norse.torch.functional¶

norse.torch.functional

Encoding
Logical
Regularization
Threshold functions
Temporal operations
Neuron models
Plasticity models
- Spike-time dependent plasticity (STDP)
- Tsodyks-Markram timing-dependent plasticity (TDP)

Encoding ¶

`constant_current_lif_encode`	Encodes input currents as fixed (constant) voltage currents, and simulates the spikes that occur during a number of timesteps/iterations (seq_length).
`gaussian_rbf`	A gaussian radial basis kernel that calculates the radial basis given a distance value (distance between \(x\) and a data value \(x'\), or \(\\|\mathbf{x} - \mathbf{x'}\\|^2\) below).
`euclidean_distance`	Simple euclidean distance metric.
`population_encode`	Encodes a set of input values into population codes, such that each singular input value is represented by a list of numbers (typically calculated by a radial basis kernel), whose length is equal to the out_features.
`poisson_encode`	Encodes a tensor of input values, which are assumed to be in the range [0,1] into a tensor of one dimension higher of binary values, which represent input spikes.
`poisson_encode_step`	Encodes a tensor of input values, which are assumed to be in the range [0,1] into a tensor of binary values, which represent input spikes.
`signed_poisson_encode`	Encodes a tensor of input values, which are assumed to be in the range [-1,1] into a tensor of one dimension higher of binary values, which represent input spikes.
`signed_poisson_encode_step`	Creates a poisson distributed signed spike vector, when
`spike_latency_lif_encode`	Encodes an input value by the time the first spike occurs.
`spike_latency_encode`	For all neurons, remove all but the first spike.
`lif_current_encoder`	Computes a single euler-integration step of a leaky integrator.
`lif_adex_current_encoder`	Computes a single euler-integration step of an adaptive exponential LIF neuron-model adapted from http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model.
`lif_ex_current_encoder`	Computes a single euler-integration step of a leaky integrator adapted from https://neuronaldynamics.epfl.ch/online/Ch5.S2.html.

Logical ¶

`logical_and`	Computes a logical and provided x and y are bitvectors.
`logical_xor`	Computes a logical xor provided x and y are bitvectors.
`logical_or`	Computes a logical or provided x and y are bitvectors.
`muller_c`	Computes the muller-c element next state provided x_1 and x_2 are bitvectors and y_prev is the previous state.
`posedge_detector`	Determines whether a transition from 0 to 1 has occured providing that z and z_prev are bitvectors

Regularization ¶

`regularize_step`	Takes one step for a regularizer that aggregates some information (based on the spike_accumulator function), which is pushed forward and returned for future inclusion in an error term.
`spike_accumulator`	A spike accumulator that aggregates spikes and returns the total sum as an integer.
`voltage_accumulator`	A spike accumulator that aggregates membrane potentials over time.

Threshold functions ¶

`heaviside`	A heaviside step function that truncates numbers <= 0 to 0 and everything else to 1.
`heavi_erfc_fn`
`heavi_tanh_fn`
`logistic_fn`
`heavi_circ_fn`
`circ_dist_fn`
`triangle_fn`
`super_fn`

Temporal operations ¶

lift

Creates a lifted version of the given activation function which applies the activation function in the temporal domain.

Neuron models ¶

Integrate-and-fire (IAF)¶

`IAFParameters`	Parametrization of a LIF neuron
`IAFFeedForwardState`	State of a feed forward IAF neuron
`iaf_feed_forward_step`

Izhikevich ¶

`IzhikevichParameters`	Parametrization of av Izhikevich neuron
`IzhikevichSpikingBehavior`	Spiking behavior of a Izhikevich neuron
`tonic_spiking`	Spiking behavior of a Izhikevich neuron
`tonic_bursting`	Spiking behavior of a Izhikevich neuron
`phasic_spiking`	Spiking behavior of a Izhikevich neuron
`phasic_bursting`	Spiking behavior of a Izhikevich neuron
`mixed_mode`	Spiking behavior of a Izhikevich neuron
`spike_frequency_adaptation`	Spiking behavior of a Izhikevich neuron
`class_1_exc`	Spiking behavior of a Izhikevich neuron
`class_2_exc`	Spiking behavior of a Izhikevich neuron
`spike_latency`	Spiking behavior of a Izhikevich neuron
`subthreshold_oscillation`	Spiking behavior of a Izhikevich neuron
`resonator`	Spiking behavior of a Izhikevich neuron
`izhikevich_feed_forward_step`

