Neurons#

Subpackages#

Submodules#

conex.behaviors.neurons.axon module#

Axon mechanisms for neurons.

class conex.behaviors.neurons.axon.NeuronAxon(*args, max_delay=1, proximal_min_delay=0, distal_min_delay=0, apical_min_delay=0, **kwargs)[source]#

Bases: Behavior

Propagate the spikes and apply the delay mechanism.

Note: should be added after fire.

Parameters:

  • max_delay (int) – Maximum delay of all dendrites connected to the neurons. This value determines the delay buffer size.

  • proximal_min_delay (int) – Minimum delay of proximal dendrites. The default is 0.

  • distal_min_delay (int) – Minimum delay of distal dendrites. The default is 0.

  • apical_min_delay (int) – Minimum delay of apical dendrites. The default is 0.

initialize(neurons)[source]#

Sets the variables of the object. This method is called by the Network class when the object is added to the network.

Note: All sub-classes of Behavior overriding this method should call the super method to ensure everything is placed on the correct device.

Parameters:

object (TaggableObject) – Object possessing the behavior.

update_min_delay(neurons)[source]#
get_spike(neurons, delay)[source]#
forward(neurons)[source]#

Forward pass of the behavior. This method is called by the Network class per simulation iteration.

Parameters:

object (TaggableObject) – Object possessing the behavior.

training: bool#

conex.behaviors.neurons.dendrite module#

Dendrite structure and computation variants.

class conex.behaviors.neurons.dendrite.SimpleDendriteStructure(*args, Proximal_max_delay=1, Distal_max_delay=1, Apical_max_delay=None, proximal_min_delay=0, distal_min_delay=0, apical_min_delay=None, **kwargs)[source]#

Bases: Behavior

Defines the Structure of the dendrite. Gathers currents for the Computation Behavior.

Parameters:

  • proximal_max_delay (int) – Maximum delay of proximal dendrites. The default is 1. Set this to 0 to discard Proximal dendrite.

  • distal_max_delay (int) – Maximum delay of distal dendrites. The default is 1. Set this to 0 to discard Distal dendrite.

  • apical_max_delay (int) – Maximum delay of distal dendrites. The default is distal_max_delay + 1. Set this to 0 to discard Apical dendrite.

  • proximal_min_delay (int) – Minimum delay of proximal dendrites. The default is 0.

  • distal_min_delay (int) – Minimum delay of distal dendrites. The default is 0.

  • apical_min_delay (int) – Minimum delay of apical dendrites. The default is distal_min_delay + 1.

initialize(neurons)[source]#

Sets the variables of the object. This method is called by the Network class when the object is added to the network.

Note: All sub-classes of Behavior overriding this method should call the super method to ensure everything is placed on the correct device.

Parameters:

object (TaggableObject) – Object possessing the behavior.

update_min_delay(neurons)[source]#
forward(neurons)[source]#

Forward pass of the behavior. This method is called by the Network class per simulation iteration.

Parameters:

object (TaggableObject) – Object possessing the behavior.

training: bool#
class conex.behaviors.neurons.dendrite.SimpleDendriteComputation(*args, I_tau=None, apical_provocativeness=None, distal_provocativeness=None, **kwargs)[source]#

Bases: Behavior

Sums the different kind of dendrite entering the neurons.

Parameters:

  • apical_provocativeness (float) – The strength of the apical dendrites. The default is None.

  • distal_provocativeness (float) – The strength of the distal dendrites. The default is None.

  • I_tau (float) – Decaying factor to current. If None, at each step, current falls to zero.

initialize(neurons)[source]#

Sets the variables of the object. This method is called by the Network class when the object is added to the network.

Note: All sub-classes of Behavior overriding this method should call the super method to ensure everything is placed on the correct device.

Parameters:

object (TaggableObject) – Object possessing the behavior.

forward(neurons)[source]#

Forward pass of the behavior. This method is called by the Network class per simulation iteration.

Parameters:

object (TaggableObject) – Object possessing the behavior.

training: bool#

conex.behaviors.neurons.homeostasis module#

class conex.behaviors.neurons.homeostasis.ActivityBaseHomeostasis(activity_rate, window_size, updating_rate, *args, decay_rate=1.0, **kwargs)[source]#

Bases: Behavior

Homeostasis Based on the target activity of neurons.

Note: Threshold of neurons should be a population size tensor.

Parameters:

  • window_size (int) – The simulation steps to accumulate spikes.

  • activity_rate (int) – The expected number of spikes in a window.

  • updating_rate (float) – A scaler to change update effect with.

  • decay_rate (float) – A scaler to change updating_rate after each applied homeostasis. The default is 1.0

initialize(neurons)[source]#

Sets the variables of the object. This method is called by the Network class when the object is added to the network.

Note: All sub-classes of Behavior overriding this method should call the super method to ensure everything is placed on the correct device.

