cleo.imaging module#

Contains Scope and sensors for two-photon microscopy

class cleo.imaging.CalBindingActivationModel(model: str)[source]#

Bases: object

Base class for modeling calcium binding/activation

Method generated by attrs for class CalBindingActivationModel.

model: str#

class cleo.imaging.CalciumModel(model: str)[source]#

Bases: object

Base class for how GECI computes calcium concentration

Must provide variable Ca (molar) in model.

Method generated by attrs for class CalciumModel.

model: str#

on_pre: str#

class cleo.imaging.DoubExpCalBindingActivation(*, A: float, tau_on: Quantity, tau_off: Quantity, Ca_rest: Quantity)[source]#

Bases: CalBindingActivationModel

Double exponential kernel convolution representing CaB binding/activation.

Convolution is implemented via ODEs; see notebooks/double_exp_conv_as_ode.ipynb for derivation.

A, tau_on, and tau_off are the versions with proper scale and units of NAOMi’s ca_amp, t_on, and t_off.

Some parameters found here. Fitting code here.

Method generated by attrs for class DoubExpCalBindingActivation.

A: float#: amplitude of double exponential kernel

Ca_rest: Quantity#: Resting Ca2+ concentration (molar).

init_syn_vars(syn: Synapses) → None[source]#

model: str#

tau_off: Quantity#: CaB unbinding/deactivation time constant (sec)

tau_on: Quantity#: CaB binding/activation time constant (sec)

class cleo.imaging.DynamicCalcium(*, Ca_rest: Quantity, gamma: Quantity, B_T: Quantity, kappa_S: float, dCa_T: Quantity)[source]#

Bases: CalciumModel

Simulates intracellular calcium dynamics from spikes. Pieced together from Lütke et al., 2013; Helmchen and Tank, 2015; and Song et al., 2021. (code)

Method generated by attrs for class DynamicCalcium.

B_T: Quantity#: total indicator (buffer) concentration (molar)

Ca_rest: Quantity#: resting Ca2+ concentration (molar)

dCa_T: Quantity#: total Ca2+ concentration increase per spike (molar)

gamma: Quantity#: clearance/extrusion rate (1/sec)

init_syn_vars(syn: Synapses) → None[source]#

kappa_S: float#: Ca2+ binding ratio of the endogenous buffer

model: str#: from eq 8 in Lütke et al., 2013

on_pre: str#: from eq 9 in Lütke et al., 2013

class cleo.imaging.ExcitationModel(model: str)[source]#

Bases: object

Defines exc_factor

Method generated by attrs for class ExcitationModel.

model: str#

class cleo.imaging.GECI(extra_namespace: dict = _Nothing.NOTHING, *, name: str = _Nothing.NOTHING, save_history: bool = True, sigma_noise: float, dFF_1AP: float, cal_model: CalciumModel, bind_act_model: CalBindingActivationModel, exc_model: ExcitationModel, K_d: Quantity, n_H: float, dFF_max: float)[source]#

Bases: Sensor

GECI model based on Song et al., 2021, with interchangeable components.

See geci() for a convenience function for creating GECI models.

As a potentially simpler alternative for future work, see the phenomological S2F model from Zhang et al., 2023. While parameter count looks similar, at least they have parameters fit already, and directly to data, rather than to biophysical processes before the data.

Method generated by attrs for class GECI.

K_d: Quantity#: indicator dissociation constant (binding affinity) (molar)

bind_act_model: CalBindingActivationModel#

cal_model: CalciumModel#

dFF_max: float#: amplitude of Hill equation for conversion from Ca2+ to ΔF/F, Fmax/F0. May be approximated from ‘dynamic range’ in literature Fmax/Fmin

exc_model: ExcitationModel#

fluor_model: str#: Uses a Hill equation to convert from Ca2+ to ΔF/F, as in Song et al., 2021

get_state() → dict[NeuronGroup, ndarray][source]#: Returns a {neuron_group: fluorescence} dict of dFF values.

init_syn_vars(syn: Synapses) → None[source]#

Initializes appropriate variables in Synapses implementing the model

Also called on reset().

