Database Schema

The synaptic physiology database is provided as sqlite files that can be queried using many relational database tools. Although it is possible to read this dataset using standard SQL queries, we also provide an sqlalchemy model that implements a richer interface to the database. The API reference below is derived from the sqlalchemy model classes, but also doubles as a description of the relational database schema.

Metadata

class aisynphys.database.schema.Metadata(**kwargs)

Sqlalchemy model for “Metadata” database table.

A single metadata related to the entire database.

Attributes

idINTEGER

metaVARCHAR

Slice

class aisynphys.database.schema.Slice(**kwargs)

Sqlalchemy model for “Slice” database table.

All brain slices on which an experiment was attempted.

Attributes

experimentsrelationship

Reference to experiment.id

cortical_sitesrelationship

Reference to cortical_site.id

idINTEGER

ext_idVARCHAR

Unique external ID for this slice

acq_timestampFLOAT

Creation timestamp for slice data acquisition folder.

speciesVARCHAR

human | mouse (from LIMS)

date_of_birthDATETIME

Date of birth for this specimen

ageINTEGER

Specimen age (in days) at time of dissection (from LIMS)

sexVARCHAR

Specimen sex (“M”, “F”, or “unknown”; from LIMS)

weightVARCHAR

Specimen weight (from LIMS)

genotypeVARCHAR

Specimen donor genotype (from LIMS)

orientationVARCHAR

Orientation of the slice plane (eg “sagittal”; from LIMS specimen name)

surfaceVARCHAR

The surface of the slice exposed during the experiment (eg “left”; from LIMS specimen name)

hemisphereVARCHAR

The brain hemisphere from which the slice originated. (from LIMS specimen name)

qualityINTEGER

Experimenter subjective slice quality assessment (0-5)

slice_timeDATETIME

Time when this specimen was sliced

slice_conditionsVARCHAR

JSON containing solutions, perfusion, incubation time, etc.

lims_specimen_nameVARCHAR

Name of LIMS “slice” specimen

storage_pathVARCHAR

Location of data within server or cache storage

metaVARCHAR

Experiment

class aisynphys.database.schema.Experiment(**kwargs)

Sqlalchemy model for “Experiment” database table.

A group of cells patched simultaneously in the same slice.

Attributes

slicerelationship

Reference to slice.id

electrodesrelationship

Reference to electrode.id

cell_listrelationship

Reference to cell.id

pair_listrelationship

Reference to pair.id

sync_recsrelationship

Reference to sync_rec.id

cortical_sitesrelationship

Reference to cortical_site.id

idINTEGER

ext_idVARCHAR

Unique external identifier string for the experiment.

slice_idINTEGER

ID of the slice used for this experiment

project_nameVARCHAR

Name of the project to which this experiment belongs.

dateDATETIME

The date of this experiment

target_regionVARCHAR

The intended brain region for this experiment

internalVARCHAR

The name of the internal solution used in this experiment (or “mixed” if more than one solution was used). The solution should be described in the pycsf database.

acsfVARCHAR

The name of the ACSF solution used in this experiment. The solution should be described in the pycsf database.

target_temperatureFLOAT

The intended temperature of the experiment (measured temperature per-recording is stored elsewhere)

rig_nameVARCHAR

Identifier for the rig that generated these results.

operator_nameVARCHAR

Opertator that generated these results.

storage_pathVARCHAR

Location of data within server or cache storage.

ephys_fileVARCHAR

Name of ephys NWB file relative to storage_path.

acq_timestampFLOAT

Creation timestamp for site data acquisition folder.

metaVARCHAR

Electrode

class aisynphys.database.schema.Electrode(**kwargs)

Sqlalchemy model for “Electrode” database table.

Each electrode records a patch attempt, whether or not it resulted in a successful cell recording.

Attributes

experimentrelationship

Reference to experiment.id

cellrelationship

Reference to cell.id

recordingsrelationship

Reference to recording.id

test_pulsesrelationship

Reference to test_pulse.id

idINTEGER

experiment_idINTEGER

ext_idVARCHAR

Electrode ID (usually 1-8) referenced in external metadata records

patch_statusVARCHAR

Status of the patch attempt: no seal, low seal, GOhm seal, tech fail, or no attempt

start_timeDATETIME

The time when recording began for this electrode.

stop_timeDATETIME

The time when recording ended for this electrode.

device_idINTEGER

External identifier for the device attached to this electrode (usually the MIES A/D channel)

metaVARCHAR

Cell

class aisynphys.database.schema.Cell(**kwargs)

Sqlalchemy model for “Cell” database table.

Each row represents a single cell in an experiment.

Attributes

experimentrelationship

Reference to experiment.id

electroderelationship

Reference to electrode.id

intrinsicrelationship

Reference to intrinsic.id

morphologyrelationship

Reference to morphology.id

stim_pulsesrelationship

Reference to stim_pulse.id

cortical_locationrelationship

Reference to cortical_cell_location.id

patch_seqrelationship

Reference to patch_seq.id

idINTEGER

experiment_idINTEGER

ext_idVARCHAR

Cell ID (usually 1-8) referenced in external metadata records

electrode_idINTEGER

ID of the electrode used to patch this cell, if any.

cre_typeVARCHAR

Comma-separated list of cre drivers apparently expressed by this cell

target_layerVARCHAR

The intended cortical layer for this cell (used as a placeholder until the actual layer call is made)

positionVARCHAR

3D location of this cell in the arbitrary coordinate system of the experiment

depthFLOAT

Depth of the cell (in m) from the cut surface of the slice.

cell_classVARCHAR

Cell class “ex”, “in”, or “mixed” determined by synaptic current, cre type, or morphology. This property makes use of synaptic currents to define cell class; it should _not_ be used when measuring connection probability.

cell_class_nonsynapticVARCHAR

Cell class “ex”, “in”, or “mixed” determined by cre type or morphology. Unlike cell_class, this property excludes synaptic currents as a determinant so that it can be used in measurements of connectivity.

metaVARCHAR

Pair

class aisynphys.database.schema.Pair(**kwargs)

Sqlalchemy model for “Pair” database table.

An ordered pair of cells, possibly connected by a synapse or gap junction.

