emodelrunner.protocols.sscx_protocols¶

Ephys protocols for the SSCX packages.

Classes

`RampProtocol`([name, ramp_stimulus, ...])	Protocol consisting of ramp and holding current.
`RampThresholdProtocol`(name[, ...])	Step protocol based on threshold.
`RatSSCxMainProtocol`(name[, rmp_protocol, ...])	Main protocol to fit RatSSCx neuron ephys parameters.
`RatSSCxRinHoldcurrentProtocol`(name[, ...])	IDRest protocol to fit RatSSCx neuron ephys parameters.
`RatSSCxThresholdDetectionProtocol`(name[, ...])	IDRest protocol to fit RatSSCx neuron ephys parameters.
`StepProtocol`([name, step_stimuli, ...])	Protocol consisting of step and holding current.
`StepThresholdProtocol`(name[, thresh_perc, ...])	Step protocol based on threshold.
`SweepProtocolCustom`([name, stimuli, ...])	SweepProtocol with generate_current method.

class emodelrunner.protocols.sscx_protocols.RampProtocol(name=None, ramp_stimulus=None, holding_stimulus=None, recordings=None, cvode_active=None)[source]¶

Bases: SweepProtocol, CurrentOutputKeyMixin

Protocol consisting of ramp and holding current.

name¶

name of this object

Type:: str

stimuli¶

List of all Stimulus objects used in protocol

Type:: list of Stimuli

recordings¶

Recording objects used in the protocol

Type:: list of Recordings

cvode_active¶

whether to use variable time step

Type:: bool

ramp_stimulus¶

ramp Stimulus

Type:: Stimulus

holding_stimulus¶

Holding Stimulus

Type:: Stimulus

generate_current(threshold_current=None, holding_current=None, dt=0.1)[source]¶

Return current time series.

Parameters:

threshold_current (float) – the threshold current (nA)
holding_current (float) – the holding current (nA)
dt (float) – timestep of the generated currents (ms)

Returns:

dict containing the generated current

property step_delay¶

Time stimulus delay.

Returns:: time at which the ramp starts (ms)

property step_duration¶

Time stimulus duration.

Returns:: duration of the ramp stimulus (ms)

class emodelrunner.protocols.sscx_protocols.RampThresholdProtocol(name, thresh_perc_start=None, thresh_perc_end=None, ramp_stimulus=None, holding_stimulus=None, recordings=None, cvode_active=None)[source]¶

Bases: RampProtocol

Step protocol based on threshold.

name¶

name of this object

Type:: str

stimuli¶

List of all Stimulus objects used in protocol

Type:: list of Stimuli

recordings¶

Recording objects used in the protocol

Type:: list of Recordings

cvode_active¶

whether to use variable time step

Type:: bool

ramp_stimulus¶

ramp Stimulus

Type:: Stimulus

holding_stimulus¶

Holding Stimulus

Type:: Stimulus

thresh_perc_start¶

percentage of the threshold current at which to set the start of the ramp amplitude

Type:: float

thresh_perc_end¶

percentage of the threshold current at which to set the end of the ramp amplitude

Type:: float

generate_current(threshold_current, holding_current, dt=0.1)[source]¶

Return current time series.

Parameters:

threshold_current (float) – the threshold current (nA)
holding_current (float) – the holding current (nA)
dt (float) – timestep of the generated currents (ms)

Returns:

dict containing the generated current

run(cell_model, param_values, sim=None, isolate=None, timeout=None)[source]¶

Run protocol.

Parameters:

cell_model (bluepyopt.ephys.models.CellModel) – the cell model
param_values (dict) – optimized parameters
sim (bluepyopt.ephys.NrnSimulator) – neuron simulator
isolate (bool) – whether to isolate the run in a process with a timeout to avoid bad cells running for too long
timeout (float) – maximum real time (s) the cell is allowed to run when isolated

Raises:

AttributeError – if the threshold_current is not set to the cell model

Returns:

dict containing the responses for the ramp protocol

class emodelrunner.protocols.sscx_protocols.RatSSCxMainProtocol(name, rmp_protocol=None, rmp_efeature=None, rinhold_protocol=None, rin_efeature=None, thdetect_protocol=None, other_protocols=None, pre_protocols=None)[source]¶

Bases: Protocol

Main protocol to fit RatSSCx neuron ephys parameters.

