devices.dynapse.DynapSimCore
- class devices.dynapse.DynapSimCore(Iw_0: float | ndarray | Tensor | array = 1e-09, Iw_1: float | ndarray | Tensor | array = 2e-09, Iw_2: float | ndarray | Tensor | array = 4e-09, Iw_3: float | ndarray | Tensor | array = 8e-09, C_ahp: float | ndarray | Tensor | array = 4e-11, C_ampa: float | ndarray | Tensor | array = 2.45e-11, C_gaba: float | ndarray | Tensor | array = 2.5e-11, C_nmda: float | ndarray | Tensor | array = 2.5e-11, C_pulse_ahp: float | ndarray | Tensor | array = 5e-13, C_pulse: float | ndarray | Tensor | array = 5e-13, C_ref: float | ndarray | Tensor | array = 1.5e-12, C_shunt: float | ndarray | Tensor | array = 2.45e-11, C_mem: float | ndarray | Tensor | array = 3e-12, Io: float | ndarray | Tensor | array = 5e-13, kappa_n: float | ndarray | Tensor | array = 0.75, kappa_p: float | ndarray | Tensor | array = 0.66, Ut: float | ndarray | Tensor | array = 0.025, Vth: float | ndarray | Tensor | array = 0.7, Idc: float | ndarray | Tensor | array = 5e-13, If_nmda: float | ndarray | Tensor | array = 5e-13, Igain_ahp: float | ndarray | Tensor | array = 2.8368794326241126e-11, Igain_ampa: float | ndarray | Tensor | array = 8.687943262411346e-09, Igain_gaba: float | ndarray | Tensor | array = 8.865248226950353e-09, Igain_nmda: float | ndarray | Tensor | array = 8.865248226950353e-09, Igain_shunt: float | ndarray | Tensor | array = 8.687943262411346e-09, Igain_mem: float | ndarray | Tensor | array = 2.1276595744680848e-11, Ipulse_ahp: float | ndarray | Tensor | array = 3.5e-07, Ipulse: float | ndarray | Tensor | array = 3.4999999999999996e-08, Iref: float | ndarray | Tensor | array = 1.0499999999999999e-09, Ispkthr: float | ndarray | Tensor | array = 1e-07, Itau_ahp: float | ndarray | Tensor | array = 2.8368794326241126e-11, Itau_ampa: float | ndarray | Tensor | array = 8.687943262411346e-11, Itau_gaba: float | ndarray | Tensor | array = 8.865248226950353e-11, Itau_nmda: float | ndarray | Tensor | array = 8.865248226950353e-11, Itau_shunt: float | ndarray | Tensor | array = 8.687943262411346e-11, Itau_mem: float | ndarray | Tensor | array = 5.319148936170212e-12, Iw_ahp: float | ndarray | Tensor | array = 5e-13)[source]
Bases:
DynapSimCurrents
,DynapSimLayout
,DynapSimWeightBits
DynapSimCore stores the simulation currents and manages the conversion from configuration objects. It also provides easy update mechanisms using coarse&fine values, high-level parameter representations and etc.
