assumed to be the average over all input patterns (as in a full dataset batch update) but a temporal average maintained for theta will “usually be equivalent” (and ngc-learn implements the threshold theta in terms of a leaky ODE to dynamically compute the temporal mean).

— Synapse Compartments: —

inputs - input (takes in external signals)

outputs - output signals (transformation induced by synapses)

weights - current value matrix of synaptic efficacies

key - JAX PRNG key

— Synaptic Plasticity Compartments: —

pre - pre-synaptic signal/value to drive 1st term of BCM update (x)

post - post-synaptic signal/value to drive 2nd term of BCM update (y)

theta - synaptic modification threshold (post-synaptic) variables

dWeights - current delta matrix containing changes to be applied to synapses

References:

Bienenstock, E. L., Cooper, L. N, and Munro, P. W. (1982). Theory for the

development of neuron selectivity: orientation specificity and binocular

interaction in visual cortex. Journal of Neuroscience, 2:32–48.

Parameters:

• name – the string name of this cell

• shape – tuple specifying shape of this synaptic cable (usually a 2-tuple with number of inputs by number of outputs)

• tau_w – synaptic update time constant

• tau_theta – threshold variable evolution time constant

• theta0 – initial condition for synaptic modification threshold

• w_bound – maximum value to enforce over newly computed efficacies (default: 0.); must > 0. to be used

• w_decay – synaptic decay factor (default: 0.)

• weight_init – a kernel to drive initialization of this synaptic cable’s values; typically a tuple with 1st element as a string calling the name of initialization to use

• resist_scale – a fixed scaling factor to apply to synaptic transform (Default: 1.), i.e., yields: out = ((W * Rscale) * in)

• p_conn – probability of a connection existing (default: 1.); setting this to < 1. will result in a sparser synaptic structure

evolve(t, dt)

classmethod help()

reset()

ngclearn.components.synapses.hebbian.eventSTDPSynapse module

class ngclearn.components.synapses.hebbian.eventSTDPSynapse.EventSTDPSynapse(*args, **kwargs)

Bases: DenseSynapse

A synaptic cable that adjusts its efficacies via event-driven, post-synaptic spike-timing-dependent plasticity (STDP).

— Synapse Compartments: —

inputs - input (takes in external signals)

outputs - output signals (transformation induced by synapses)

weights - current value matrix of synaptic efficacies

key - JAX PRNG key

— Synaptic Plasticity Compartments: —

pre_tols - pre-synaptic time-of-last-spike (tols) to drive 1st term of STDP update (takes in external values)

postSpike - post-synaptic spike to drive 2nd term of STDP update (takes in external signals)

dWeights - current delta matrix containing changes to be applied to synaptic efficacies

References:

Tavanaei, Amirhossein, Timothée Masquelier, and Anthony Maida.

“Representation learning using event-based STDP.” Neural Networks 105

(2018): 294-303.

Parameters:

• name – the string name of this cell

• shape – tuple specifying shape of this synaptic cable (usually a 2-tuple with number of inputs by number of outputs)

• eta – global learning rate initial condition

• lmbda – controls degree of synaptic disconnect (“lambda”)

• A_plus – strength of long-term potentiation (LTP)

• A_minus – strength of long-term depression (LTD)

• presyn_win_len – pre-synaptic window time, or how far back in time to look for the presence of a pre-synaptic spike, in milliseconds (default: 1 ms)

• w_bound – maximum value/magnitude any synaptic efficacy can be (default: 1)

• weight_init – a kernel to drive initialization of this synaptic cable’s values; typically a tuple with 1st element as a string calling the name of initialization to use

• resist_scale – a fixed scaling factor to apply to synaptic transform (Default: 1), i.e., yields: out = ((W * Rscale) * in) + b

• p_conn – probability of a connection existing (default: 1.); setting this to < 1. will result in a sparser synaptic structure

evolve(t, dt)

classmethod help()

reset()

ngclearn.components.synapses.hebbian.expSTDPSynapse module

class ngclearn.components.synapses.hebbian.expSTDPSynapse.ExpSTDPSynapse(*args, **kwargs)

Bases: DenseSynapse

A synaptic cable that adjusts its efficacies via trace-based form of spike-timing-dependent plasticity (STDP) based on an exponential weight dependence (the strength of which is controlled by a factor).