Parameters:: syn (Synapses) – The synapses object implementing this model

location: str#

cytoplasm or membrane

Type:: where sensor is expressed

n_H: float#: Hill coefficient for conversion from Ca2+ to ΔF/F

property params: dict#: Returns a dictionary of all parameters from model/submodels

class cleo.imaging.LightExcitation[source]#

Bases: ExcitationModel

Models light-dependent excitation (not implemented yet)

Method generated by attrs for class LightExcitation.

model: str#

class cleo.imaging.NullBindingActivation[source]#

Bases: CalBindingActivationModel

Doesn’t model binding/activation; i.e., goes straight from [Ca2+] to ΔF/F

Method generated by attrs for class NullBindingActivation.

model: str#

class cleo.imaging.NullExcitation[source]#

Bases: ExcitationModel

Models excitation as a constant factor

Method generated by attrs for class NullExcitation.

model: str#

class cleo.imaging.PreexistingCalcium[source]#

Bases: CalciumModel

Calcium concentration is pre-existing in neuron model

Method generated by attrs for class PreexistingCalcium.

model: str#

class cleo.imaging.Scope(sensor: Sensor, img_width: Quantity, focus_depth: Quantity = None, location: Quantity = array([0., 0., 0.]) * metre, direction: Quantity = (0, 0, 1), soma_radius: Quantity = 10. * umetre, snr_cutoff: float = 1, rand_seed: int = None, *, name: str = _Nothing.NOTHING, save_history: bool = True)[source]#

Bases: Recorder

Two-photon microscope.

Injection kwargs:

rho_rel_generator (Callable[[int], NDArray[(Any,), float]], optional) – A function assigning expression levels. Takes n as an arg, outputs float array. lambda n: np.ones(n) by default.
focus_depth (Quantity, optional) – The depth of the focal plane, by default that of the scope.
soma_radius (Quantity, optional) – The radius of the soma of the neuron, by default that of the scope. Used to compute noise focus factor, since smaller ROIs will have a noisier distribution of fluorescence, averaged over fewer pixels.

Method generated by attrs for class Scope.

add_self_to_plot(ax: Axes3D, axis_scale_unit: Unit, **kwargs) → list[Artist][source]#

Add device to an existing plot

Should only be called by plot().

Parameters:

ax (Axes3D) – The existing matplotlib Axes object
axis_scale_unit (Unit) – The unit used to label axes and define chart limits
**kwargs (optional) –

Returns:

A list of artists used to render the device. Needed for use in conjunction with VideoVisualizer.

Return type:

list[Artist]

connect_to_neuron_group(neuron_group: NeuronGroup, **kwparams) → None[source]#

Connect device to given neuron_group.

If your device introduces any objects which Brian must keep track of, such as a NeuronGroup, Synapses, or Monitor, make sure to add these to brian_objects.

Parameters:

neuron_group (NeuronGroup) –
**kwparams (optional) – Passed from inject

dFF: list[NDArray[Any, float]]#: ΔF/F from every call to get_state(). Shape is (n_samples, n_ROIs). Stored if save_history

property dFF_1AP: NDArray[Any, Float]#: dFF_1AP for all targets, in order injected. Varies with expression levels.

direction: NDArray[3, float]#: Direction in which the microscope is pointing. By default straight down (+z direction)

focus_coords_per_injct: list[NDArray[Any, float]]#: coordinates on the focal plane of neurons selected from each injection

focus_depth: Quantity#: The depth of the focal plane, with distance units

get_state() → NDArray[Any, Float][source]#

Returns a 1D array of ΔF/F values for all targets, in order injected.

Returns:: Fluorescence values for all targets
Return type:: NDArray[(Any,), float]

i_targets_for_neuron_group(neuron_group)[source]#: can handle multiple injections into same ng

i_targets_per_injct: list[NDArray[Any, int]]#: targets of neurons selected from each injection

img_width: Quantity#: The width (diameter) of the (circular) image captured by the microscope. Specified in distance units.

inject_sensor_for_targets(**kwparams) → None[source]#

location: Quantity#: Location of the objective lens.

property n: int#: Number of imaged ROIs

neuron_groups: list[NeuronGroup]#: neuron groups the scope has been injected into, in order of injection

rand_seed: int#

reset(**kwargs) → None[source]#: Reset the device to a neutral state

rho_rel_per_injct: list[NDArray[Any, float]]#: relative expression levels of neurons selected from each injection

sensor: Sensor#

property sigma_noise: NDArray[Any, Float]#: noise std dev (in terms of ΔF/F) for all targets, in order injected.