simcore = DynapSimCore.from_Dynapse2Core(config.chips[0].cores[0]) Itau_ampa = simcore.Itau_ampa
Attributes overview
AHP synapse capacitance in Farads
AMPA synapse capacitance in Farads
GABA synapse capacitance in Farads
neuron membrane capacitance in Farads
NMDA synapse capacitance in Farads
pulse-width creation sub-circuit capacitance in Farads
spike frequency adaptation circuit pulse-width creation sub-circuit capacitance in Farads
refractory period sub-circuit capacitance in Farads
SHUNT synapse capacitance in Farads
Constant DC current injected to membrane in Amperes
NMDA gate soft cut-off current setting the NMDA gating voltage in Amperes
gain bias current of the spike frequency adaptation block in Amperes
gain bias current of excitatory AMPA synapse in Amperes
gain bias current of inhibitory GABA synapse in Amperes
gain bias current for neuron membrane in Amperes
gain bias current of excitatory NMDA synapse in Amperes
gain bias current of the inhibitory SHUNT synapse in Amperes
Dark current in Amperes that flows through the transistors even at the idle state
bias current setting the pulse width for neuron membrane
t_pulse
in Amperesbias current setting the pulse width for spike frequency adaptation block
t_pulse_ahp
in Amperesbias current setting the refractory period
t_ref
in Amperesspiking threshold current, neuron spikes if \(I_{mem} > I_{spkthr}\) in Amperes
Spike frequency adaptation leakage current setting the time constant
tau_ahp
in AmperesAMPA synapse leakage current setting the time constant
tau_ampa
in AmperesGABA synapse leakage current setting the time constant
tau_gaba
in AmperesNeuron membrane leakage current setting the time constant
tau_mem
in AmperesNMDA synapse leakage current setting the time constant
tau_nmda
in AmperesSHUNT synapse leakage current setting the time constant
tau_shunt
in AmperesWeight bits stacked together
weight bit 0 current of the neurons of the core in Amperes
weight bit 1 current of the neurons of the core in Amperes
weight bit 2 current of the neurons of the core in Amperes
weight bit 3 current of the neurons of the core in Amperes
spike frequency adaptation weight current of the neurons of the core in Amperes
Thermal voltage in Volts
The cut-off Vgs potential of the transistors in Volts (not type specific)
currents returns a subset of object which belongs to DynapSimCurrents
Igain_ahp, Igain_ampa, Igain_gaba, Igain_nmda, Igain_shunt, Igain_mem
Subthreshold slope factor (n-type transistor)
Subthreshold slope factor (p-type transistor)
layout returns a subset of object which belongs to DynapSimLayout
time creates the high level time constants set by currents Ipulse_ahp, Ipulse, Iref, Itau_ahp, Itau_ampa, Itau_gaba, Itau_nmda, Itau_shunt, Itau_mem
weight_bits returns a subset of object which belongs to DynapSimWeightBits
Methods overview
__init__
([Iw_0, Iw_1, Iw_2, Iw_3, C_ahp, ...])compare
(core1, core2)compare compares two DynapSimCore objects detects the different values set
export_Dynapse2Parameters converts all current values to their coarse-fine value representations for device configuration
from_Dynapse2Core
(core)from_Dynapse2Core is a class factory method which uses samna configuration objects to extract the simulation currents
from_specification
([Idc, If_nmda, ...])from_specification is a class factory method helping DynapSimCore object construction using higher level representaitons of the currents like gain ratio or time constant whenever applicable.
get_full
(size)get_full creates a dictionary with respect to the object, with arrays of current values
update
(attr, value)update_current updates an attribute and returns a new object, does not change the original object.
update_gain_ratio
(attr, value)update_gain_ratio updates currents setting gain ratio (Igain/Itau) attributes
update_time_constant
(attr, value)update_time_constant updates currents setting time constant attributes
- C_ahp: float | ndarray | Tensor | array = 4e-11
AHP synapse capacitance in Farads
- C_ampa: float | ndarray | Tensor | array = 2.45e-11
AMPA synapse capacitance in Farads
- C_gaba: float | ndarray | Tensor | array = 2.5e-11