Attributes

experimentrelationship

Reference to experiment.id

pre_cellrelationship

Reference to cell.id

post_cellrelationship

Reference to cell.id

reciprocalrelationship

Reference to pair.id

pulse_responsesrelationship

Reference to pulse_response.id

synapserelationship

Reference to synapse.id

poly_synapserelationship

Reference to poly_synapse.id

dynamicsrelationship

Reference to dynamics.id

synapse_predictionrelationship

Reference to synapse_prediction.id

gap_junctionrelationship

Reference to gap_junction.id

synapse_modelrelationship

Reference to synapse_model.id

idINTEGER

experiment_idINTEGER

pre_cell_idINTEGER

ID of the presynaptic cell

post_cell_idINTEGER

ID of the postsynaptic cell

has_synapseBOOLEAN

Whether a chemical monosynaptic connection was manually detected for this cell pair

has_polysynapseBOOLEAN

Whether a polysynaptic connection was manually detected for this cell pair

has_electricalBOOLEAN

Whether an electrical synapse / gap junction was manually detected for this cell pair

crosstalk_artifactFLOAT

Amplitude of crosstalk artifact measured in current clamp

n_ex_test_spikesINTEGER

Number of QC-passed spike-responses recorded for this pair at excitatory holding potential

n_in_test_spikesINTEGER

Number of QC-passed spike-responses recorded for this pair at inhibitory holding potential

distanceFLOAT

Distance between somas (in m)

lateral_distanceFLOAT

Distance between somas perpendicular to the pia-wm axis (in m)

vertical_distanceFLOAT

Distance between somas along the pia-wm axis (in m)

reciprocal_idINTEGER

ID of the reciprocal to this cell pair (the pair with pre_cell and post_cell swapped)

metaVARCHAR

Intrinsic

class aisynphys.database.schema.Intrinsic(**kwargs)

Sqlalchemy model for “Intrinsic” database table.

Describes the intrinsic properties of cells using 1 sec long current steps and chirps.

Attributes

cellrelationship

Reference to cell.id

idINTEGER

cell_idINTEGER

The ID of the entry in the cell table to which these results apply

rheobaseFLOAT

Current at rheobase

fi_slopeFLOAT

Slope of the current-spiking relationship

input_resistanceFLOAT

Input resistance of the cell (from response peaks, capturing properties of channels at baseline)

input_resistance_ssFLOAT

(True) steady-state input resistance of the cell

sagFLOAT

Hyperpolarizing sag ratio (peak/steady-state), measured from ~ -100mV current injection

tauFLOAT

Membrane time constant

sag_peak_tFLOAT

Time of peak hyperpolarizing sag.

sag_depolFLOAT

Depolarizing sag ratio (peak/steady-state), measured from the largest subthreshold depolarizing input

sag_peak_t_depolFLOAT

Time of peak depolarizing sag.

ap_upstroke_downstroke_ratioFLOAT

The upstroke-downstroke ratio of the first spike

ap_widthFLOAT

Spike width

ap_upstrokeFLOAT

Spike upstroke rate

ap_downstrokeFLOAT

Spike downstroke rate

ap_threshold_vFLOAT

Spike threshold voltage

ap_peak_deltavFLOAT

Spike peak voltage relative to threshold

ap_fast_trough_deltavFLOAT

AHP / fast trough voltage relative to threshold

firing_rate_rheoFLOAT

Mean firing rate for rheobase sweep

latency_rheoFLOAT

First spike latency for rheobase sweep

firing_rate_40paFLOAT

Mean firing rate for +40pA sweep (relative to rheobase)

latency_40paFLOAT

First spike latency for +40pA sweep (relative to rheobase)

adaptation_indexFLOAT

Adaptation index (ratio of consecutive ISIs), averaged across sweeps

isi_cvFLOAT

Coefficient of variation of ISI distribution, averaged across sweeps

chirp_peak_freqFLOAT

Frequency at which the chirp response peaks

chirp_3db_freqFLOAT

Frequency at which the chirp response amplitude is 3 dB below the peak.

chirp_peak_ratioFLOAT

Ratio of chirp resonance peak amplitude to low-frequency response amplitude

chirp_peak_impedanceFLOAT

Impedance at chirp resonance peak.

chirp_sync_freqFLOAT

Frequency at which the chirp phase response equals zero.

chirp_inductive_phaseFLOAT

Integrated of chirp phase response where phase > 0 (below sync freq).

isi_adapt_ratioFLOAT

Ratio of ISI on 5th to 1st spike

upstroke_adapt_ratioFLOAT

Ratio of upstroke on 5th to 1st spike

downstroke_adapt_ratioFLOAT

Ratio of downstroke on 5th to 1st spike

width_adapt_ratioFLOAT

Ratio of spike width on 5th to 1st spike

threshold_v_adapt_ratioFLOAT

Ratio of spike threshold on 5th to 1st spike

metaVARCHAR

Morphology

class aisynphys.database.schema.Morphology(**kwargs)

Sqlalchemy model for “Morphology” database table.

Describes morphological properties of cells.

Attributes

cellrelationship

Reference to cell.id

idINTEGER

cell_idINTEGER

The ID of the cell described by each record

pyramidalBOOLEAN

Indicates whether the experimenter labeled the cell as pyramidal. This call is based on the presence of a prominent apical dendrite seen in the fluorescent dye fill during experiment. The dendrite_type column is recommended as a more reliable indicator of excitatory morphology.

qual_morpho_typeVARCHAR

Qualitative desctription of cell morphology

dendrite_typeVARCHAR

Dendrite type of cell (spiny, aspiny, sparsely spiny) determined from biocytin staining. Generally spiny cells are taken to be excitatory; aspiny or sparsely spiny cells are inhibitory.

apical_trunc_distanceFLOAT

Measured distance to truncation of apical dendrite

axon_trunc_distanceFLOAT

Measured distance to truncation of axon

apical_truncationVARCHAR

Qualitative description of apical dendrite truncation

axon_truncationVARCHAR

Qualitative description of axon truncation

axon_originVARCHAR

Origination of axon; soma, dendrite, etc

metaVARCHAR

SyncRec

class aisynphys.database.schema.SyncRec(**kwargs)

Sqlalchemy model for “SyncRec” database table.

A synchronous recording represents a “sweep” – multiple recordings that were made simultaneously on different electrodes.

Attributes

experimentrelationship

Reference to experiment.id

recordingsrelationship

Reference to recording.id

idINTEGER

experiment_idINTEGER

ext_idVARCHAR

External ID of the SyncRecording

temperatureFLOAT

Bath temperature during this recording

metaVARCHAR

Recording

class aisynphys.database.schema.Recording(**kwargs)

Sqlalchemy model for “Recording” database table.

A recording represents a single contiguous sweep recorded from a single electrode.

Attributes

sync_recrelationship

Reference to sync_rec.id

electroderelationship

Reference to electrode.id

patch_clamp_recordingrelationship

Reference to patch_clamp_recording.id

test_pulsesrelationship

Reference to test_pulse.id

stim_pulsesrelationship

Reference to stim_pulse.id

baselinesrelationship

Reference to baseline.id

idINTEGER

sync_rec_idINTEGER

References the synchronous recording to which this recording belongs.

electrode_idINTEGER

Identifies the electrode that generated this recording

start_timeDATETIME

The clock time at the start of this recording

sample_rateINTEGER

Sample rate for this recording

device_nameVARCHAR

Name of the device that generated this recording

stim_nameVARCHAR

The name of the stimulus protocol used in this recording, if any

stim_metaVARCHAR

A data structure describing the stimulus protocol

metaVARCHAR

PatchClampRecording

class aisynphys.database.schema.PatchClampRecording(**kwargs)

Sqlalchemy model for “PatchClampRecording” database table.