Pseudo code:

Find resting membrane potential
Find input resistance
Run other protocols:
- Find holding current
- Find rheobase
- Run IDRest
- Possibly run other protocols (based on constructor arguments)
- Return all the responses

name¶

name of the protocol

Type:: str

rmp_protocol¶

resting membrane potential protocol

Type:: StepProtocol

rmp_efeature¶

voltage base efeature

Type:: bluepyopt.ephys.efeatures.eFELFeature

rinhold_protocol¶

protocol to get the holding current

Type:: RatSSCxRinHoldcurrentProtocol

rin_efeature¶

ohmic input resistance vb ssse

Type:: bluepyopt.ephys.efeatures.eFELFeature

thdetect_protocol¶

protocol to detect threshold current

Type:: RatSSCxThresholdDetectionProtocol

other_protocols¶

other protocols to run

Type:: bluepyopt.ephys.protocols.Protocol

pre_protocols¶

protocols to run before the ‘other protocols’

Type:: bluepyopt.ephys.protocols.Protocol

generate_current(threshold_current=None, holding_current=None, dt=0.1)[source]¶

Generate current for all protocols except rin and threshold detection.

Parameters:

threshold_current (float) – the threshold current (nA)
holding_current (float) – the holding current (nA)
dt (float) – timestep of the generated currents (ms)

Returns:

dict containing the generated currents

run(cell_model, param_values, sim=None, isolate=None)[source]¶

Run protocol.

Parameters:

cell_model (bluepyopt.ephys.models.CellModel) – the cell model
param_values (dict) – optimized parameters
sim (bluepyopt.ephys.NrnSimulator) – neuron simulator
isolate (bool) – whether to isolate the run in a process with a timeout to avoid bad cells running for too long

Returns:

dict containing the responses for all the protocols

run_other_protocols(responses, cell_model, sim)[source]¶

Run other protocols.

Parameters:

responses (dict) – responses to be updated
cell_model (bluepyopt.ephys.models.CellModel) – the cell on which to apply the other protocols
sim (bluepyopt.ephys.NrnSimulator) – neuron simulator

run_pre_protocols(responses, cell_model, sim)[source]¶

Run the pre-protocols.

Parameters:

responses (dict) – responses to be updated
cell_model (bluepyopt.ephys.models.CellModel) – the cell on which to apply the pre protocols
sim (bluepyopt.ephys.NrnSimulator) – neuron simulator

subprotocols()[source]¶

Return all the subprotocols contained in this protocol, is recursive.

Returns:: dict containing all the subprotocols

class emodelrunner.protocols.sscx_protocols.RatSSCxRinHoldcurrentProtocol(name, rin_protocol_template=None, voltagebase_efeature=None, holdi_estimate_multiplier=2, holdi_precision=0.1, holdi_max_depth=5, prefix=None)[source]¶

Bases: Protocol

IDRest protocol to fit RatSSCx neuron ephys parameters.

name¶

name of the protocol

Type:: str

rin_protocol_template¶

template for Rin protocol with amplitude for the holding stimulus to be filled

Type:: StepProtocol

voltagebase_efeature¶

voltage base efeature

Type:: bluepyopt.ephys.efeatures.eFELFeature

holdi_estimate_multiplier¶

when searching for holding current amplitude, multiply holding current estimate by this value to get lower bound

Type:: float

holdi_precision¶

precision with which to reach holding voltage when searching for holding current

Type:: float

holdi_max_depth¶

maximum number of times to compute the holding voltage when searching for holding current if holdi_precision is not reached

Type:: int

prefix¶

prefix used in naming responses, features, recordings, etc.

Type:: str

rin_efeature¶

ohmic input resistance vb ssse

Type:: bluepyopt.ephys.efeatures.eFELFeature

rin_protocol¶

Rin protocol with holding current amplitude

Type:: StepProtocol

create_rin_protocol(holdi=None)[source]¶

Create threshold protocol.

Parameters:: holdi (float) – amplitude of holding current (nA)
Returns:: the Rin protocol with holding current
Return type:: StepProtocol

run(cell_model, param_values, sim, rmp=None)[source]¶

Run protocol.

Parameters:

cell_model (bluepyopt.ephys.models.CellModel) – the cell model
param_values (dict) – optimized parameters
sim (bluepyopt.ephys.NrnSimulator) – neuron simulator
rmp (float) – resting membrane potential (mV)

Returns:

dict containing the responses for the Rin protocol using holding current

search_holdi(cell_model, param_values, sim, holding_voltage, rin_noholding, rmp)[source]¶

Find the holding current to hold cell at holding_voltage.

Parameters:

cell_model (bluepyopt.ephys.models.CellModel) – the cell model
param_values (dict) – optimized parameters
sim (bluepyopt.ephys.NrnSimulator) – neuron simulator
holding_voltage (float) – experimental mean of the voltage base efeature (mV)
rin_noholding (float) – Rin efeature (ohmic input resistance vb ssse) value when no holding current is applied (mV/nA)
rmp (float) – resting membrane potential (mV)

Returns:

the holding current (nA) that reproduces the experimental holding voltage

Return type:

float

subprotocols()[source]¶

Return subprotocols.

