devices.dynapse.DynapseNeurons

Bases: GenericNeurons

DynapseNeurons stores the core computational properties of a Dynap-SE network

Attributes overview

`Iscale`	the scaling current
`dt`	the time step for the forward-Euler ODE solver
`Idc`	Constant DC current injected to membrane in Amperes
`If_nmda`	NMDA gate soft cut-off current setting the NMDA gating voltage in Amperes
`Igain_ahp`	gain bias current of the spike frequency adaptation block in Amperes
`Igain_mem`	gain bias current for neuron membrane in Amperes
`Igain_syn`	gain bias current of synaptic gates (AMPA, GABA, NMDA, SHUNT) combined in Amperes
`Ipulse_ahp`	bias current setting the pulse width for spike frequency adaptation block `t_pulse_ahp` in Amperes
`Ipulse`	bias current setting the pulse width for neuron membrane `t_pulse` in Amperes
`Iref`	bias current setting the refractory period `t_ref` in Amperes
`Ispkthr`	spiking threshold current, neuron spikes if \(I_{mem} > I_{spkthr}\) in Amperes
`Itau_ahp`	Spike frequency adaptation leakage current setting the time constant `tau_ahp` in Amperes
`Itau_mem`	Neuron membrane leakage current setting the time constant `tau_mem` in Amperes
`Itau_syn`	AMPA, GABA, NMDA, SHUNT) synapses combined leakage current setting the time constant `tau_syn` in Amperes
`Iw_ahp`	spike frequency adaptation weight current of the neurons of the core in Amperes
`input_nodes`	The input nodes attached to this module
`output_nodes`	The output nodes attached to this module
`name`	An arbitrary name attached to this specific `GraphModule`
`computational_module`	The computational module that acts as the source for this graph module

Methods overview

`__init__`(input_nodes, output_nodes, name, ...)
`add_input`(node)	Add a `GraphNode` as an input source to this module, and connect it
`add_output`(node)	Add a `GraphNode` as an output of this module, and connect it
`clear_inputs`()	Remove all `GraphNode` s as inputs of this module
`clear_outputs`()	Remove all `GraphNode` s as outputs of this module
`current_attrs`()	current_attrs lists all current paramters stored inside DynapseNeurons computational graph
`get_full`()	get_full creates a dictionary of parameteric current attributes with extended current values
`merge`(graph_list)	merge combines a list of computational `DynapseNeuron` objects into one.
`remove_input`(node)	Remove a `GraphNode` as an input of this module, and disconnect it
`remove_output`(node)	Remove a `GraphNode` as an output of this module, and disconnect it

Idc: IntVector | FloatVector: Constant DC current injected to membrane in Amperes

If_nmda: IntVector | FloatVector: NMDA gate soft cut-off current setting the NMDA gating voltage in Amperes

Igain_ahp: IntVector | FloatVector: gain bias current of the spike frequency adaptation block in Amperes

Igain_mem: IntVector | FloatVector: gain bias current for neuron membrane in Amperes

Igain_syn: IntVector | FloatVector: gain bias current of synaptic gates (AMPA, GABA, NMDA, SHUNT) combined in Amperes

Ipulse: IntVector | FloatVector: bias current setting the pulse width for neuron membrane t_pulse in Amperes

Ipulse_ahp: IntVector | FloatVector: bias current setting the pulse width for spike frequency adaptation block t_pulse_ahp in Amperes

Iref: IntVector | FloatVector: bias current setting the refractory period t_ref in Amperes

Iscale: float | None = None: the scaling current

Ispkthr: IntVector | FloatVector: spiking threshold current, neuron spikes if \(I_{mem} > I_{spkthr}\) in Amperes

Itau_ahp: IntVector | FloatVector: Spike frequency adaptation leakage current setting the time constant tau_ahp in Amperes

Itau_mem: IntVector | FloatVector: Neuron membrane leakage current setting the time constant tau_mem in Amperes

Itau_syn: IntVector | FloatVector: AMPA, GABA, NMDA, SHUNT) synapses combined leakage current setting the time constant tau_syn in Amperes

Iw_ahp: IntVector | FloatVector: spike frequency adaptation weight current of the neurons of the core in Amperes