Extra data for recordings made with a patch clamp amplifier

Attributes

recordingrelationship

Reference to recording.id

multi_patch_proberelationship

Reference to multi_patch_probe.id

nearest_test_pulserelationship

Reference to test_pulse.id

idINTEGER

recording_idINTEGER

clamp_modeVARCHAR

The mode used by the patch clamp amplifier: “ic” or “vc”

patch_modeVARCHAR

The state of the membrane patch. E.g. ‘whole cell’, ‘cell attached’, ‘loose seal’, ‘bath’, ‘inside out’, ‘outside out’

baseline_potentialFLOAT

Median steady-state potential (recorded for IC or commanded for VC) during the recording

baseline_currentFLOAT

Median steady-state current (recorded for VC or commanded for IC) during the recording

baseline_noise_stdevFLOAT

Noise measured as standard deviation of steady-state parts of the recording

nearest_test_pulse_idINTEGER

ID of the test pulse that was recorded closest to this recording (and possibly embedded within the recording)

qc_passBOOLEAN

Indicates whether this recording passes a minimal ephys QC

access_adj_baseline_potentialFLOAT

Baseline membrane potential estimated by adjusting VC command for access resistance

metaVARCHAR

MultiPatchProbe

class aisynphys.database.schema.MultiPatchProbe(**kwargs)

Sqlalchemy model for “MultiPatchProbe” database table.

Extra data for multipatch recordings intended to test synaptic dynamics.

Attributes

patch_clamp_recordingrelationship

Reference to patch_clamp_recording.id

idINTEGER

patch_clamp_recording_idINTEGER

induction_frequencyFLOAT

The induction frequency (Hz) of presynaptic pulses

recovery_delayFLOAT

The recovery delay (s) inserted between presynaptic pulses

n_spikes_evokedINTEGER

The number of presynaptic spikes evoked

metaVARCHAR

TestPulse

class aisynphys.database.schema.TestPulse(**kwargs)

Sqlalchemy model for “TestPulse” database table.

A short, usually hyperpolarizing pulse used to test the resistance of pipette, cell access, or cell membrane.

Attributes

electroderelationship

Reference to electrode.id

recordingrelationship

Reference to recording.id

idINTEGER

electrode_idINTEGER

ID of the electrode on which this test pulse was recorded.

recording_idINTEGER

ID of the recording that contains this test pulse, if any.

start_indexINTEGER

Index into recording where test pulse begins

stop_indexINTEGER

Index into recording where test pulse ends

baseline_currentFLOAT

Pipette current immediately before test pulse

baseline_potentialFLOAT

Membrane potential immediately before test pulse

access_resistanceFLOAT

Access resistance estimated from test pulse

input_resistanceFLOAT

Membrane input resistance estimated from test pulse

capacitanceFLOAT

Membrane capacitance estimated from test pulse

time_constantFLOAT

Decay time constant of exponential fit to test pulse

access_resistance_lowpassFLOAT

median access resistance of nearby test pulses (to filter out artifacts)

metaVARCHAR

StimPulse

class aisynphys.database.schema.StimPulse(**kwargs)

Sqlalchemy model for “StimPulse” database table.

A pulse stimulus intended to evoke an action potential

Attributes

recordingrelationship

Reference to recording.id

cellrelationship

Reference to cell.id

spikesrelationship

Reference to stim_spike.id

pulse_responserelationship

Reference to pulse_response.id

dataBLOB

Numpy array of presynaptic recording sampled at 20kHz

idINTEGER

recording_idINTEGER

pulse_numberINTEGER

The ordinal position of this pulse within a train of pulses.

cell_idINTEGER

Cell that was targeted by this stimulus, if any.

onset_timeFLOAT

The starting time of the pulse, relative to the beginning of the recording

amplitudeFLOAT

Amplitude of the presynaptic pulse

durationFLOAT

Length of the pulse in seconds

n_spikesINTEGER

Number of spikes evoked by this pulse

first_spike_timeFLOAT

Time of the first spike evoked by this pulse, measured from the beginning of the recording until the max slope of the spike rising phase.

data_start_timeFLOAT

Starting time of the data chunk, relative to the beginning of the recording

positionVARCHAR

3D location of this stimulation in the arbitrary coordinate system of the experiment

qc_passBOOLEAN

Indicates whether this stimulation passed qc.

previous_pulse_dtFLOAT

Time elapsed since the last stimulus in the same cell

metaVARCHAR

StimSpike

class aisynphys.database.schema.StimSpike(**kwargs)

Sqlalchemy model for “StimSpike” database table.

An action potential evoked by a stimulus pulse. Note that some metrics may be omitted if they could not be determined accurately.

Attributes

stim_pulserelationship

Reference to stim_pulse.id

idINTEGER

stim_pulse_idINTEGER

onset_timeFLOAT

The time of the earliest detectable effect of the spike.

max_slope_timeFLOAT

The time of the max slope of the spike, relative to the beginning of the recording.

max_slopeFLOAT

Maximum slope of the presynaptic spike

peak_timeFLOAT

The time of the peak of the spike, relative to the beginning of the recording.

peak_diffFLOAT

Amplitude of the spike peak, relative to baseline

peak_valueFLOAT

Absolute value of the spike peak

metaVARCHAR

PulseResponse

class aisynphys.database.schema.PulseResponse(**kwargs)

Sqlalchemy model for “PulseResponse” database table.

A chunk of postsynaptic recording taken during a presynaptic pulse stimulus

Attributes

stim_pulserelationship

Reference to stim_pulse.id

recordingrelationship

Reference to recording.id

pairrelationship

Reference to pair.id

baselinerelationship

Reference to baseline.id

pulse_response_fitrelationship

Reference to pulse_response_fit.id

pulse_response_strengthrelationship

Reference to pulse_response_strength.id

dataBLOB

numpy array of response data sampled at 20kHz

idINTEGER

recording_idINTEGER

The full recording from which this pulse was extracted

stim_pulse_idINTEGER

The presynaptic pulse

pair_idINTEGER

The pre-post cell pair involved in this pulse response

baseline_idINTEGER

A random baseline snippet matched from the same recording.

data_start_timeFLOAT

Starting time of this chunk of the recording in seconds, relative to the beginning of the recording

ex_qc_passBOOLEAN

Indicates whether this recording snippet passes QC for excitatory synapse probing

in_qc_passBOOLEAN

Indicates whether this recording snippet passes QC for inhibitory synapse probing

metaVARCHAR

Baseline

class aisynphys.database.schema.Baseline(**kwargs)

Sqlalchemy model for “Baseline” database table.