Returns:: dict containing the Rin protocol and the Rin protocol template

voltage_base(current, cell_model, param_values, sim=None)[source]¶

Calculate voltage base for certain stimulus current.

Parameters:

current (float) – amplitude of the holding current to inject (nA)
cell_model (bluepyopt.ephys.models.CellModel) – the cell model
param_values (dict) – optimized parameters
sim (bluepyopt.ephys.NrnSimulator) – neuron simulator

Returns:

voltage base (mV) of response

Return type:

float

class emodelrunner.protocols.sscx_protocols.RatSSCxThresholdDetectionProtocol(name, step_protocol_template=None, max_threshold_voltage=-40, holding_voltage=None, prefix=None)[source]¶

Bases: Protocol

IDRest protocol to fit RatSSCx neuron ephys parameters.

name¶

name of the protocol

Type:: str

step_protocol_template¶

template for threshold protocol with amplitude for the holding and steps stimuli to be filled

Type:: StepProtocol

max_threshold_voltage¶

Use this (mV) to get max threshold current upper bound when searching for threshold current

Type:: float

short_perc¶

multiply step duration by this value when detecting spikes with short stimulus. Should be < 1. Not actually a percentage (0.1 -> 10%)

Type:: float

short_steps¶

the number of short steps to perform to determine the upper bound of the threshold search with long steps

Type:: int

holding_voltage¶

experimental mean of the voltage base efeature (mV) of the Rin protocol

Type:: float

prefix¶

prefix used in naming responses, features, recordings, etc.

Type:: str

create_short_threshold_protocol(holdi=None, step_current=None)[source]¶

Create short threshold protocol.

Parameters:

holdi (float) – holding current amplitude (nA)
step_current (float) – step current amplitude (nA)

Returns:

the threshold protocol with shorter step duration and total duration

Return type:

StepProtocol

create_step_protocol(holdi=0.0, step_current=0.0)[source]¶

Create threshold protocol.

Parameters:

holdi (float) – holding current amplitude (nA)
step_current (float) – step current amplitude (nA)

Returns:

the threshold protocol

Return type:

StepProtocol

detect_spike(cell_model, param_values, sim=None, step_current=None, holdi=None, short=False)[source]¶

Detect if spike is present at current level.

Parameters:

cell_model (bluepyopt.ephys.models.CellModel) – the cell model
param_values (dict) – optimized parameters
sim (bluepyopt.ephys.NrnSimulator) – neuron simulator
step_current (float) – step current amplitude (nA)
holdi (float) – holding current amplitude (nA)
short (bool) – whether the protocol step duration should be shorten

Returns:

True if at least one spike was detected, False otherwise

Return type:

bool

max_threshold_current(rin=None)[source]¶

Find the current necessary to get to max_threshold_voltage.

Parameters:: rin (float) – Rin efeature (ohmic input resistance vb ssse) value (mV/nA)
Returns:: maximum threshold current (nA)
Return type:: float

run(cell_model, sim, holdi, rin)[source]¶

Run protocol.

Parameters:

cell_model (bluepyopt.ephys.models.CellModel) – the cell model
sim (bluepyopt.ephys.NrnSimulator) – neuron simulator
holdi (float) – holding current amplitude (nA)
rin (float) – Rin efeature (ohmic input resistance vb ssse) value (mV/nA)

Returns:

dict containing threshold current

search_spike_threshold(cell_model, param_values, sim=None, holdi=None, lower_bound=None, upper_bound=None, precision=0.01, max_depth=5)[source]¶

Find the current step spiking threshold.

Parameters:

cell_model (bluepyopt.ephys.models.CellModel) – the cell model
param_values (dict) – optimized parameters
sim (bluepyopt.ephys.NrnSimulator) – neuron simulator
holdi (float) – holding current amplitude (nA)
lower_bound (float) – lower bound in which to search for threshold current (nA)
upper_bound (float) – upper bound in which to search for threshold current (nA)
precision (float) – precision with which to compute threshold current (nA)
max_depth (int) – maximum number of times to run the cell when searching for threshold current when precision is not reached

Returns:

threshold current amplitude (nA)

Return type:

float

subprotocols()[source]¶

Return subprotocols.