__gain_current converts a gain ratio to a amplifier gain current using the leakage current provided

Parameters:

r (float) – the desired amplifier gain ratio
Itau (FloatVector) – the depended leakage current

Returns:

an amplifier gain current

Return type:

FloatVector

__get_equal_shape() → Tuple[int]

get_equal_shape makes sure that the all arguments have the same shape

Raises:: ValueError – Attribute shapes does not match!
Returns:: the equal shape of all the arguments
Return type:: Tuple[int]

__init__(input_nodes: ~rockpool.graph.graph_base.SetList[GraphNode], output_nodes: ~rockpool.graph.graph_base.SetList[GraphNode], name: str, computational_module: ~typing.Any, Idc: int | ~numpy.ndarray | ~torch.Tensor | ~jax.numpy.array | float = <factory>, If_nmda: int | ~numpy.ndarray | ~torch.Tensor | ~jax.numpy.array | float = <factory>, Igain_ahp: int | ~numpy.ndarray | ~torch.Tensor | ~jax.numpy.array | float = <factory>, Igain_mem: int | ~numpy.ndarray | ~torch.Tensor | ~jax.numpy.array | float = <factory>, Igain_syn: int | ~numpy.ndarray | ~torch.Tensor | ~jax.numpy.array | float = <factory>, Ipulse_ahp: int | ~numpy.ndarray | ~torch.Tensor | ~jax.numpy.array | float = <factory>, Ipulse: int | ~numpy.ndarray | ~torch.Tensor | ~jax.numpy.array | float = <factory>, Iref: int | ~numpy.ndarray | ~torch.Tensor | ~jax.numpy.array | float = <factory>, Ispkthr: int | ~numpy.ndarray | ~torch.Tensor | ~jax.numpy.array | float = <factory>, Itau_ahp: int | ~numpy.ndarray | ~torch.Tensor | ~jax.numpy.array | float = <factory>, Itau_mem: int | ~numpy.ndarray | ~torch.Tensor | ~jax.numpy.array | float = <factory>, Itau_syn: int | ~numpy.ndarray | ~torch.Tensor | ~jax.numpy.array | float = <factory>, Iw_ahp: int | ~numpy.ndarray | ~torch.Tensor | ~jax.numpy.array | float = <factory>, Iscale: float | None = None, dt: float | None = None) → None

__leakage_current(C: float) → float | ndarray | Tensor | array

__leakage_current uses default layout configuration and converts a time constant to a leakage current using the conversion method defined in DynapSimTime module

Parameters:

tc (FloatVector) – the time constant in seconds
C (float) – the capacitance value in Farads

Returns:

the leakage current

Return type:

FloatVector

__pulse_current(C: float) → float | ndarray | Tensor | array

__pulse_current uses default layout configuration and converts a pulse width to a pulse current using the conversion method defined in DynapSimTime module

Parameters:

t_pw (FloatVector) – the pulse width in seconds
C (float) – the capacitance value in Farads

Returns:

a pulse current

Return type:

FloatVector

__scale(scale: float) → List[float]

__scale converts any FloatVector to list and scales

Parameters:

v (FloatVector) – the float vector of interest
scale (float) – the scaling factor

Returns:

a float list

Return type:

List[float]

_convert_from converts a graph module to DynapseNeuron structure. Uses default parameter

NOTE:

LIF does not have equivalent computation for

AHP (After-Hyper-Polarization)
NMDA Voltage Depended Gating

Therefore : Itau_ahp, If_nmda, Igain_ahp, Ipulse_ahp, and, Iw_ahp currents are zero.