A snippet of baseline data used for comparison to pulse_response records

Attributes

recordingrelationship

Reference to recording.id

pulse_responsesrelationship

Reference to pulse_response.id

dataBLOB

numpy array of baseline data sampled at 20kHz

idINTEGER

recording_idINTEGER

The recording from which this baseline snippet was extracted.

data_start_timeFLOAT

Starting time of this chunk of the recording in seconds, relative to the beginning of the recording

modeFLOAT

most common value in the baseline snippet

ex_qc_passBOOLEAN

Indicates whether this recording snippet passes QC for excitatory synapse probing

in_qc_passBOOLEAN

Indicates whether this recording snippet passes QC for inhibitory synapse probing

metaVARCHAR

Synapse

class aisynphys.database.schema.Synapse(**kwargs)

Sqlalchemy model for “Synapse” database table.

Chemical synapse properties

Attributes

pairrelationship

Reference to pair.id

avg_response_fitsrelationship

Reference to avg_response_fit.id

resting_state_fitrelationship

Reference to resting_state_fit.id

conductancerelationship

Reference to conductance.id

idINTEGER

pair_idINTEGER

The ID of the entry in the pair table to which these results apply

synapse_typeVARCHAR

“ex” or “in” indicating whether the synapse is excitatory or inhibitory

latencyFLOAT

Latency in seconds from spike max slope until synaptic response onset.

psp_amplitudeFLOAT

Amplitude of resting-state PSPs in Volts.

psp_rise_timeFLOAT

Rise time in seconds measured from averaged PSPs.

psp_decay_tauFLOAT

decay time constant in seconds measured from averaged PSPs.

psc_amplitudeFLOAT

Amplitude of resting-state PSCs in Amperes.

psc_rise_timeFLOAT

Rise time in seconds measured from averaged PSCs.

psc_decay_tauFLOAT

decay time constant in seconds measured from averaged PSCs.

metaVARCHAR

AvgResponseFit

class aisynphys.database.schema.AvgResponseFit(**kwargs)

Sqlalchemy model for “AvgResponseFit” database table.

Fit to average post synaptic response for a given pair. Each pair may have fits for VC and IC recordings, held at -70 and -55 mV.

Attributes

synapserelationship

Reference to synapse.id

poly_synapserelationship

Reference to poly_synapse.id

avg_dataBLOB

Averaged PSP/PSC that was fit.

idINTEGER

synapse_idINTEGER

The ID of the entry in the synapse table to which these results apply

poly_synapse_idINTEGER

The ID of the entry in the poly_synapse table to which these results apply

clamp_modeVARCHAR

The clamp mode “ic” or “vc”

holdingFLOAT

The holding potential -70 or -55

laser_power_commandFLOAT

The pockel cell command value for the 2p laser

fit_xoffsetFLOAT

Fit time from max slope of the presynaptic spike until onset of the synaptic response (seconds)

fit_yoffsetFLOAT

Fit constant y-offset (amps or volts)

fit_ampFLOAT

Fit synaptic response amplitude (amps or volts)

fit_rise_timeFLOAT

Fit rise time (seconds) from response onset until peak

fit_rise_powerFLOAT

Fit rise exponent (usually fixed at 2)

fit_decay_tauFLOAT

Fit exponential decay time constant (seconds)

fit_exp_ampFLOAT

Fit baseline exponental amplitude (amps or volts)

fit_exp_tauFLOAT

Fit baseline exponental decay time constant (seconds)

nrmseFLOAT

Normalized RMS error of the fit residual

initial_xoffsetFLOAT

Initial latency supplied to fitting algorithm

manual_qc_passBOOLEAN

If true, this fit passes manual verification QC

avg_data_start_timeFLOAT

Starting time of avg_data, relative to the presynaptic spike

n_averaged_responsesINTEGER

Number of postsynaptic responses that were averaged in avg_data

avg_baseline_noiseFLOAT

Standard deviation of avg_data before the presynaptic stimulus

metaVARCHAR

PolySynapse

class aisynphys.database.schema.PolySynapse(**kwargs)

Sqlalchemy model for “PolySynapse” database table.

Chemical properties of polysnaptic events

Attributes

pairrelationship

Reference to pair.id

avg_response_fitsrelationship

Reference to avg_response_fit.id

resting_state_fitrelationship

Reference to resting_state_fit.id

idINTEGER

pair_idINTEGER

The ID of the entry in the pair table to which these results apply

synapse_typeVARCHAR

“ex” or “in” indicating whether the synapse is excitatory or inhibitory

latencyFLOAT

Latency in seconds from spike max slope until synaptic response onset.

psp_amplitudeFLOAT

Amplitude of resting-state PSPs in Volts.

psp_rise_timeFLOAT

Rise time in seconds measured from averaged PSPs.

psp_decay_tauFLOAT

decay time constant in seconds measured from averaged PSPs.

psc_amplitudeFLOAT

Amplitude of resting-state PSCs in Amperes.

psc_rise_timeFLOAT

Rise time in seconds measured from averaged PSCs.

psc_decay_tauFLOAT

decay time constant in seconds measured from averaged PSCs.

metaVARCHAR

PulseResponseFit

class aisynphys.database.schema.PulseResponseFit(**kwargs)

Sqlalchemy model for “PulseResponseFit” database table.

Curve fits to individual synaptic responses.

Attributes

pulse_responserelationship

Reference to pulse_response.id

idINTEGER

pulse_response_idINTEGER

fit_ampFLOAT

Fit amplitude of the response to this stimulus

fit_latencyFLOAT

Fit latency of the response to this stimulus

fit_yoffsetFLOAT

Fit y offset of the response to this stimulus

fit_rise_timeFLOAT

Fit rise time of the response to this stimulus

fit_decay_tauFLOAT

Fit decay tau of the response to this stimulus

fit_exp_ampFLOAT

Fit exponential amplitude of the baseline before this stimulus

fit_nrmseFLOAT

Normalized RMS error of the fit

baseline_fit_ampFLOAT

Fit amplitude of the baseline before the stimulus

baseline_fit_latencyFLOAT

Fit latency of the baseline before the stimulus

baseline_fit_yoffsetFLOAT

Fit y offset of the baseline before the stimulus

baseline_fit_rise_timeFLOAT

Fit rise time of the baseline before the stimulus

baseline_fit_decay_tauFLOAT

Fit decay tau of the baseline before the stimulus

baseline_fit_exp_ampFLOAT

Fit exponential amplitude of the baseline before this stimulus

baseline_fit_nrmseFLOAT

Normalized RMS error of the fit

dec_fit_ampFLOAT

Fit amplitude of the deconvolved response to this stimulus

dec_fit_reconv_ampFLOAT

Fit amplitude of the deconvolved response to this stimulus, reconvolved to physiological units

