Scientific References
This page collects all scientific publications, methods, and software libraries that Synaptipy implements or builds upon. References are grouped by topic. Each entry identifies the specific module or section where it is used.
For full mathematical derivations see Algorithmic Definitions.
Action Potential Detection and Kinetics
Bean, B. P. (2007). The action potential in mammalian central neurons. Nature Reviews Neuroscience, 8(6), 451-465. doi:10.1038/nrn2148
Default dV/dt threshold (20 V/s) for spike onset detection in
single_spike.py; reference for cortical pyramidal neuron AP kinetics.
Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, 117(4), 500-544. doi:10.1113/jphysiol.1952.sp004764
Foundational AP model. Basis for dV/dt-threshold detection, Na⁺ channel inactivation, and the separation of fast/medium AHP windows (
single_spike.py,firing_dynamics.py).
Naundorf, B., Wolf, F., & Volgushev, M. (2006). Unique features of action potential initiation in cortical neurons. Nature, 440(7087), 1060-1063. doi:10.1038/nature04610
Artifact ceiling constant 300 V/s in
single_spike.py— above this rate the rising phase is flagged as non-physiological.
Sekerli, M., Del Negro, C. A., Lee, R. H., & Bhatt, D. L. (2004). Estimating action potential thresholds from neuronal time-series: new metrics and evaluation of methodologies. IEEE Transactions on Biomedical Engineering, 51(9), 1665-1672. doi:10.1109/TBME.2004.827827
Maximum-curvature (d²V/dt²) AP threshold method in
single_spike.py§6.2 and §15.2.
Henze, D. A., & Buzsaki, G. (2001). Action potential threshold of hippocampal pyramidal cells in vivo is increased by recent spiking activity. Neuroscience, 105(1), 121-130. doi:10.1016/S0306-4522(01)00167-1
Motivation for per-spike dynamic threshold to accommodate Na⁺ channel inactivation across a spike train (
single_spike.py§15.2).
After-Hyperpolarisation (AHP)
Storm, J. F. (1987). Action potential repolarization and a fast after-hyperpolarization in rat hippocampal pyramidal cells. Journal of Physiology, 385, 733-759. doi:10.1113/jphysiol.1987.sp016517
Fast AHP window (1-5 ms) calibration — BK/Kv3 channel kinetics (
single_spike.py).
Sah, P., & Faber, E. S. L. (2002). Channels underlying neuronal calcium-activated potassium currents. Progress in Neurobiology, 66(5), 345-353. doi:10.1016/S0301-0082(02)00004-7
Medium AHP window (10-50 ms) calibration — SK/IK Ca²⁺-activated K⁺ channels (
single_spike.py).
Passive Membrane Properties
Hamill, O. P., Marty, A., Neher, E., Sakmann, B., & Sigworth, F. J. (1981). Improved patch-clamp techniques for high-resolution current recording from cells and cell-free membrane patches. Pflugers Archiv, 391(2), 85-100. doi:10.1007/BF00656997
Series-resistance measurement and whole-cell capacitance estimation (
passive_properties.py§5.2 and §15.1). Seminal paper describing the whole-cell configuration of the patch-clamp technique.
Neher, E., & Sakmann, B. (1976). Single-channel currents recorded from membrane of denervated frog muscle fibres. Nature, 260(5554), 799-802. doi:10.1038/260799a0
Foundation for the patch-clamp method; provides the biophysical basis for passive-property analysis in
passive_properties.py.
Robinson, R. B., & Siegelbaum, S. A. (2003). Hyperpolarization-activated cation currents: From molecules to physiological function. Annual Review of Physiology, 65, 453-480. doi:10.1146/annurev.physiol.65.092101.142734
HCN channel physiology — basis for peak vs. steady-state Rᵢₙ distinction (§2.2) and sag ratio interpretation (§4,
passive_properties.py).
Electrode Corrections
Barry, P. H., & Lynch, J. W. (1991). Liquid junction potentials and small cell effects in patch-clamp analysis. Journal of Membrane Biology, 121(2), 101-117. doi:10.1007/BF01870526
Liquid Junction Potential correction procedure — §16 Step A,
processing_pipeline.py.