— Synapse Compartments: —

inputs - input (takes in external signals)

outputs - output signals (transformation induced by synapses)

weights - current value matrix of synaptic efficacies

key - JAX PRNG key

— Synaptic Plasticity Compartments: —

preSpike - pre-synaptic spike to drive 1st term of STDP update (takes in external signals)

postSpike - post-synaptic spike to drive 2nd term of STDP update (takes in external signals)

preTrace - pre-synaptic trace value to drive 1st term of STDP update (takes in external signals)

postTrace - post-synaptic trace value to drive 2nd term of STDP update (takes in external signals)

dWeights - current delta matrix containing changes to be applied to synaptic efficacies

References:

Nessler, Bernhard, et al. “Bayesian computation emerges in generic cortical

microcircuits through spike-timing-dependent plasticity.” PLoS computational

biology 9.4 (2013): e1003037.

Bi, Guo-qiang, and Mu-ming Poo. “Synaptic modification by correlated

activity: Hebb’s postulate revisited.” Annual review of neuroscience 24.1

(2001): 139-166.

Parameters:

• name – the string name of this cell

• shape – tuple specifying shape of this synaptic cable (usually a 2-tuple with number of inputs by number of outputs)

• A_plus – strength of long-term potentiation (LTP)

• A_minus – strength of long-term depression (LTD)

• exp_beta – controls effect of exponential Hebbian shift/dependency

• eta – global learning rate

• pretrace_target – controls degree of pre-synaptic disconnect, i.e., amount of decay (higher -> lower synaptic values)

• weight_init – a kernel to drive initialization of this synaptic cable’s values; typically a tuple with 1st element as a string calling the name of initialization to use

• resist_scale – a fixed scaling (resistance) factor to apply to synaptic transform (Default: 1.), i.e., yields: out = ((W * Rscale) * in) + b

• p_conn – probability of a connection existing (default: 1.); setting this to < 1. will result in a sparser synaptic structure

• w_bound – maximum value/magnitude any synaptic efficacy can be (default: 1)

• tau_w – synaptic weight decay coefficient to apply to STDP update

• weight_mask – synaptic binary masking matrix to apply (to enforce a constant sparse structure; default: None)

evolve()

classmethod help()

reset()

ngclearn.components.synapses.hebbian.hebbianSynapse module

class ngclearn.components.synapses.hebbian.hebbianSynapse.HebbianSynapse(*args, **kwargs)

Bases: DenseSynapse

A synaptic cable that adjusts its efficacies via a two-factor Hebbian adjustment rule.

— Synapse Compartments: —

inputs - input (takes in external signals)

outputs - output signals (transformation induced by synapses)

weights - current value matrix of synaptic efficacies

biases - current value vector of synaptic bias values

key - JAX PRNG key

— Synaptic Plasticity Compartments: —

pre - pre-synaptic signal to drive first term of Hebbian update (takes in external signals)

post - post-synaptic signal to drive 2nd term of Hebbian update (takes in external signals)

dWeights - current delta matrix containing changes to be applied to synaptic efficacies

dBiases - current delta vector containing changes to be applied to bias values

opt_params - locally-embedded optimizer statisticis (e.g., Adam 1st/2nd moments if adam is used)

Parameters:

• name – the string name of this cell

• shape – tuple specifying shape of this synaptic cable (usually a 2-tuple with number of inputs by number of outputs)

• eta – global learning rate

• weight_init – a kernel to drive initialization of this synaptic cable’s values; typically a tuple with 1st element as a string calling the name of initialization to use

• bias_init – a kernel to drive initialization of biases for this synaptic cable (Default: None, which turns off/disables biases)

• w_bound – maximum weight to softly bound this cable’s value matrix to; if set to 0, then no synaptic value bounding will be applied

• is_nonnegative – enforce that synaptic efficacies are always non-negative after each synaptic update (if False, no constraint will be applied)

• prior – a kernel to drive prior of this synaptic cable’s values; typically a tuple with 1st element as a string calling the name of prior to use and 2nd element as a floating point number calling the prior parameter lambda (Default: (‘constant’, 0.)) currently it supports “l1”/”lasso”/”laplacian” or “l2”/”ridge”/”gaussian” or “l1l2”/”elastic_net”. usage guide: prior = (‘l1’, 0.01) or prior = (‘lasso’, lmbda) prior = (‘l2’, 0.01) or prior = (‘ridge’, lmbda) prior = (‘l1l2’, (0.01, 0.01)) or prior = (‘elastic_net’, (lmbda, l1_ratio))