sigma_per_injct: list[NDArray[Any, float]]#: sigma_noise of neurons selected from each injection

snr_cutoff: float#: SNR below which neurons are discarded. Applied only when not focus_depth is not None

soma_radius: Quantity#: Assumed radius of neurons, used to compute noise focus factor. Smaller neurons have noisier signals.

t_ms: list[float]#: Times at which sensor traces are recorded, in ms, stored if save_history

target_neurons_in_plane(ng, focus_depth: Quantity = None, soma_radius: Quantity = None) → tuple[NDArray[Any, Int], NDArray[Any, Float], NDArray[Any, 3, Float]][source]#

calls target_neurons_in_plane() with scope parameter defaults. focus_depth and soma_radius can be overridden here.

Parameters:

ng (NeuronGroup) – The neuron group to target.
focus_depth (Quantity) – The depth of the focal plane, by default that of the microscope.
soma_radius (Quantity, optional) – The radius of the soma of the neuron, by default that of the microscope. Used to compute noise focus factor, since smaller ROIs will have a noisier distribution of fluorescence, averaged over fewer pixels.

Returns:

A tuple of (i_targets, noise_focus_factor, coords_on_plane)

Return type:

Tuple[NDArray[(Any,), int], NDArray[(Any,), float], NDArray[(Any, 3), float]]

class cleo.imaging.Sensor(extra_namespace: dict = _Nothing.NOTHING, *, name: str = _Nothing.NOTHING, save_history: bool = True, sigma_noise: float, dFF_1AP: float, location: str)[source]#

Bases: SynapseDevice

Base class for sensors

Method generated by attrs for class Sensor.

dFF_1AP: float#: ΔF/F for 1 AP, only used for scope SNR cutoff

property exc_spectrum: list[tuple[float, float]]#: Excitation spectrum, alias for spectrum

get_state() → dict[NeuronGroup, ndarray][source]#: Returns a {neuron_group: fluorescence} dict of dFF values.

location: str#

cytoplasm or membrane

Type:: where sensor is expressed

sigma_noise: float#: standard deviation of Gaussian noise in ΔF/F measurement

property snr: float#: Signal-to-noise ratio for 1 AP

cleo.imaging.gcamp3(light_dependent=False, doub_exp_conv=True, pre_existing_cal=False, K_d=287. * nmolar, n_H=2.52, dFF_max=12, sigma_noise=0.018571428571428572, dFF_1AP=0.039, ca_amp=0.05, t_on=1, t_off=1, Ca_rest=50. * nmolar, kappa_S=110, gamma=292.3 * hertz, B_T=200. * umolar, dCa_T=7.6 * umolar, name='gcamp3', **kwparams) → GECI#

Returns a (light-dependent) GECI model with specified submodel choices. Default parameters are taken from NAOMi’s code (Song et al., 2021) as well as Dana et al., 2019 and Zhang et al., 2023.

Only those parameters used in chosen model components apply. If the default is None, then we don’t have it fit yet.

ca_amp, t_on, and t_off are given as in NAOMi, but are converted to the proper scale and units for the double exponential convolution model. Namely, A = ca_amp / (second / 100) and tau_[on|off] = second / t_[on|off].

cleo.imaging.gcamp6f(light_dependent=False, doub_exp_conv=True, pre_existing_cal=False, K_d=290. * nmolar, n_H=2.7, dFF_max=25.2, sigma_noise=0.03748181818181818, dFF_1AP=0.09775500000000001, ca_amp=76.1251, t_on=0.8535, t_off=98.6173, Ca_rest=50. * nmolar, kappa_S=110, gamma=292.3 * hertz, B_T=200. * umolar, dCa_T=7.6 * umolar, name='gcamp6f', **kwparams) → GECI#

Returns a (light-dependent) GECI model with specified submodel choices. Default parameters are taken from NAOMi’s code (Song et al., 2021) as well as Dana et al., 2019 and Zhang et al., 2023.

