nir.ir.neuron

Module Contents

Classes

CubaLIF	Current based leaky integrate and-fire-neuron model.
I	Integrator.
IF	Integrate-and-fire neuron model.
LI	Leaky integrator neuron model.
LIF	Leaky integrate and-fire-neuron model.

class nir.ir.neuron.CubaLIF

Bases: nir.ir.node.NIRNode

Current based leaky integrate and-fire-neuron model.

The current based leaky integrate-and-fire neuron model is defined by the following equations:

\[\tau_{syn} \dot {I} = - I + w_{in} S\]

\[\tau_{mem} \dot {v} = (v_{leak} - v) + R I\]

\[\begin{split}z = \begin{cases} 1 & v > v_{threshold} \\ 0 & else \end{cases}\end{split}\]

\[\begin{split}v = \begin{cases} v-v_{threshold} & z=1 \\ v & else \end{cases}\end{split}\]

Where \(\tau_{syn}\) is the synaptic time constant, \(\tau_{mem}\) is the membrane time constant, \(R\) is the resistance, \(v_{leak}\) is the leak voltage, \(v_{threshold}\) is the firing threshold, \(w_{in}\) is the input current weight (elementwise), and \(S\) is the input spike.

tau_syn: numpy.ndarray

tau_mem: numpy.ndarray

r: numpy.ndarray

v_leak: numpy.ndarray

v_threshold: numpy.ndarray

w_in: numpy.ndarray = 1.0

input_type: Optional[Dict[str, numpy.ndarray]]

output_type: Optional[Dict[str, numpy.ndarray]]

metadata: Dict[str, Any]

__post_init__()

class nir.ir.neuron.I

Bases: nir.ir.node.NIRNode

Integrator.

The integrator neuron model is defined by the following equation:

\[\dot{v} = R I\]

r: numpy.ndarray

input_type: Optional[Dict[str, numpy.ndarray]]

output_type: Optional[Dict[str, numpy.ndarray]]

metadata: Dict[str, Any]

__post_init__()

class nir.ir.neuron.IF

Bases: nir.ir.node.NIRNode

Integrate-and-fire neuron model.

The integrate-and-fire neuron model is defined by the following equations:

\[\dot{v} = R I\]

\[\begin{split}z = \begin{cases} 1 & v > v_{thr} \\ 0 & else \end{cases}\end{split}\]

\[\begin{split}v = \begin{cases} v-v_{thr} & z=1 \\ v & else \end{cases}\end{split}\]

r: numpy.ndarray

v_threshold: numpy.ndarray

input_type: Optional[Dict[str, numpy.ndarray]]

output_type: Optional[Dict[str, numpy.ndarray]]

metadata: Dict[str, Any]

__post_init__()

class nir.ir.neuron.LI

Bases: nir.ir.node.NIRNode

Leaky integrator neuron model.

The leaky integrator neuron model is defined by the following equation:

\[\tau \dot{v} = (v_{leak} - v) + R I\]

Where \(\tau\) is the time constant, \(v\) is the membrane potential, \(v_{leak}\) is the leak voltage, \(R\) is the resistance, and \(I\) is the input current.

tau: numpy.ndarray

r: numpy.ndarray

v_leak: numpy.ndarray

input_type: Optional[Dict[str, numpy.ndarray]]

output_type: Optional[Dict[str, numpy.ndarray]]

metadata: Dict[str, Any]

__post_init__()

class nir.ir.neuron.LIF

Bases: nir.ir.node.NIRNode

Leaky integrate and-fire-neuron model.

The leaky integrate-and-fire neuron model is defined by the following equations:

\[\tau \dot{v} = (v_{leak} - v) + R I\]

\[\begin{split}z = \begin{cases} 1 & v > v_{thr} \\ 0 & else \end{cases}\end{split}\]

\[\begin{split}v = \begin{cases} v-v_{thr} & z=1 \\ v & else \end{cases}\end{split}\]

Where \(\tau\) is the time constant, \(v\) is the membrane potential, \(v_{leak}\) is the leak voltage, \(R\) is the resistance, \(v_{threshold}\) is the firing threshold, and \(I\) is the input current.

tau: numpy.ndarray

r: numpy.ndarray

v_leak: numpy.ndarray

v_threshold: numpy.ndarray

input_type: Optional[Dict[str, numpy.ndarray]]

output_type: Optional[Dict[str, numpy.ndarray]]

metadata: Dict[str, Any]

__post_init__()