Leaky integrator ¶

`LIParameters`	Parameters of a leaky integrator
`LIState`	State of a leaky-integrator
`li_feed_forward_step`

Leaky integrate-and-fire (LIF)¶

`LIFParameters`	Parametrization of a LIF neuron
`LIFFeedForwardState`	State of a feed forward LIF neuron
`lif_feed_forward_step`	Computes a single euler-integration step for a lif neuron-model.
`lif_feed_forward_adjoint_step`	Implementes a single euler forward and adjoint backward step of a leaky integrate and fire neuron with current based exponential synapses.
`lif_feed_forward_adjoint_step_sparse`	Implementes a single euler forward and adjoint backward step of a leaky integrate and fire neuron with current based exponential synapses.

LIF, conductance based ¶

`CobaLIFParameters`	Parameters of conductance based LIF neuron.
`CobaLIFFeedForwardState`	State of a conductance based feed forward LIF neuron.
`coba_lif_feed_forward_step`	Euler integration step for a conductance based LIF neuron.

LIF, adaptive exponential ¶

`LIFAdExParameters`	Parametrization of an Adaptive Exponential Leaky Integrate and Fire neuron
`LIFAdExFeedForwardState`	State of a feed forward LIFAdEx neuron
`lif_adex_feed_forward_step`	Computes a single euler-integration step of an adaptive exponential LIF neuron-model adapted from http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model.
`lif_adex_current_encoder`	Computes a single euler-integration step of an adaptive exponential LIF neuron-model adapted from http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model.

LIF, exponential ¶

`LIFExParameters`	Parametrization of an Exponential Leaky Integrate and Fire neuron
`LIFExFeedForwardState`	State of a feed forward LIFEx neuron
`lif_ex_feed_forward_step`	Computes a single euler-integration step of an exponential LIF neuron-model adapted from https://neuronaldynamics.epfl.ch/online/Ch5.S2.html.
`lif_ex_current_encoder`	Computes a single euler-integration step of a leaky integrator adapted from https://neuronaldynamics.epfl.ch/online/Ch5.S2.html.

LIF, multicompartmental (MC)¶

`lif_mc_feed_forward_step`	Computes a single euler-integration feed forward step of a LIF multi-compartment neuron-model.
`lif_mc_refrac_feed_forward_step`

LIF, refractory ¶

`LIFRefracParameters`	Parameters of a LIF neuron with absolute refractory period.
`LIFRefracFeedForwardState`	State of a feed forward LIF neuron with absolute refractory period.
`lif_refrac_feed_forward_step`	Computes a single euler-integration step of a feed forward
`lif_refrac_feed_forward_adjoint_step`	Implementes a single euler forward and adjoint backward step of a leaky integrate and fire neuron with current based exponential synapses and a refractory period.

Long short-term memory (LSNN)¶

`LSNNParameters`	Parameters of an LSNN neuron
`LSNNFeedForwardState`	Integration state kept for a lsnn module
`lsnn_feed_forward_step`	Euler integration step for LIF Neuron with threshold adaptation.
`lsnn_feed_forward_adjoint_step`	Implementes a single euler forward and adjoint backward step of a lif neuron with adaptive threshhold and current based exponential synapses.

Plasticity models ¶

Spike-time dependent plasticity (STDP)¶

`STDPSensorParameters`	Parameters of an STDP sensor as it is used for event driven plasticity rules.
`STDPSensorState`	State of an event driven STDP sensor.
`stdp_sensor_step`	Event driven STDP rule.

Tsodyks-Markram timing-dependent plasticity (TDP)¶

`TsodyksMakramParameters`	Parameters of the Tsodyks-Makram Model
`TsodyksMakramState`	State of the Tsodyks-Makram Model, note that we are tracking the input current state separately.
`stp_step`	Euler integration step for Tsodyks Makram model of STP.

Norse v0.0.7

norse.torch.functional¶