GABA synapse capacitance in Farads
- C_mem: float | ndarray | Tensor | array = 3e-12
neuron membrane capacitance in Farads
- C_nmda: float | ndarray | Tensor | array = 2.5e-11
NMDA synapse capacitance in Farads
- C_pulse: float | ndarray | Tensor | array = 5e-13
pulse-width creation sub-circuit capacitance in Farads
- C_pulse_ahp: float | ndarray | Tensor | array = 5e-13
spike frequency adaptation circuit pulse-width creation sub-circuit capacitance in Farads
- C_ref: float | ndarray | Tensor | array = 1.5e-12
refractory period sub-circuit capacitance in Farads
- C_shunt: float | ndarray | Tensor | array = 2.45e-11
SHUNT synapse capacitance in Farads
- Idc: float | ndarray | Tensor | array = 5e-13
Constant DC current injected to membrane in Amperes
- If_nmda: float | ndarray | Tensor | array = 5e-13
NMDA gate soft cut-off current setting the NMDA gating voltage in Amperes
- Igain_ahp: float | ndarray | Tensor | array = 2.8368794326241126e-11
gain bias current of the spike frequency adaptation block in Amperes
- Igain_ampa: float | ndarray | Tensor | array = 8.687943262411346e-09
gain bias current of excitatory AMPA synapse in Amperes
- Igain_gaba: float | ndarray | Tensor | array = 8.865248226950353e-09
gain bias current of inhibitory GABA synapse in Amperes
- Igain_mem: float | ndarray | Tensor | array = 2.1276595744680848e-11
gain bias current for neuron membrane in Amperes
- Igain_nmda: float | ndarray | Tensor | array = 8.865248226950353e-09
gain bias current of excitatory NMDA synapse in Amperes
- Igain_shunt: float | ndarray | Tensor | array = 8.687943262411346e-09
gain bias current of the inhibitory SHUNT synapse in Amperes
- Io: float | ndarray | Tensor | array = 5e-13
Dark current in Amperes that flows through the transistors even at the idle state
- Ipulse: float | ndarray | Tensor | array = 3.4999999999999996e-08
bias current setting the pulse width for neuron membrane
t_pulse
in Amperes
- Ipulse_ahp: float | ndarray | Tensor | array = 3.5e-07
bias current setting the pulse width for spike frequency adaptation block
t_pulse_ahp
in Amperes
- Iref: float | ndarray | Tensor | array = 1.0499999999999999e-09
bias current setting the refractory period
t_ref
in Amperes
- Ispkthr: float | ndarray | Tensor | array = 1e-07
spiking threshold current, neuron spikes if \(I_{mem} > I_{spkthr}\) in Amperes
- Itau_ahp: float | ndarray | Tensor | array = 2.8368794326241126e-11
Spike frequency adaptation leakage current setting the time constant
tau_ahp
in Amperes
- Itau_ampa: float | ndarray | Tensor | array = 8.687943262411346e-11
AMPA synapse leakage current setting the time constant
tau_ampa
in Amperes
- Itau_gaba: float | ndarray | Tensor | array = 8.865248226950353e-11
GABA synapse leakage current setting the time constant
tau_gaba
in Amperes
- Itau_mem: float | ndarray | Tensor | array = 5.319148936170212e-12
Neuron membrane leakage current setting the time constant
tau_mem
in Amperes
- Itau_nmda: float | ndarray | Tensor | array = 8.865248226950353e-11
NMDA synapse leakage current setting the time constant
tau_nmda
in Amperes
- Itau_shunt: float | ndarray | Tensor | array = 8.687943262411346e-11
SHUNT synapse leakage current setting the time constant
tau_shunt
in Amperes
- property Iw: ndarray
Weight bits stacked together
- Iw_0: float | ndarray | Tensor | array = 1e-09
weight bit 0 current of the neurons of the core in Amperes
- Iw_1: float | ndarray | Tensor | array = 2e-09
weight bit 1 current of the neurons of the core in Amperes
- Iw_2: float | ndarray | Tensor | array = 4e-09
weight bit 2 current of the neurons of the core in Amperes
- Iw_3: float | ndarray | Tensor | array = 8e-09
weight bit 3 current of the neurons of the core in Amperes
- Iw_ahp: float | ndarray | Tensor | array = 5e-13
spike frequency adaptation weight current of the neurons of the core in Amperes
- Ut: float | ndarray | Tensor | array = 0.025
Thermal voltage in Volts
- Vth: float | ndarray | Tensor | array = 0.7
The cut-off Vgs potential of the transistors in Volts (not type specific)