dec_fit_latencyFLOAT

Fit latency of the deconvolved response to this stimulus

dec_fit_yoffsetFLOAT

Fit y offset of the deconvolved response to this stimulus

dec_fit_rise_timeFLOAT

Fit rise time of the deconvolved response to this stimulus

dec_fit_decay_tauFLOAT

Fit decay tau of the deconvolved response to this stimulus

dec_fit_nrmseFLOAT

Normalized RMS error of the fit

baseline_dec_fit_ampFLOAT

Fit amplitude of the deconvolved baseline before the stimulus

baseline_dec_fit_reconv_ampFLOAT

Fit amplitude of the deconvolved baseline before this stimulus, reconvolved to physiological units

baseline_dec_fit_latencyFLOAT

Fit latency of the deconvolved baseline before the stimulus

baseline_dec_fit_yoffsetFLOAT

Fit y offset of the deconvolved baseline before the stimulus

baseline_dec_fit_rise_timeFLOAT

Fit rise time of the deconvolved baseline before the stimulus

baseline_dec_fit_decay_tauFLOAT

Fit decay tau of the deconvolved baseline before the stimulus

baseline_dec_fit_nrmseFLOAT

Normalized RMS error of the fit

metaVARCHAR

PulseResponseStrength

class aisynphys.database.schema.PulseResponseStrength(**kwargs)

Sqlalchemy model for “PulseResponseStrength” database table.

Measurements of membrane potential or current deflection following each evoked presynaptic spike.

Attributes

pulse_responserelationship

Reference to pulse_response.id

baselinerelationship

Reference to baseline.id

idINTEGER

pulse_response_idINTEGER

pos_ampFLOAT

max-median offset from baseline to pulse response window

neg_ampFLOAT

min-median offset from baseline to pulse response window

pos_dec_ampFLOAT

max-median offset from baseline to pulse response window from devonvolved trace

neg_dec_ampFLOAT

min-median offset from baseline to pulse response window from deconvolved trace

pos_dec_latencyFLOAT

duration (seconds) from presynaptic spike max dv/dt until the sample measured in pos_dec_amp

neg_dec_latencyFLOAT

duration (seconds) from presynaptic spike max dv/dt until the sample measured in neg_dec_amp

crosstalkFLOAT

trace difference immediately before and after onset of presynaptic stimulus pulse

baseline_idINTEGER

baseline_pos_ampFLOAT

max-median offset from baseline to pulse response window

baseline_neg_ampFLOAT

min-median offset from baseline to pulse response window

baseline_pos_dec_ampFLOAT

max-median offset from baseline to pulse response window from devonvolved trace

baseline_neg_dec_ampFLOAT

min-median offset from baseline to pulse response window from deconvolved trace

baseline_pos_dec_latencyFLOAT

duration (seconds) from presynaptic spike max dv/dt until the sample measured in pos_dec_amp

baseline_neg_dec_latencyFLOAT

duration (seconds) from presynaptic spike max dv/dt until the sample measured in neg_dec_amp

baseline_crosstalkFLOAT

trace difference immediately before and after onset of presynaptic stimulus pulse

metaVARCHAR

Dynamics

class aisynphys.database.schema.Dynamics(**kwargs)

Sqlalchemy model for “Dynamics” database table.

Describes short term dynamics of synaptic connections.

Attributes

pairrelationship

Reference to pair.id

stp_all_stimuliVARCHAR

list of initial, induction, and recovery measurements for all stimuli presented

idINTEGER

pair_idINTEGER

The ID of the cell pair described by each record

qc_passBOOLEAN

Indicates whether dynamics records pass quality control

n_source_eventsINTEGER

Number of qc-passed pulse response amplitudes from which dynamics metrics were generated

paired_pulse_ratio_50hzFLOAT

The median ratio of 2nd / 1st pulse amplitudes for 50Hz pulse trains.

stp_initial_50hzFLOAT

The median relative change from 1st to 2nd pulse for 50Hz pulse trains

stp_initial_50hz_nFLOAT

Number of samples represented in stp_initial_50Hz

stp_initial_50hz_stdFLOAT

Standard deviation of samples represented in stp_initial_50Hz

stp_induction_50hzFLOAT

The median relative change from 1st to 6th-8th pulses for 50Hz pulse trains

stp_induction_50hz_nFLOAT

Number of samples represented in stp_induction_50Hz

stp_induction_50hz_stdFLOAT

Standard deviation of samples represented in stp_induction_50Hz

stp_recovery_250msFLOAT

The median relative change from 1st-4th to 9th-12th pulses for pulse trains with a 250 ms recovery period

stp_recovery_250ms_nFLOAT

Number of samples represented in stp_recovery_250ms

stp_recovery_250ms_stdFLOAT

Standard deviation of samples represented in stp_recovery_250ms

stp_recovery_single_250msFLOAT

The median relative change from 1st to 9th pulses for pulse trains with a 250 ms recovery period

stp_recovery_single_250ms_nFLOAT

Number of samples represented in stp_recovery_single_250ms

stp_recovery_single_250ms_stdFLOAT

Standard deviation of samples represented in stp_recovery_single_250ms

pulse_amp_90th_percentileFLOAT

The 90th-percentile largest pulse amplitude, used to normalize change values in this table

noise_amp_90th_percentileFLOAT

The 90th-percentile largest amplitude measured from background noise, used for comparison to pulse_amp_90th_percentile