Returns:: the threshold detection protocol and the threshold detection protocol template
Return type:: dict

class emodelrunner.protocols.sscx_protocols.StepProtocol(name=None, step_stimuli=None, holding_stimulus=None, recordings=None, cvode_active=None, stochkv_det=None)[source]¶

Bases: SweepProtocol, CurrentOutputKeyMixin

Protocol consisting of step and holding current.

name¶

name of this object

Type:: str

stimuli¶

List of all Stimulus objects used in protocol

Type:: list of Stimuli

recordings¶

Recording objects used in the protocol

Type:: list of Recordings

cvode_active¶

whether to use variable time step

Type:: bool

step_stimuli¶

List of step Stimulus objects used in protocol

Type:: list of Stimuli

holding_stimulus¶

Holding Stimulus

Type:: Stimulus

stochkv_det¶

set if stochastic or deterministic

Type:: bool

generate_current(threshold_current=None, holding_current=None, dt=0.1)[source]¶

Return current time series.

Parameters:

threshold_current (float) – the threshold current (nA)
holding_current (float) – the holding current (nA)
dt (float) – timestep of the generated currents (ms)

Returns:

dict containing the generated current

instantiate(sim=None, cell_model=None)[source]¶

Instantiate.

Parameters:

sim (bluepyopt.ephys.NrnSimulator) – neuron simulator
cell_model (bluepyopt.ephys.CellModel) – cell model

run(cell_model, param_values, sim=None, isolate=None, timeout=None)[source]¶

Run protocol.

Parameters:

cell_model (bluepyopt.ephys.models.CellModel) – the cell model
param_values (dict) – optimized parameters
sim (bluepyopt.ephys.NrnSimulator) – neuron simulator
isolate (bool) – whether to isolate the run in a process with a timeout to avoid bad cells running for too long
timeout (float) – maximum real time (s) the cell is allowed to run when isolated

Returns:

dict containing the responses for the step protocol

property step_amplitude¶

Stimuli mean amplitude.

Returns:: the mean amplitude of the step stimuli (nA)

property stim_duration¶

Time stimulus duration.

Returns:: the duration of the stimulus (ms)

property stim_end¶

Time stimulus ends.

Returns:: the time at which the stimulus ends (ms)

property stim_last_start¶

Time stimulus last start.

Returns:: the time at which the last step stimulus in the list starts (ms)

property stim_start¶

Time stimulus starts.

Returns:: the time at which the stimulus starts (ms)

class emodelrunner.protocols.sscx_protocols.StepThresholdProtocol(name, thresh_perc=None, step_stimuli=None, holding_stimulus=None, recordings=None, cvode_active=None, stochkv_det=None)[source]¶

Bases: StepProtocol

Step protocol based on threshold.

name¶

name of this object

Type:: str

stimuli¶

List of all Stimulus objects used in protocol

Type:: list of Stimuli

recordings¶

Recording objects used in the protocol

Type:: list of Recordings

cvode_active¶

whether to use variable time step

Type:: bool

step_stimuli¶

List of step Stimulus objects used in protocol

Type:: list of Stimuli

holding_stimulus¶

Holding Stimulus

Type:: Stimulus

stochkv_det¶

set if stochastic or deterministic

Type:: bool

thresh_perc¶

percentage of the threshold current at which to set the step amplitudes

Type:: float

generate_current(threshold_current, holding_current, dt=0.1)[source]¶

Return current time series.

Parameters:

threshold_current (float) – the threshold current (nA)
holding_current (float) – the holding current (nA)
dt (float) – timestep of the generated currents (ms)

Returns:

dict containing the generated current

run(cell_model, param_values, sim=None, isolate=None, timeout=None)[source]¶

Run protocol.

Parameters:

cell_model (bluepyopt.ephys.models.CellModel) – the cell model
param_values (dict) – optimized parameters
sim (bluepyopt.ephys.NrnSimulator) – neuron simulator
isolate (bool) – whether to isolate the run in a process with a timeout to avoid bad cells running for too long
timeout (float) – maximum real time (s) the cell is allowed to run when isolated

Raises:

AttributeError – if the threshold_current is not set to the cell model

Returns:

dict containing the responses for the step protocol

class emodelrunner.protocols.sscx_protocols.SweepProtocolCustom(name=None, stimuli=None, recordings=None, cvode_active=None, deterministic=False)[source]¶

Bases: SweepProtocol

SweepProtocol with generate_current method.

name¶

name of this object

Type:: str

stimuli¶

List of all Stimulus objects used in protocol

Type:: list of Stimuli

recordings¶

Recording objects used in the protocol

Type:: list of Recordings

cvode_active¶

whether to use variable time step

Type:: bool

Args of the parent constructor:

name (str): name of this object
stimuli (list of Stimuli): Stimulus objects used in the protocol
recordings (list of Recordings): Recording objects used in the protocol
cvode_active (bool): whether to use variable time step

static generate_current(threshold_current, holding_current, dt=0.1)[source]¶

Return an empty dictionary.

Parameters:

threshold_current (float) – the threshold current (nA)
holding_current (float) – the holding current (nA)
dt (float) – timestep of the generated currents (ms)

Returns:

empty dict