Neher, E. (1992). Correction for liquid junction potentials in patch clamp experiments. Methods in Enzymology, 207, 123-131. doi:10.1016/0076-6879(92)07008-C
Accepted standard reference for LJP correction in whole-cell patch-clamp (
processing_pipeline.py).
Armstrong, C. M., & Bezanilla, F. (1977). Inactivation of the sodium channel. II. Gating current experiments. Journal of General Physiology, 70(5), 567-590. doi:10.1085/jgp.70.5.567
Original P/N subtraction protocol — §16 Step B,
processing_pipeline.py.
Bezanilla, F., & Armstrong, C. M. (1977). Inactivation of the sodium channel. I. Sodium current experiments. Journal of General Physiology, 70(5), 549-566. doi:10.1085/jgp.70.5.549
Companion paper establishing P/N leak subtraction (
processing_pipeline.py).
Signal Processing
Savitzky, A., & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8), 1627-1639. doi:10.1021/ac60214a047
Savitzky-Golay filter used for AHP waveform smoothing (§6.7) and dV/dt computation (§6.9, §15.2) in
single_spike.py.
Butterworth, S. (1930). On the Theory of Filter Amplifiers. Wireless Engineer, 7, 536-541. (Original publication; no formal DOI.)
Butterworth IIR filter design — basis for all lowpass, highpass, and bandpass filters in §14.1 (
signal_processor.py), applied zero-phase viascipy.signal.sosfiltfilt.
Welch, P. D. (1967). The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. IEEE Transactions on Audio and Electroacoustics, 15(2), 70-73. doi:10.1109/TAU.1967.1161901
Welch’s modified periodogram method for PSD estimation — §14.4 line noise detection and
compute_psd()insignal_processor.pyviascipy.signal.welch.
Robust Noise Estimation
Hampel, F. R. (1974). The influence curve and its role in robust estimation. Journal of the American Statistical Association, 69(346), 383-393. doi:10.1080/01621459.1974.10482962
Median Absolute Deviation (MAD) with 1.4826 consistency factor for Gaussian-equivalent standard deviation; used in all three event-detection methods (§7.1, §7.2, §7.3,
synaptic_events.py).
Rousseeuw, P. J., & Croux, C. (1993). Alternatives to the median absolute deviation. Journal of the American Statistical Association, 88(424), 1273-1283. doi:10.1080/01621459.1993.10476408
Robustness properties and 50% breakdown point of the MAD scale estimator; justification for use in contaminated electrophysiology traces (
synaptic_events.py).
Synaptic Event Detection
Rall, W. (1967). Distinguishing theoretical synaptic potentials computed for different soma-dendritic distributions of synaptic input. Journal of Neurophysiology, 30(5), 1138-1168. doi:10.1152/jn.1967.30.5.1138
Cable-theory prediction of 2-3x kinetic slowing for distal dendritic inputs; justification for three-kernel template bank in §7.2 and §15.4 (
synaptic_events.py).
Major, G., Larkman, A. U., Jonas, P., Sakmann, B., & Jack, J. J. B. (1994). Detailed passive cable models of whole-cell recorded CA3 pyramidal neurons in rat hippocampal slices. Journal of Neuroscience, 14(8), 4613-4638. doi:10.1523/JNEUROSCI.14-08-04613.1994
Quantitative validation of dendritic filtering (2-3x tau slowing) underlying the template bank in §15.4 (
synaptic_events.py).
Paired-Pulse Ratio and Short-Term Plasticity
Zucker, R. S., & Regehr, W. G. (2002). Short-term synaptic plasticity. Annual Review of Physiology, 64, 355-405. doi:10.1146/annurev.physiol.64.092501.114547
PPR R2 baseline correction methodology (§15.5); referencing R2 amplitude to the pre-stimulus resting baseline (
evoked_responses.py).
Regehr, W. G. (2012). Short-term presynaptic plasticity. Cold Spring Harbor Perspectives in Biology, 4(7), a005702. doi:10.1101/cshperspect.a005702
Conceptual framework for PPR interpretation — facilitation (PPR > 1) and depression (PPR < 1) classification (
evoked_responses.py).
Spike-Train Statistics
Holt, G. R., Softky, W. R., Koch, C., & Douglas, R. J. (1996). Comparison of discharge variability in vitro and in vivo in cat visual cortex neurons. Journal of Neurophysiology, 75(5), 1806-1814. doi:10.1152/jn.1996.75.5.1806
CV and CV₂ computation (
firing_dynamics.py§12).