pulse_amp_first_50hzFLOAT

Median amplitude of first pulse on 50 hz trains

pulse_amp_first_50hz_nFLOAT

Number of samples represented in pulse_amp_first_50hz

pulse_amp_first_50hz_stdFLOAT

Standard deviation of samples represented in pulse_amp_stp_initial_50hz

pulse_amp_stp_initial_50hzFLOAT

Median amplitude of second pulse on 50 hz trains

pulse_amp_stp_initial_50hz_nFLOAT

Number of samples represented in pulse_amp_stp_initial_50hz

pulse_amp_stp_initial_50hz_stdFLOAT

Standard deviation of samples represented in pulse_amp_stp_initial_50hz

pulse_amp_stp_induction_50hzFLOAT

Median amplitude of 6th-8th pulses on 50 hz trains

pulse_amp_stp_induction_50hz_nFLOAT

Number of samples represented in pulse_amp_stp_induction_50hz

pulse_amp_stp_induction_50hz_stdFLOAT

Standard deviation of samples represented in pulse_amp_stp_induction_50hz

pulse_amp_stp_recovery_250msFLOAT

Median amplitude of 9th-12th pulses on 50 hz trains

pulse_amp_stp_recovery_250ms_nFLOAT

Number of samples represented in pulse_amp_stp_recovery_250ms

pulse_amp_stp_recovery_250ms_stdFLOAT

Standard deviation of samples represented in pulse_amp_stp_recovery_250ms

pulse_amp_stp_recovery_single_250msFLOAT

Median amplitude of 9th pulse on 50 hz trains

pulse_amp_stp_recovery_single_250ms_nFLOAT

Number of samples represented in pulse_amp_stp_recovery_single_250ms

pulse_amp_stp_recovery_single_250ms_stdFLOAT

Standard deviation of samples represented in pulse_amp_stp_recovery_single_250ms

noise_stdFLOAT

Standard deviation of PSP amplitudes measured from background noise

variability_resting_stateFLOAT

Variability of PSP amplitudes only from events with no preceding spikes for at least 8 seconds, corrected for background noise.

variability_second_pulse_50hzFLOAT

Variability of PSP amplitudes in 2nd pulses of 50Hz trains

variability_stp_induced_state_50hzFLOAT

Variability of PSP amplitudes in 5th-8th pulses of 50Hz trains

variability_change_initial_50hzFLOAT

Difference between variability of 1st and 2nd pulses in 50Hz trains, corrected for background noise.

variability_change_induction_50hzFLOAT

Difference between variability of 1st and 5th-8th pulses in 50Hz trains, corrected for background noise.

paired_event_correlation_1_2_rFLOAT

Pearson correlation coefficient for amplitudes of 1st:2nd pulses in 50Hz trains.

paired_event_correlation_1_2_pFLOAT

Pearson correlation p-value related to paired_event_correlation_1_2_r.

paired_event_correlation_2_4_rFLOAT

Pearson correlation coefficient for amplitudes of 1st:2nd pulses in 50Hz trains.

paired_event_correlation_2_4_pFLOAT

Pearson correlation p-value related to paired_event_correlation_1_2_r.

paired_event_correlation_4_8_rFLOAT

Pearson correlation coefficient for amplitudes of 1st:2nd pulses in 50Hz trains.

paired_event_correlation_4_8_pFLOAT

Pearson correlation p-value related to paired_event_correlation_1_2_r.

metaVARCHAR

SynapsePrediction

class aisynphys.database.schema.SynapsePrediction(**kwargs)

Sqlalchemy model for “SynapsePrediction” database table.

Unbiased metrics used for automated synapse detection.

Attributes

pairrelationship

Reference to pair.id

idINTEGER

pair_idINTEGER

The ID of the entry in the pair table to which these results apply

synapse_typeVARCHAR

String “ex” or “in”, indicating whether this analysis chose to treat the pair as excitatory or inhibitory

ic_n_samplesINTEGER

Number of samples (pulse responses) that were pooled from current clamp recordings

ic_crosstalk_meanFLOAT

ic_base_crosstalk_meanFLOAT

ic_amp_meanFLOAT

ic_amp_stdevFLOAT

ic_base_amp_meanFLOAT

ic_base_amp_stdevFLOAT

ic_amp_ttestFLOAT

ic_amp_ks2sampFLOAT

ic_deconv_amp_meanFLOAT

ic_deconv_amp_stdevFLOAT

ic_base_deconv_amp_meanFLOAT

ic_base_deconv_amp_stdevFLOAT

ic_deconv_amp_ttestFLOAT

ic_deconv_amp_ks2sampFLOAT

ic_latency_meanFLOAT

ic_latency_stdevFLOAT

ic_base_latency_meanFLOAT

ic_base_latency_stdevFLOAT

ic_latency_ttestFLOAT

ic_latency_ks2sampFLOAT

vc_n_samplesINTEGER

vc_crosstalk_meanFLOAT

vc_base_crosstalk_meanFLOAT

vc_amp_meanFLOAT

vc_amp_stdevFLOAT

vc_base_amp_meanFLOAT

vc_base_amp_stdevFLOAT

vc_amp_ttestFLOAT

vc_amp_ks2sampFLOAT

vc_deconv_amp_meanFLOAT

vc_deconv_amp_stdevFLOAT

vc_base_deconv_amp_meanFLOAT

vc_base_deconv_amp_stdevFLOAT

vc_deconv_amp_ttestFLOAT

vc_deconv_amp_ks2sampFLOAT

vc_latency_meanFLOAT

vc_latency_stdevFLOAT

vc_base_latency_meanFLOAT

vc_base_latency_stdevFLOAT

vc_latency_ttestFLOAT

vc_latency_ks2sampFLOAT

ic_average_responseBLOB

ic_average_response_t0FLOAT

ic_average_base_stdevFLOAT

vc_average_responseBLOB

vc_average_response_t0FLOAT

vc_average_base_stdevFLOAT

ic_fit_ampFLOAT

ic_fit_xoffsetFLOAT

ic_fit_yoffsetFLOAT

ic_fit_rise_timeFLOAT

ic_fit_rise_powerFLOAT

ic_fit_decay_tauFLOAT

ic_fit_exp_ampFLOAT

ic_fit_exp_tauFLOAT

ic_fit_nrmseFLOAT

vc_fit_ampFLOAT

vc_fit_xoffsetFLOAT

vc_fit_yoffsetFLOAT

vc_fit_rise_timeFLOAT

vc_fit_rise_powerFLOAT

vc_fit_decay_tauFLOAT

vc_fit_exp_ampFLOAT

vc_fit_exp_tauFLOAT

vc_fit_nrmseFLOAT

metaVARCHAR

RestingStateFit

class aisynphys.database.schema.RestingStateFit(**kwargs)

Sqlalchemy model for “RestingStateFit” database table.

Contains curve fits to averages of “resting state” synaptic responses.