- __init__(Iw_0: float | ndarray | Tensor | array = 1e-09, Iw_1: float | ndarray | Tensor | array = 2e-09, Iw_2: float | ndarray | Tensor | array = 4e-09, Iw_3: float | ndarray | Tensor | array = 8e-09, C_ahp: float | ndarray | Tensor | array = 4e-11, C_ampa: float | ndarray | Tensor | array = 2.45e-11, C_gaba: float | ndarray | Tensor | array = 2.5e-11, C_nmda: float | ndarray | Tensor | array = 2.5e-11, C_pulse_ahp: float | ndarray | Tensor | array = 5e-13, C_pulse: float | ndarray | Tensor | array = 5e-13, C_ref: float | ndarray | Tensor | array = 1.5e-12, C_shunt: float | ndarray | Tensor | array = 2.45e-11, C_mem: float | ndarray | Tensor | array = 3e-12, Io: float | ndarray | Tensor | array = 5e-13, kappa_n: float | ndarray | Tensor | array = 0.75, kappa_p: float | ndarray | Tensor | array = 0.66, Ut: float | ndarray | Tensor | array = 0.025, Vth: float | ndarray | Tensor | array = 0.7, Idc: float | ndarray | Tensor | array = 5e-13, If_nmda: float | ndarray | Tensor | array = 5e-13, Igain_ahp: float | ndarray | Tensor | array = 2.8368794326241126e-11, Igain_ampa: float | ndarray | Tensor | array = 8.687943262411346e-09, Igain_gaba: float | ndarray | Tensor | array = 8.865248226950353e-09, Igain_nmda: float | ndarray | Tensor | array = 8.865248226950353e-09, Igain_shunt: float | ndarray | Tensor | array = 8.687943262411346e-09, Igain_mem: float | ndarray | Tensor | array = 2.1276595744680848e-11, Ipulse_ahp: float | ndarray | Tensor | array = 3.5e-07, Ipulse: float | ndarray | Tensor | array = 3.4999999999999996e-08, Iref: float | ndarray | Tensor | array = 1.0499999999999999e-09, Ispkthr: float | ndarray | Tensor | array = 1e-07, Itau_ahp: float | ndarray | Tensor | array = 2.8368794326241126e-11, Itau_ampa: float | ndarray | Tensor | array = 8.687943262411346e-11, Itau_gaba: float | ndarray | Tensor | array = 8.865248226950353e-11, Itau_nmda: float | ndarray | Tensor | array = 8.865248226950353e-11, Itau_shunt: float | ndarray | Tensor | array = 8.687943262411346e-11, Itau_mem: float | ndarray | Tensor | array = 5.319148936170212e-12, Iw_ahp: float | ndarray | Tensor | array = 5e-13) None
- __update_high_level(obj: DynapSimCoreHigh, attr_getter: Callable[[str], Any], attr: str, value: Any) DynapSimCore
__update_high_level updates high level representations of the current values like time constants and gain ratios. The current values are updated accordingly without changing the original object.
- Parameters:
obj (DynapSimCoreHigh) – the high level object that stores the projections of the current values
attr_getter (Callable[[str], Any]) – a function to get the high level attribute from the high level object
attr (str) – any attribute that belongs to any DynapSimCoreHigh object
value (Any) – the new value to set
- Returns:
updated DynapSimCore object
- Return type:
- static compare(core1: DynapSimCore, core2: DynapSimCore) Dict[str, Tuple[Any]] [source]
compare compares two DynapSimCore objects detects the different values set
- Parameters:
core1 (DynapSimCore) – the first core object
core2 (DynapSimCore) – the second core object to compare against the first one
- Returns:
a dictionary of changed values
- Return type:
Dict[str, Tuple[Any]]
- property currents: DynapSimCurrents
currents returns a subset of object which belongs to DynapSimCurrents
- export_Dynapse2Parameters() Dict[str, Tuple[uint8, uint8]] [source]
export_Dynapse2Parameters converts all current values to their coarse-fine value representations for device configuration
- Returns:
a dictionary of mapping between parameter names and respective coarse-fine values
- Return type:
Dict[str, Tuple[np.uint8, np.uint8]]
- classmethod from_Dynapse2Core(core: Dynapse2Core) DynapSimCore [source]
from_Dynapse2Core is a class factory method which uses samna configuration objects to extract the simulation currents
- Returns:
a dynapse core simulation object whose parameters are imported from a samna configuration object
- Return type:
- classmethod from_specification(Idc: float | ndarray | Tensor | array = 5e-13, If_nmda: float | ndarray | Tensor | array = 5e-13, r_gain_ahp: float | ndarray | Tensor | array = 1, r_gain_ampa: float | ndarray | Tensor | array = 100, r_gain_gaba: float | ndarray | Tensor | array = 100, r_gain_nmda: float | ndarray | Tensor | array = 100, r_gain_shunt: float | ndarray | Tensor | array = 100, r_gain_mem: float | ndarray | Tensor | array = 4, t_pulse_ahp: float | ndarray | Tensor | array = 1e-06, t_pulse: float | ndarray | Tensor | array = 1e-05, t_ref: float | ndarray | Tensor | array = 0.001, Ispkthr: float | ndarray | Tensor | array = 1e-07, tau_ahp: float | ndarray | Tensor | array = 0.05, tau_ampa: float | ndarray | Tensor | array = 0.01, tau_gaba: float | ndarray | Tensor | array = 0.01, tau_nmda: float | ndarray | Tensor | array = 0.01, tau_shunt: float | ndarray | Tensor | array = 0.01, tau_mem: float | ndarray | Tensor | array = 0.02, Iw_0: float | ndarray | Tensor | array = 1e-09, Iw_1: float | ndarray | Tensor | array = 2e-09, Iw_2: float | ndarray | Tensor | array = 4e-09, Iw_3: float | ndarray | Tensor | array = 8e-09, Iw_ahp: float | ndarray | Tensor | array = 5e-13, C_ahp: float | ndarray | Tensor | array = 4e-11, C_ampa: float | ndarray | Tensor | array = 2.45e-11, C_gaba: float | ndarray | Tensor | array = 2.5e-11, C_nmda: float | ndarray | Tensor | array = 2.5e-11, C_pulse_ahp: float | ndarray | Tensor | array = 5e-13, C_pulse: float | ndarray | Tensor | array = 5e-13, C_ref: float | ndarray | Tensor | array = 1.5e-12, C_shunt: float | ndarray | Tensor | array = 2.45e-11, C_mem: float | ndarray | Tensor | array = 3e-12, Io: float | ndarray | Tensor | array = 5e-13, kappa_n: float | ndarray | Tensor | array = 0.75, kappa_p: float | ndarray | Tensor | array = 0.66, Ut: float | ndarray | Tensor | array = 0.025, Vth: float | ndarray | Tensor | array = 0.7) DynapSimCore [source]
from_specification is a class factory method helping DynapSimCore object construction using higher level representaitons of the currents like gain ratio or time constant whenever applicable.
- Parameters:
Idc (FloatVector, optional) – Constant DC current injected to membrane in Amperes, defaults to default_currents[“Idc”]
If_nmda (FloatVector, optional) – NMDA gate soft cut-off current setting the NMDA gating voltage in Amperes, defaults to default_currents[“If_nmda”]
r_gain_ahp (FloatVector, optional) – spike frequency adaptation block gain ratio, defaults to default_gain_ratios[“r_gain_ahp”]
r_gain_ampa (FloatVector, optional) – xcitatory AMPA synpse gain ratio, defaults to default_gain_ratios[“r_gain_ampa”]
r_gain_gaba (FloatVector, optional) – inhibitory GABA synpse gain ratio, defaults to default_gain_ratios[“r_gain_gaba”]
r_gain_nmda (FloatVector, optional) – excitatory NMDA synpse gain ratio, defaults to default_gain_ratios[“r_gain_nmda”]
r_gain_shunt (FloatVector, optional) – inhibitory SHUNT synpse gain ratio, defaults to default_gain_ratios[“r_gain_shunt”]
r_gain_mem (FloatVector, optional) – neuron membrane gain ratio, defaults to default_gain_ratios[“r_gain_mem”]
t_pulse_ahp (FloatVector, optional) – the spike pulse width for spike frequency adaptation circuit in seconds, defaults to default_time_constants[“t_pulse_ahp”]
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”]
Ispkthr (FloatVector, optional) – spiking threshold current, neuron spikes if \(I_{mem} > I_{spkthr}\) in Amperes, defaults to default_currents[“Ispkthr”]
tau_ahp (FloatVector, optional) – Spike frequency leakage time constant in seconds, defaults to default_time_constants[“tau_ahp”]
tau_ampa (FloatVector, optional) – AMPA synapse leakage time constant in seconds, defaults to default_time_constants[“tau_ampa”]
tau_gaba (FloatVector, optional) – GABA synapse leakage time constant in seconds, defaults to default_time_constants[“tau_gaba”]
tau_nmda (FloatVector, optional) – NMDA synapse leakage time constant in seconds, defaults to default_time_constants[“tau_nmda”]
tau_shunt (FloatVector, optional) – SHUNT synapse leakage time constant in seconds, defaults to default_time_constants[“tau_shunt”]
tau_mem (FloatVector, optional) – Neuron membrane leakage time constant in seconds, defaults to default_time_constants[“tau_mem”]