Only those parameters used in chosen model components apply. If the default is None, then we don’t have it fit yet.

cleo.imaging.gcamp6rs06(light_dependent=False, doub_exp_conv=True, pre_existing_cal=False, K_d=0.32 * umolar, n_H=3, dFF_max=15, sigma_noise=0.030227272727272728, dFF_1AP=None, ca_amp=None, t_on=None, t_off=None, Ca_rest=50. * nmolar, kappa_S=110, gamma=292.3 * hertz, B_T=200. * umolar, dCa_T=7.6 * umolar, name='gcamp6rs06', **kwparams) → GECI#

Returns a (light-dependent) GECI model with specified submodel choices. Default parameters are taken from NAOMi’s code (Song et al., 2021) as well as Dana et al., 2019 and Zhang et al., 2023.

Only those parameters used in chosen model components apply. If the default is None, then we don’t have it fit yet.

Don’t know sigma_noise. Default is the GCaMP6s value.

cleo.imaging.gcamp6rs09(light_dependent=False, doub_exp_conv=True, pre_existing_cal=False, K_d=0.52 * umolar, n_H=3.2, dFF_max=25, sigma_noise=0.030227272727272728, dFF_1AP=None, ca_amp=None, t_on=None, t_off=None, Ca_rest=50. * nmolar, kappa_S=110, gamma=292.3 * hertz, B_T=200. * umolar, dCa_T=7.6 * umolar, name='gcamp6rs09', **kwparams) → GECI#

Returns a (light-dependent) GECI model with specified submodel choices. Default parameters are taken from NAOMi’s code (Song et al., 2021) as well as Dana et al., 2019 and Zhang et al., 2023.

Only those parameters used in chosen model components apply. If the default is None, then we don’t have it fit yet.

Don’t know sigma_noise. Default is the GCaMP6s value.

cleo.imaging.gcamp6s(light_dependent=False, doub_exp_conv=True, pre_existing_cal=False, K_d=147. * nmolar, n_H=2.45, dFF_max=27.2, sigma_noise=0.030227272727272728, dFF_1AP=0.133, ca_amp=54.6943, t_on=0.4526, t_off=68.5461, Ca_rest=50. * nmolar, kappa_S=110, gamma=292.3 * hertz, B_T=200. * umolar, dCa_T=7.6 * umolar, name='gcamp6s', **kwparams) → GECI#

Returns a (light-dependent) GECI model with specified submodel choices. Default parameters are taken from NAOMi’s code (Song et al., 2021) as well as Dana et al., 2019 and Zhang et al., 2023.

Only those parameters used in chosen model components apply. If the default is None, then we don’t have it fit yet.

cleo.imaging.geci(light_dependent: bool, doub_exp_conv: bool, pre_existing_cal: bool, **kwparams) → GECI[source]#

Initializes a GECI model with given parameters.

Parameters:

light_dependent (bool) – Whether the indicator is light-dependent.
doub_exp_conv (bool) – Whether to use double exponential convolution for binding/activation.
pre_existing_cal (bool) – Whether to use calcium concentrations already simulated in the neuron model.
**kwparams – Keyword parameters for GECI and sub-models.

Returns:

A (LightDependent)GECI model specified submodels and parameters.

Return type:

GECI

cleo.imaging.jgcamp7b(light_dependent=False, doub_exp_conv=True, pre_existing_cal=False, K_d=82. * nmolar, n_H=3.06, dFF_max=22.1, sigma_noise=0.007556818181818182, dFF_1AP=0.61712, ca_amp=None, t_on=None, t_off=None, Ca_rest=50. * nmolar, kappa_S=110, gamma=292.3 * hertz, B_T=200. * umolar, dCa_T=7.6 * umolar, name='jgcamp7b', **kwparams) → GECI#

Returns a (light-dependent) GECI model with specified submodel choices. Default parameters are taken from NAOMi’s code (Song et al., 2021) as well as Dana et al., 2019 and Zhang et al., 2023.

Only those parameters used in chosen model components apply. If the default is None, then we don’t have it fit yet.

cleo.imaging.jgcamp7c(light_dependent=False, doub_exp_conv=True, pre_existing_cal=False, K_d=298. * nmolar, n_H=2.44, dFF_max=145.6, sigma_noise=0.011788636363636364, dFF_1AP=0.24605000000000002, ca_amp=None, t_on=None, t_off=None, Ca_rest=50. * nmolar, kappa_S=110, gamma=292.3 * hertz, B_T=200. * umolar, dCa_T=7.6 * umolar, name='jgcamp7c', **kwparams) → GECI#

Returns a (light-dependent) GECI model with specified submodel choices. Default parameters are taken from NAOMi’s code (Song et al., 2021) as well as Dana et al., 2019 and Zhang et al., 2023.