Attributes

synapserelationship

Reference to synapse.id

poly_synapserelationship

Reference to poly_synapse.id

idINTEGER

synapse_idINTEGER

The ID of the entry in the synapse table to which these results apply

poly_synapse_idINTEGER

The ID of the entry in the poly_synapse table to which these results apply

ic_ampFLOAT

fit amplitude of current clamp average first pulses

ic_latencyFLOAT

fit time elapsed since the time of presynaptic spike (max dv/dt) of current clamp data

ic_rise_timeFLOAT

fit rise time of psp of current clamp data

ic_decay_tauFLOAT

fit decay of psp of current clamp data

ic_exp_ampFLOAT

fit amplitude of exponential baseline

ic_exp_tauFLOAT

fit tau of exponential baseline

ic_avg_dataBLOB

array of the data voltage waveform used in fitting

ic_avg_data_start_timeFLOAT

time value of the first sample in ic_avg_data, relative to the presynaptic spike

ic_pulse_idsBLOB

data base pulse ids included in the current clamp fit

ic_nrmseFLOAT

error of fit of current clamp fit

vc_ampFLOAT

fit amplitude of voltage clamp average first pulses

vc_latencyFLOAT

fit time elapsed since the time of presynaptic spike (max dv/dt) of voltage clamp data

vc_rise_timeFLOAT

fit rise time of psp measured in voltage clamp

vc_decay_tauFLOAT

fit decay of psp measured in voltage clamp

vc_exp_ampFLOAT

fit amplitude of exponential baseline

vc_exp_tauFLOAT

fit tau of exponential baseline

vc_avg_dataBLOB

array of the data current waveform used in fitting

vc_avg_data_start_timeFLOAT

time value of the first sample in vc_avg_data, relative to the presynaptic spike

vc_pulse_idsBLOB

data base pulse ids included in the voltage clamp fit

vc_nrmseFLOAT

error of fit of voltage clamp fit

metaVARCHAR

GapJunction

class aisynphys.database.schema.GapJunction(**kwargs)

Sqlalchemy model for “GapJunction” database table.

Describes the presence of gap junction.

Attributes

pairrelationship

Reference to pair.id

idINTEGER

pair_idINTEGER

The ID of the entry in the pair table to which these results apply

corr_coeff_pulseFLOAT

The Pearson correlation coefficient of pre- and post-synaptic long pulse

corr_coeff_noiseFLOAT

The Pearson correlation coefficient of pre- and post-synaptic background

p_val_pulseFLOAT

The Pearson p-value of pre- and post-synaptic long pulse

p_val_noiseFLOAT

The Pearson p-value of pre- and post-synaptic background

coupling_coeff_pulseFLOAT

The coupling coefficient of pre- and post-synaptic long pulse

coupling_coeff_noiseFLOAT

The coupling coefficient of pre- and post-synaptic background

junctional_conductanceFLOAT

The junctional conductance of pre- and post-synaptic long pulse

metaVARCHAR

CorticalCellLocation

class aisynphys.database.schema.CorticalCellLocation(**kwargs)

Sqlalchemy model for “CorticalCellLocation” database table.

Each row holds location information for a single cortical cell.

Attributes

cellrelationship

Reference to cell.id

cortical_siterelationship

Reference to cortical_site.id

idINTEGER

cell_idINTEGER

ID of the cell these locations apply to.

cortical_site_idINTEGER

ID of the site location measurements fit within.

cortical_layerVARCHAR

Cortical layer of cell defined by layer drawings (after trimming, not identical to that in LIMS)

distance_to_piaFLOAT

The distance from the cell to the pial surface in m.

distance_to_wmFLOAT

The distance from the cell to the white matter in m.

fractional_depthFLOAT

The cortical depth of the cell where pia is 0 and wm is 1.

layer_depthFLOAT

Absolute depth within the layer in m.

layer_thicknessFLOAT

Local thickness of layer in m.

fractional_layer_depthFLOAT

Fractional depth within the cells layer.

positionVARCHAR

2D array, position of cell in slice image coordinates (in m)

metaVARCHAR

CorticalSite

class aisynphys.database.schema.CorticalSite(**kwargs)

Sqlalchemy model for “CorticalSite” database table.

Each row holds measurements about one cortical site in a slice.

Attributes

slicerelationship

Reference to slice.id

cell_locationsrelationship

Reference to cortical_cell_location.id

experimentrelationship

Reference to experiment.id

idINTEGER

slice_idINTEGER

ID of the slice this site belongs to.

experiment_idINTEGER

ID to the experiment this site is part of

pia_to_wm_distanceFLOAT

The distance (in m) from the pia to the white matter.

pia_positionVARCHAR

3D location where the pia was marked in the arbitrary coordinate system of the experiment

wm_positionVARCHAR

3D location where the wm was marked in the arbitrary coordinate system of the experiment

layer_boundariesVARCHAR

Dictionary with fractional layer boundaries appropriate for the site.

brain_regionVARCHAR

The name of the brain region for the site.

metaVARCHAR

PatchSeq

class aisynphys.database.schema.PatchSeq(**kwargs)

Sqlalchemy model for “PatchSeq” database table.

Describes transcriptomic data of a cell obtained from patch_seq experiments

Attributes

cellrelationship

Reference to cell.id

idINTEGER

cell_idINTEGER

The ID of the cell described by each record

tube_idVARCHAR

Patched Cell Container ID used for RNA analysis

nucleusVARCHAR

Whether the nucleus was recovered from the cell, +, -, ?

resealBOOLEAN

Was there a giga-reseal during nucleus extraction

patchseq_hashVARCHAR

Hash of patchseq results from amplification and mapping used for updating

result_baVARCHAR

Pass/Fail

area_400_10000bpFLOAT

Area (0-1) of amplified content in the 400-10,000 bp range which is an indication of intact RNA

picogreen_yieldFLOAT

(pg/ul)

cluster_detailVARCHAR

Detailed name of last mapped cluster, for class-level nodes this a descriptive name

cluster_labelVARCHAR

Label of last mapped cluster, numerical for class-level nodes

scoreFLOAT

Mapping score from 0-1

res_indexFLOAT

Resolution of the last mapped cluster from 0-1 with 1 being the terminal leaf

top_leafVARCHAR

top_leaf_scoreFLOAT

Confidence of top_leaf mapping (0-1)

broad_class_labelVARCHAR

Presumed class designation; assinged to all cells regardless of mapping depth

subclass_labelVARCHAR

Presumed subclass designation; assigned to all cells regardless of mapping depth

quality_scoreFLOAT

norm_marker_sumFLOAT

seurat_clusterVARCHAR

Mapped cluster based on Seurat method

seurat_scoreFLOAT

Mapping score of seurat_cluster (0-1)

tree_first_clusterVARCHAR

First mapping cluster based on Tree method

tree_first_btFLOAT

Mapping score of first cluster (0-1)

tree_first_klFLOAT

Divergence of mapping score to FACS data

tree_first_corFLOAT

Correlation of mapping score to FACS data

tree_second_clusterVARCHAR

Second mapping cluster based on Tree method

tree_second_btFLOAT

Mapping score of second cluster (0-1)

tree_second_klFLOAT

Divergence of mapping score to FACS data

tree_second_corFLOAT

Correlation of mapping score to FACS data

tree_third_clusterVARCHAR

Third mapping cluster based on Tree method

tree_third_btFLOAT

Mapping score of third cluster (0-1)

tree_callVARCHAR

Tree mapping

genes_detectedINTEGER

Number of genes detected

t_typeVARCHAR

Transcriptomic type = tree_first_cluster if tree_call in [Core, I1]

last_mapVARCHAR

mapping from the last batch run

last_scoreVARCHAR

mapping score from the last batch run

mapped_subclassVARCHAR

Subclass that this cell mapped to

batchVARCHAR

Shiny batch number, used for tracking updates

metaVARCHAR

SynapseModel

class aisynphys.database.schema.SynapseModel(**kwargs)

Sqlalchemy model for “SynapseModel” database table.