Shinomoto, S., Shima, K., & Tanji, J. (2003). Differences in spiking patterns among cortical neurons. Neural Computation, 15(12), 2823-2842. doi:10.1162/089976603322518759
Local Variation (LV) metric (
firing_dynamics.py§12).
Burst Detection
Grace, A. A., & Bunney, B. S. (1984). The control of firing pattern in nigral dopamine neurons: burst firing. Journal of Neuroscience, 4(11), 2877-2890. doi:10.1523/JNEUROSCI.04-11-02877.1984
Original ISI-criterion burst detection; adapted for cortical neurons in §9 (
firing_dynamics.py).
Harris, K. D., Hirase, H., Leinekugel, X., Henze, D. A., & Buzsáki, G. (2001). Temporal interaction between single spikes and complex spike bursts in hippocampal pyramidal cells. Neuron, 32(1), 141-149. doi:10.1016/S0896-6273(01)00447-0
Dynamic ISI fraction (30% of mean ISI) for burst detection in §9 (
firing_dynamics.py).
Data Standards
Garcia, S., Guarino, D., Jaillet, F., et al. (2014). Neo: an object model for handling electrophysiology data in multiple formats. Frontiers in Neuroinformatics, 8, 10. doi:10.3389/fninf.2014.00010
Electrophysiology I/O layer — all file format reading (ABF, WinWCP, CED, Intan, Igor, NWB, Open Ephys, and 30+ more) via
neoininfrastructure/.
Rubel, O., Tritt, A., Ly, R., et al. (2022). The Neurodata Without Borders ecosystem for neurophysiological data science. eLife, 11:e78362. doi:10.7554/eLife.78362
NWB 2.x export and FAIR metadata compliance;
CurrentClampSeries,VoltageClampSeries,IntracellularRecordingsTable,ProcessingModulecontainers (infrastructure/nwb_exporter.py).
Scientific Python Ecosystem
Virtanen, P., Gommers, R., Oliphant, T. E., et al. (2020). SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods, 17, 261-272. doi:10.1038/s41592-019-0686-2
scipy.optimize.curve_fit— tau (§3), capacitance (§5.2, §15.1), PPR decay fitting (§15.5, §15.6)scipy.signal.sosfiltfilt/butter/iirnotch— all digital filters (§14.1)scipy.signal.welch— PSD / line noise assessment (§14.4)scipy.signal.savgol_filter— AHP smoothing (§6.7)scipy.signal.detrend— baseline detrending (§14.2, §15.7)scipy.signal.find_peaks— event detection (§7.1)scipy.stats.linregress— I-V regression (§8), F-I slope (§10), drift (§1)scipy.stats.median_abs_deviation— MAD noise floor (§7)scipy.integrate.trapezoid— capacitive charge integration (§5.2)
Harris, C. R., Millman, K. J., van der Walt, S. J., et al. (2020). Array programming with NumPy. Nature, 585, 357-362. doi:10.1038/s41586-020-2649-2
All numerical array operations:
numpy.gradient(dV/dt),numpy.diff(ISI),numpy.trapezoid(charge),numpy.nanmean(robust mean for CV₂/LV).
McKinney, W. (2010). Data structures for statistical computing in Python. Proceedings of the 9th Python in Science Conference (SciPy 2010), 51-56. doi:10.25080/Majora-92bf1922-00a
pandas.DataFrame— batch-engine CSV output, wide and long format tables compatible with Python, R, and MATLAB downstream workflows.
Visualization
Wong, B. (2011). Points of view: Color blindness. Nature Methods, 8(6), 441. doi:10.1038/nmeth.1618
Colorblind-safe palette for all Synaptipy plot colors (
application/gui/).
How to Cite Synaptipy
If you use Synaptipy in published research, please cite the software directly
using the metadata in CITATION.cff:
Shahul, A. K. (2026). SynaptiPy: An Open-Source Electrophysiology
Visualization and Analysis Suite (v0.1.3b7).
https://github.com/anzalks/synaptipy
In addition, please consider citing the upstream libraries your analysis depends on (Neo, PyNWB, SciPy, NumPy, pandas) as listed in the Dependencies and Citations section of the README.