Only those parameters used in chosen model components apply. If the default is None, then we don’t have it fit yet.

cleo.imaging.jgcamp7f(light_dependent=False, doub_exp_conv=True, pre_existing_cal=False, K_d=174. * nmolar, n_H=2.3, dFF_max=30.2, sigma_noise=0.021763636363636363, dFF_1AP=0.22743000000000002, ca_amp=None, t_on=None, t_off=None, Ca_rest=50. * nmolar, kappa_S=110, gamma=292.3 * hertz, B_T=200. * umolar, dCa_T=7.6 * umolar, name='jgcamp7f', **kwparams) → GECI#

Returns a (light-dependent) GECI model with specified submodel choices. Default parameters are taken from NAOMi’s code (Song et al., 2021) as well as Dana et al., 2019 and Zhang et al., 2023.

Only those parameters used in chosen model components apply. If the default is None, then we don’t have it fit yet.

cleo.imaging.jgcamp7s(light_dependent=False, doub_exp_conv=True, pre_existing_cal=False, K_d=68. * nmolar, n_H=2.49, dFF_max=40.4, sigma_noise=0.009975000000000001, dFF_1AP=0.65968, ca_amp=None, t_on=None, t_off=None, Ca_rest=50. * nmolar, kappa_S=110, gamma=292.3 * hertz, B_T=200. * umolar, dCa_T=7.6 * umolar, name='jgcamp7s', **kwparams) → GECI#

Returns a (light-dependent) GECI model with specified submodel choices. Default parameters are taken from NAOMi’s code (Song et al., 2021) as well as Dana et al., 2019 and Zhang et al., 2023.

Only those parameters used in chosen model components apply. If the default is None, then we don’t have it fit yet.

cleo.imaging.ogb1(light_dependent=False, doub_exp_conv=True, pre_existing_cal=False, K_d=250. * nmolar, n_H=1, dFF_max=14, sigma_noise=0.030227272727272728, dFF_1AP=None, ca_amp=None, t_on=None, t_off=None, Ca_rest=50. * nmolar, kappa_S=110, gamma=292.3 * hertz, B_T=200. * umolar, dCa_T=7.6 * umolar, name='ogb1', **kwparams) → GECI#

Returns a (light-dependent) GECI model with specified submodel choices. Default parameters are taken from NAOMi’s code (Song et al., 2021) as well as Dana et al., 2019 and Zhang et al., 2023.

Only those parameters used in chosen model components apply. If the default is None, then we don’t have it fit yet.

Don’t know sigma_noise. Default is the GCaMP6s value.

cleo.imaging.target_neurons_in_plane(ng: NeuronGroup, scope_focus_depth: Quantity, scope_img_width: Quantity, scope_location: Quantity = array([0., 0., 0.]) * metre, scope_direction: tuple = (0, 0, 1), soma_radius: Quantity = 10. * umetre, sensor_location: str = 'cytoplasm') → tuple[NDArray[Any, Int], NDArray[Any, Float], NDArray[Any, 3, Float]][source]#

Returns a tuple of (i_targets, noise_focus_factor, coords_on_plane)

Parameters:

ng (NeuronGroup) – The neuron group to target.
scope_focus_depth (Quantity) – The depth of the focal plane of the microscope.
scope_img_width (Quantity) – The width of the image captured by the microscope.
scope_location (Quantity, optional) – The location of the microscope, by default (0, 0, 0) * mm.
scope_direction (tuple, optional) – The direction of the microscope, by default (0, 0, 1).
soma_radius (Quantity, optional) – The radius of the soma of the neuron, by default 10 * um. Used to compute noise focus factor, since smaller ROIs will have a noisier distribution of fluorescence, averaged over fewer pixels.
sensor_location (str, optional) – The location of the sensor, by default “cytoplasm”.

Returns:

A tuple of (i_targets, noise_focus_factor, coords_on_plane)

Return type:

Tuple[NDArray[(Any,), int], NDArray[(Any,), float], NDArray[(Any, 3), float]]