• sign_value – multiplicative factor to apply to final synaptic update before it is applied to synapses; this is useful if gradient descent style optimization is required (as Hebbian rules typically yield adjustments for ascent)

• optim_type –

  optimization scheme to physically alter synaptic values once an update is computed (Default: “sgd”); supported schemes include “sgd” and “adam”

  Note:

  technically, if “sgd” or “adam” is used but signVal = 1, then the ascent form of each rule is employed (signVal = -1) or a negative learning rate will mean a descent form of the optim_scheme is being employed

• pre_wght – pre-synaptic weighting factor (Default: 1.)

• post_wght – post-synaptic weighting factor (Default: 1.)

• resist_scale – a fixed scaling factor to apply to synaptic transform (Default: 1.), i.e., yields: out = ((W * Rscale) * in) + b

• p_conn – probability of a connection existing (default: 1.); setting this to < 1. will result in a sparser synaptic structure

calc_update()

evolve()

classmethod help()

load(directory: str)

The default load method for JaxComponents, it is expected to work with the default save. If the save method is modified this one will need to be modified too.

Parameters:

directory – The directory to load the .npz file.

reset()

save(directory: str)

The default save method for JaxComponents, it stores the values of all non-targeted (non-wired) compartments into a .npz file.

Parameters:

directory – The directory to save the .npz file.

ngclearn.components.synapses.hebbian.traceSTDPSynapse module

class ngclearn.components.synapses.hebbian.traceSTDPSynapse.TraceSTDPSynapse(*args, **kwargs)

Bases: DenseSynapse

A synaptic cable that adjusts its efficacies via trace-based form of spike-timing-dependent plasticity (STDP), including an optional power-scale dependence that can be equipped to the Hebbian adjustment (the strength of which is controlled by a scalar factor).

— Synapse Compartments: —

inputs - input (takes in external signals)

outputs - output signals (transformation induced by synapses)

weights - current value matrix of synaptic efficacies

key - JAX PRNG key

— Synaptic Plasticity Compartments: —

preSpike - pre-synaptic spike to drive 1st term of STDP update (takes in external signals)

postSpike - post-synaptic spike to drive 2nd term of STDP update (takes in external signals)

preTrace - pre-synaptic trace value to drive 1st term of STDP update (takes in external signals)

postTrace - post-synaptic trace value to drive 2nd term of STDP update (takes in external signals)

dWeights - current delta matrix containing changes to be applied to synaptic efficacies

eta - global learning rate (multiplier beyond A_plus and A_minus)

References:

Morrison, Abigail, Ad Aertsen, and Markus Diesmann. “Spike-timing-dependent

plasticity in balanced random networks.” Neural computation 19.6 (2007): 1437-1467.

Bi, Guo-qiang, and Mu-ming Poo. “Synaptic modification by correlated

activity: Hebb’s postulate revisited.” Annual review of neuroscience 24.1

(2001): 139-166.

Parameters:

• name – the string name of this cell

• shape – tuple specifying shape of this synaptic cable (usually a 2-tuple with number of inputs by number of outputs)

• A_plus – strength of long-term potentiation (LTP)

• A_minus – strength of long-term depression (LTD)

• eta – global learning rate initial value/condition (default: 1)

• mu – controls the power scale of the Hebbian shift

• pretrace_target – controls degree of pre-synaptic disconnect, i.e., amount of decay (higher -> lower synaptic values)

• weight_init – a kernel to drive initialization of this synaptic cable’s values; typically a tuple with 1st element as a string calling the name of initialization to use

• resist_scale – a fixed scaling factor to apply to synaptic transform (Default: 1.), i.e., yields: out = ((W * resistance) * in)

• p_conn – probability of a connection existing (default: 1); setting this to < 1. will result in a sparser synaptic structure

• w_bound – maximum value/magnitude any synaptic efficacy can be (default: 1)

• tau_w – synaptic weight decay coefficient to apply to STDP update

• weight_mask – synaptic binary masking matrix to apply (to enforce a constant sparse structure; default: None)

evolve()

classmethod help()

reset()

Module contents