Summary of stochastic model outputs per synapse

Attributes

pairrelationship

Reference to pair.id

parameter_spaceVARCHAR

Describes the parameter space searched by the model

marginal_distributionsVARCHAR

Contains marginal distributions for all model parameters

confidence_intervalsVARCHAR

Contains confidence intervals for all model parameters

ml_stp_all_stimuliVARCHAR

list of initial, induction, and recovery measurements for all stimuli presented

idINTEGER

pair_idINTEGER

The ID of the cell pair described by each record

n_source_eventsINTEGER

Number of qc-passed pulse response amplitudes used to fit the model

sparse_pca_vectorBLOB

Sparse PCA vector describing model output over entire parameter space

max_likelihoodFLOAT

The maximum model likelihood value

ml_n_release_sitesFLOAT

Maximum likelihood value for n_release_sites

ml_base_release_probabilityFLOAT

Maximum likelihood value for base_release_probability

ml_mini_amplitudeFLOAT

Maximum likelihood value for mini_amplitude

ml_mini_amplitude_cvFLOAT

Maximum likelihood value for mini_amplitude_cv

ml_depression_amountFLOAT

Maximum likelihood value for depression_amount

ml_depression_tauFLOAT

Maximum likelihood value for depression_tau

ml_facilitation_amountFLOAT

Maximum likelihood value for facilitation_amount

ml_facilitation_tauFLOAT

Maximum likelihood value for facilitation_tau

ml_measurement_stdevFLOAT

Maximum likelihood value for measurement_stdev

ml_strengthFLOAT

maximum likelihood value of n_release_sites * base_release_probability * mini_amplitude

ml_strength_ciVARCHAR

confidence interval for ml_strength

ml_quanta_per_spikeFLOAT

maximum likelihood value of n_release_sites * base_release_probability

ml_quanta_per_spike_ciVARCHAR

confidence interval for ml_quanta_per_spike

ml_sites_pr_ratioFLOAT

maximum likelihood ratio of n_release_sites : base_release_probability

ml_sites_pr_ratio_ciVARCHAR

confidence interval for ml_sites_pr_ratio

ml_release_dependence_ratioFLOAT

ratio of maximum likelihood in release-dependent vs release-independent portions of the parameter space

ml_paired_pulse_ratio_50hzFLOAT

The median ratio of 2nd / 1st pulse amplitudes for 50Hz pulse trains.

ml_stp_initial_50hzFLOAT

The median relative change from 1st to 2nd pulse for 50Hz pulse trains

ml_stp_initial_50hz_nFLOAT

Number of samples represented in stp_initial_50Hz

ml_stp_initial_50hz_stdFLOAT

Standard deviation of samples represented in stp_initial_50Hz

ml_stp_induction_50hzFLOAT

The median relative change from 1st to 5th-8th pulses for 50Hz pulse trains

ml_stp_induction_50hz_nFLOAT

Number of samples represented in stp_induction_50Hz

ml_stp_induction_50hz_stdFLOAT

Standard deviation of samples represented in stp_induction_50Hz

ml_stp_recovery_250msFLOAT

The median relative change from 1st-4th to 9th-12th pulses for pulse trains with a 250 ms recovery period

ml_stp_recovery_250ms_nFLOAT

Number of samples represented in stp_recovery_250ms

ml_stp_recovery_250ms_stdFLOAT

Standard deviation of samples represented in stp_recovery_250ms

ml_stp_recovery_single_250msFLOAT

The median relative change from 1st to 9th pulses for pulse trains with a 250 ms recovery period

ml_stp_recovery_single_250ms_nFLOAT

Number of samples represented in stp_recovery_single_250ms

ml_stp_recovery_single_250ms_stdFLOAT

Standard deviation of samples represented in stp_recovery_single_250ms

ml_pulse_amp_90th_percentileFLOAT

The 90th-percentile largest pulse amplitude, used to normalize change values in this table

ml_noise_amp_90th_percentileFLOAT

The 90th-percentile largest amplitude measured from background noise, used for comparison to pulse_amp_90th_percentile

ml_noise_stdFLOAT

Standard deviation of PSP amplitudes measured from background noise

ml_variability_resting_stateFLOAT

Variability of PSP amplitudes only from events with no preceding spikes for at least 8 seconds, corrected for background noise.

ml_variability_second_pulse_50hzFLOAT

Variability of PSP amplitudes in 2nd pulses of 50Hz trains

ml_variability_stp_induced_state_50hzFLOAT

Variability of PSP amplitudes in 5th-8th pulses of 50Hz trains

ml_variability_change_initial_50hzFLOAT

Difference between variability of 1st and 2nd pulses in 50Hz trains, corrected for background noise.

ml_variability_change_induction_50hzFLOAT

Difference between variability of 1st and 5th-8th pulses in 50Hz trains, corrected for background noise.

ml_paired_event_correlation_1_2_rFLOAT

Pearson correlation coefficient for amplitudes of 1st:2nd pulses in 50Hz trains.

ml_paired_event_correlation_1_2_pFLOAT

Pearson correlation p-value related to paired_event_correlation_1_2_r.

ml_paired_event_correlation_2_4_rFLOAT

Pearson correlation coefficient for amplitudes of 1st:2nd pulses in 50Hz trains.

ml_paired_event_correlation_2_4_pFLOAT

Pearson correlation p-value related to paired_event_correlation_1_2_r.

ml_paired_event_correlation_4_8_rFLOAT

Pearson correlation coefficient for amplitudes of 1st:2nd pulses in 50Hz trains.

ml_paired_event_correlation_4_8_pFLOAT

Pearson correlation p-value related to paired_event_correlation_1_2_r.

metaVARCHAR

Pipeline

class aisynphys.database.schema.Pipeline(**kwargs)

Sqlalchemy model for “Pipeline” database table.

Stores information about which pipeline analysis jobs were run, when, and whether there was an error.

Attributes

idINTEGER

module_nameVARCHAR

The name of the pipeline module that generated this result

job_idVARCHAR

Unique value identifying the job that was processed

finish_timeDATETIME

The date/time when this job completed processing

successBOOLEAN

Whether the job completed successfully

errorVARCHAR

Error or warning messages generated during job processing

metaVARCHAR