Parameters:

object (TaggableObject) – Object possessing the behavior.

forward(neurons)[source]#

Forward pass of the behavior. This method is called by the Network class per simulation iteration.

Parameters:

object (TaggableObject) – Object possessing the behavior.

training: bool#
class conex.behaviors.neurons.homeostasis.VoltageBaseHomeostasis(*args, target_voltage=None, max_ta=None, min_ta=None, eta_ip=0.001, **kwargs)[source]#

Bases: Behavior

Homeostasis base on the voltage rate of Neurons.

Parameters:

  • target_voltage (float) – The expected voltage of neuron. Defaults to None.

  • max_ta (float) – The desired maximum voltage for a neuron. If not provided, the value of target_voltage is used.

  • min_ta (float) – The desired minimum voltage for a neuron. If not provided, the value of target_voltage is used.

  • eta_ip (flaot) – The updating speed of the homeostasis process. The default is 0.001.

initialize(neurons)[source]#

Sets the variables of the object. This method is called by the Network class when the object is added to the network.

Note: All sub-classes of Behavior overriding this method should call the super method to ensure everything is placed on the correct device.

Parameters:

object (TaggableObject) – Object possessing the behavior.

forward(neurons)[source]#

Forward pass of the behavior. This method is called by the Network class per simulation iteration.

Parameters:

object (TaggableObject) – Object possessing the behavior.

training: bool#

conex.behaviors.neurons.setters module#

Helper behaviors to communicate with input and output layers

class conex.behaviors.neurons.setters.SensorySetter(*args, **kwargs)[source]#

Bases: Behavior

Gets input from Layer Object and sets as spike for the population.

initialize(neurons)[source]#

Sets the variables of the object. This method is called by the Network class when the object is added to the network.

Note: All sub-classes of Behavior overriding this method should call the super method to ensure everything is placed on the correct device.

Parameters:

object (TaggableObject) – Object possessing the behavior.

forward(neurons)[source]#

Forward pass of the behavior. This method is called by the Network class per simulation iteration.

Parameters:

object (TaggableObject) – Object possessing the behavior.

training: bool#
class conex.behaviors.neurons.setters.LocationSetter(*args, **kwargs)[source]#

Bases: Behavior

Gets location from Layer Object and sets as spike for the population.

initialize(neurons)[source]#

Sets the variables of the object. This method is called by the Network class when the object is added to the network.

Note: All sub-classes of Behavior overriding this method should call the super method to ensure everything is placed on the correct device.

Parameters:

object (TaggableObject) – Object possessing the behavior.

forward(neurons)[source]#

Forward pass of the behavior. This method is called by the Network class per simulation iteration.

Parameters:

object (TaggableObject) – Object possessing the behavior.

training: bool#

conex.behaviors.neurons.specs module#

General specifications needed for spiking neurons.

class conex.behaviors.neurons.specs.InherentNoise(*args, mode='rand', scale=1, offset=0, **kwargs)[source]#

Bases: Behavior

Applies noisy voltage to neurons in the population.

Parameters:

  • mode (str) – Mode to be used in initialize the tensor. Accepts similar values to Pymonntorch’s tensor function. Defaults to “rand”.

  • scale (float) – Scale factor to multiply to the tensor. Default is 1.0.

  • offset (function) – An offset value to be added to the tensor. Default is 0.0.

initialize(neurons)[source]#

Sets the variables of the object. This method is called by the Network class when the object is added to the network.

Note: All sub-classes of Behavior overriding this method should call the super method to ensure everything is placed on the correct device.

Parameters:

object (TaggableObject) – Object possessing the behavior.

forward(neurons)[source]#

Forward pass of the behavior. This method is called by the Network class per simulation iteration.

Parameters:

object (TaggableObject) – Object possessing the behavior.

training: bool#
class conex.behaviors.neurons.specs.Fire(*args, **kwargs)[source]#

Bases: Behavior

Asks neurons to Fire.

forward(neurons)[source]#

Forward pass of the behavior. This method is called by the Network class per simulation iteration.

Parameters:

object (TaggableObject) – Object possessing the behavior.

training: bool#
class conex.behaviors.neurons.specs.KWTA(k, *args, dimension=None, **kwargs)[source]#

Bases: Behavior

KWTA behavior of spiking neurons:

if v >= threshold then v = v_reset and all other spiked neurons are inhibited.

Note: Population should be built by NeuronDimension. and firing behavior should be added too.

Parameters:

  • k (int) – number of winners.

  • dimension (int, optional) – K-WTA on specific dimension. defaults to None.

initialize(neurons)[source]#

Sets the variables of the object. This method is called by the Network class when the object is added to the network.

Note: All sub-classes of Behavior overriding this method should call the super method to ensure everything is placed on the correct device.

Parameters:

object (TaggableObject) – Object possessing the behavior.

forward(neurons)[source]#

Forward pass of the behavior. This method is called by the Network class per simulation iteration.

Parameters:

object (TaggableObject) – Object possessing the behavior.

training: bool#