Parameters:

mod (GraphModule) – the reference graph module
r_gain_mem (FloatVector, optional) – neuron membrane gain ratio, defaults to default_gain_ratios[“r_gain_mem”]
r_gain_syn (FloatVector, optional) – synapse gain ratio, defaults to default_gain_ratios[“r_gain_ampa”]
t_pulse (FloatVector, optional) – the spike pulse width for neuron membrane in seconds, defaults to default_time_constants[“t_pulse”]
t_ref (FloatVector, optional) – refractory period of the neurons in seconds, defaults to default_time_constants[“t_ref”]
C_pulse (FloatVector, optional) – pulse-width creation sub-circuit capacitance in Farads, defaults to default_layout[“C_pulse”]
C_ref (FloatVector, optional) – refractory period sub-circuit capacitance in Farads, defaults to default_layout[“C_ref”]
C_mem (FloatVector, optional) – neuron membrane capacitance in Farads, defaults to default_layout[“C_mem”]
C_syn (FloatVector, optional) – synaptic capacitance in Farads, defaults to default_layout[“C_syn”]
Iscale (float, optional) – the scaling current, defaults to default_weights[“Iscale”]

Raises:

ValueError – graph module cannot be converted

Returns:

DynapseNeurons object instance

Return type:

DynapseNeurons

classmethod _factory(size_in: int, size_out: int, name: str | None = None, computational_module: Any | None = None, *args, **kwargs) → GraphModuleBase

Build a new GraphModule or GraphModule subclass, with new input and output GraphNode s created automatically

Use this factory method to construct a new GraphModule from scratch, which needs new input and output GraphNode s created automatically. This helper method will be inherited by new GraphModule subclasses, and will act as factory methods also for your custom GraphModule subclass.

Parameters:

size_in (int) – The number of input GraphNode s to create and attach
size_out (int) – The number of output GraphNode s to create and attach
name (str, optional) – An arbitrary name to attach to this GraphModule, defaults to None
computational_module (Optional[Module], optional) – A rockpool computational module that forms the “generator” of this graph module, defaults to None

Returns:

The newly constructed GraphModule or GraphModule subclass

Return type:

GraphModuleBase

add_input(node: GraphNode) → None

Add a GraphNode as an input source to this module, and connect it

The new node will be appended after the last current input node. The node will be connected with this GraphModule as a sink.

Parameters:: node (GraphNode) – The node to add as an input source

add_output(node: GraphNode) → None

Add a GraphNode as an output of this module, and connect it

The new node will be appended after the last current output node. The node will be connected with this GraphModule as a source.

Parameters:: node (GraphNode) – The node to add as an output

clear_inputs() → None: Remove all GraphNode s as inputs of this module

clear_outputs() → None: Remove all GraphNode s as outputs of this module

computational_module: Module

The computational module that acts as the source for this graph module

Type:: Module

static current_attrs() → List[str][source]

current_attrs lists all current paramters stored inside DynapseNeurons computational graph

Returns:: a list of parametric curents
Return type:: List[str]

dt: float | None = None: the time step for the forward-Euler ODE solver

get_full() → Dict[str, ndarray][source]

get_full creates a dictionary of parameteric current attributes with extended current values

Returns:: the object dictionary with extended current arrays
Return type:: Dict[str, np.ndarray]

input_nodes: SetList['GraphNode']

The input nodes attached to this module

Type:: SetList[GraphNode]

classmethod merge(graph_list: List[DynapseNeurons]) → DynapseNeurons[source]

merge combines a list of computational DynapseNeuron objects into one. The length of attributes are equal to the number of output nodes. Even though the submodules has single valued attributes, they are repeated as many times as the number of their output neurons.

NOTE : Returned single object is neither connected to the input nor the outputs of the any of the modules given.

Parameters:: graph_list (List[DynapseNeurons]) – an ordered list of DynapseNeuron objects
Returns:: a single DynapseNeuron object with combined arrays of attributes
Return type:: DynapseNeurons

name: str

An arbitrary name attached to this specific GraphModule

Type:: str

output_nodes: SetList['GraphNode']

The output nodes attached to this module

Type:: SetList[GraphNode]

remove_input(node: GraphNode) → None

Remove a GraphNode as an input of this module, and disconnect it

The node will be disconnected from this GraphModule as a sink, and will be removed from the module.

Parameters:: node (GraphNode) – The node to remove. If this node exists as an input to the module, it will be removed.

remove_output(node: GraphNode) → None

Remove a GraphNode as an output of this module, and disconnect it

The node will be disconnected from this GraphModule as a source, and will be removed from the module.

Parameters:: node (GraphNode) – The node to remove. If this node exists as an output to the module, it will be removed.