Iw_0 (FloatVector, optional) – weight bit 0 current of the neurons of the core in Amperes, defaults to default_weights[“Iw_0”]
Iw_1 (FloatVector, optional) – weight bit 1 current of the neurons of the core in Amperes, defaults to default_weights[“Iw_1”]
Iw_2 (FloatVector, optional) – weight bit 2 current of the neurons of the core in Amperes, defaults to default_weights[“Iw_2”]
Iw_3 (FloatVector, optional) – weight bit 3 current of the neurons of the core in Amperes, defaults to default_weights[“Iw_3”]
Iw_ahp (FloatVector, optional) – spike frequency adaptation weight current of the neurons of the core in Amperes, defaults to default_currents[“Iw_ahp”]
C_ahp (FloatVector, optional) – AHP synapse capacitance in Farads, defaults to default_layout[“C_ahp”]
C_ampa (FloatVector, optional) – AMPA synapse capacitance in Farads, defaults to default_layout[“C_ampa”]
C_gaba (FloatVector, optional) – GABA synapse capacitance in Farads, defaults to default_layout[“C_gaba”]
C_nmda (FloatVector, optional) – NMDA synapse capacitance in Farads, defaults to default_layout[“C_nmda”]
C_pulse_ahp (FloatVector, optional) – spike frequency adaptation circuit pulse-width creation sub-circuit capacitance in Farads, defaults to default_layout[“C_pulse_ahp”]
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_shunt (FloatVector, optional) – SHUNT synapse capacitance in Farads, defaults to default_layout[“C_shunt”]
C_mem (FloatVector, optional) – neuron membrane capacitance in Farads, defaults to default_layout[“C_mem”]
Io (FloatVector, optional) – Dark current in Amperes that flows through the transistors even at the idle state, defaults to default_layout[“Io”]
kappa_n (FloatVector, optional) – Subthreshold slope factor (n-type transistor), defaults to default_layout[“kappa_n”]
kappa_p (FloatVector, optional) – Subthreshold slope factor (p-type transistor), defaults to default_layout[“kappa_p”]
Ut (FloatVector, optional) – Thermal voltage in Volts, defaults to default_layout[“Ut”]
Vth (FloatVector, optional) – The cut-off Vgs potential of the transistors in Volts (not type specific), defaults to default_layout[“Vth”]
- Returns:
DynapSimCore object instance
- Return type:
- property gain: DynapSimGain
Igain_ahp, Igain_ampa, Igain_gaba, Igain_nmda, Igain_shunt, Igain_mem
- Type:
gain creates the high level gain ratios set by currents
- get_full(size: int) Dict[str, ndarray]
get_full creates a dictionary with respect to the object, with arrays of current values
- Parameters:
size (int) – the lengths of the current arrays
- Returns:
the object dictionary with current arrays given the size
- Return type:
Dict[str, np.ndarray]
- kappa_n: float | ndarray | Tensor | array = 0.75
Subthreshold slope factor (n-type transistor)
- kappa_p: float | ndarray | Tensor | array = 0.66
Subthreshold slope factor (p-type transistor)
- property layout: DynapSimLayout
layout returns a subset of object which belongs to DynapSimLayout
- property time: DynapSimTime
time creates the high level time constants set by currents Ipulse_ahp, Ipulse, Iref, Itau_ahp, Itau_ampa, Itau_gaba, Itau_nmda, Itau_shunt, Itau_mem
- update(attr: str, value: Any) DynapSimCore [source]
update_current updates an attribute and returns a new object, does not change the original object.
- Parameters:
attr (str) – any attribute that belongs to DynapSimCore object
value (Any) – the new value to set
- Returns:
updated DynapSimCore object
- Return type:
- update_gain_ratio(attr: str, value: Any) DynapSimCore [source]
update_gain_ratio updates currents setting gain ratio (Igain/Itau) attributes
- Parameters:
attr (str) – any attribute that belongs to any DynapSimGain object
value (Any) – the new value to set
- Returns:
updated DynapSimCore object
- Return type:
- update_time_constant(attr: str, value: Any) DynapSimCore [source]
update_time_constant updates currents setting time constant attributes
- Parameters:
attr (str) – any attribute that belongs to any DynapSimTime object
value (Any) – the new value to set
- Returns:
updated DynapSimCore object
- Return type:
- property weight_bits: DynapSimWeightBits
weight_bits returns a subset of object which belongs to DynapSimWeightBits