Annotated bibliography

The Concepts pages cite these works inline (each citation links straight to PubMed or the publisher). This page collects the full references with a one-line note on why it matters for users of nSTAT. PMIDs were verified against PubMed; classic engineering/statistics works that predate PubMed indexing are listed without a PMID.

Microelectrode recordings, spikes, and the LFP

  • Buzsáki G, Anastassiou CA, Koch C (2012). The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes. Nature Reviews Neuroscience 13:407–420. PMID 22595786 · doi:10.1038/nrn3241. The canonical explanation of what a microelectrode actually measures — why fast transients are spikes and the low-frequency remainder is the LFP.

  • Einevoll GT, Kayser C, Logothetis NK, Panzeri S (2013). Modelling and analysis of local field potentials for studying the function of cortical circuits. Nature Reviews Neuroscience 14:770–785. PMID 24135696 · doi:10.1038/nrn3599. What the LFP reflects and how to interpret it — background for the spectral tools in nSTAT.

  • Pesaran B, Vinck M, Einevoll GT, et al. (2018). Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation. Nature Neuroscience 21:903–919. PMID 29942039 · doi:10.1038/s41593-018-0171-8. Modern practical guide to field-potential analysis, including the pitfalls of spectral estimation that multitaper methods address.

  • Stevenson IH, Kording KP (2011). How advances in neural recording affect data analysis. Nature Neuroscience 14:139–142. PMID 21270781 · doi:10.1038/nn.2731. Why population-scale recordings demand statistical models like the point-process GLMs at the heart of nSTAT.

Spike sorting

  • Lewicki MS (1998). A review of methods for spike sorting: the detection and classification of neural action potentials. Network: Computation in Neural Systems 9:R53–R78. PMID 10221571 · doi:10.1088/0954-898X_9_4_001. Foundational review of the spike-sorting problem nSTAT assumes is already solved (it works on sorted spike trains).

  • Harris KD, Henze DA, Csicsvari J, Hirase H, Buzsáki G (2000). Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. Journal of Neurophysiology 84:401–414. PMID 10899214 · doi:10.1152/jn.2000.84.1.401. Quantifies how imperfect spike sorting is — motivation for clusterless decoding (see below).

  • Quian Quiroga R, Nadasdy Z, Ben-Shaul Y (2004). Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Computation 16:1661–1687. PMID 15228749 · doi:10.1162/089976604774201631. A widely used spike-sorting algorithm; good background on spike features.

Point processes, GLMs, and goodness-of-fit

  • Truccolo W, Eden UT, Fellows MR, Donoghue JP, Brown EN (2005). A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. Journal of Neurophysiology 93:1074–1089. PMID 15356183 · doi:10.1152/jn.00697.2004. The point-process-GLM framework nSTAT implements: stimulus + history + ensemble terms in one conditional intensity function.

  • Paninski L (2004). Maximum likelihood estimation of cascade point-process neural encoding models. Network: Computation in Neural Systems 15:243–262. PMID 15600233 · doi:10.1088/0954-898X_15_4_002. Why the log-likelihood of a point-process GLM is concave — the reason GLM fitting in nSTAT converges reliably.

  • Brown EN, Barbieri R, Ventura V, Kass RE, Frank LM (2002). The time-rescaling theorem and its application to neural spike train data analysis. Neural Computation 14:325–346. PMID 11802915 · doi:10.1162/08997660252741149. The theorem behind FitResult.computeKSStats — how to turn a fitted CIF into a Kolmogorov–Smirnov goodness-of-fit test.

  • Tao L, Weber KM, Arai K, Eden UT (2018). A common goodness-of-fit framework for neural population models using marked point process time-rescaling. Journal of Computational Neuroscience 45:147–162. PMID 30298220 · doi:10.1007/s10827-018-0698-4. The multivariate population goodness-of-fit implemented by nstat.population_time_rescale.

  • Lewis PAW, Shedler GS (1979). Simulation of nonhomogeneous Poisson processes by thinning. Naval Research Logistics Quarterly 26:403–413. doi:10.1002/nav.3800260304. The thinning algorithm nSTAT uses to simulate spike trains from a time-varying rate.

State-space models and decoding

  • Smith AC, Brown EN (2003). Estimating a state-space model from point process observations. Neural Computation 15:965–991. PMID 12803953 · doi:10.1162/089976603765202622. The EM algorithm behind the state-space GLM (SSGLM) for across-trial learning dynamics.

  • Eden UT, Frank LM, Barbieri R, Solo V, Brown EN (2004). Dynamic analysis of neural encoding by point process adaptive filtering. Neural Computation 16:971–998. PMID 15070506 · doi:10.1162/089976604773135069. The point-process adaptive filter (PPAF) used for decoding in nSTAT.

  • Zhang K, Ginzburg I, McNaughton BL, Sejnowski TJ (1998). Interpreting neuronal population activity by reconstruction: unified framework with application to hippocampal place cells. Journal of Neurophysiology 79:1017–1044. PMID 9463459 · doi:10.1152/jn.1998.79.2.1017. The Bayesian population-reconstruction decoder used in the place-cell walkthrough (examples/tutorials/place_cell_walkthrough.py).

  • Brown EN, Frank LM, Tang D, Quirk MC, Wilson MA (1998). A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells. Journal of Neuroscience 18:7411–7425. PMID 9736661 · doi:10.1523/JNEUROSCI.18-18-07411.1998. Foundational decoding of position from place-cell ensembles; the dataset family behind the place-cell walkthrough.

  • Denovellis EL, Gillespie AK, Coulter ME, et al. (2021). Hippocampal replay of experience at real-world speeds. eLife 10:e64505. PMID 34570699 · doi:10.7554/eLife.64505. The clusterless state-space decoder bridged by nstat.extras.decoding.clusterless_bridge.

Spectral estimation

  • Thomson DJ (1982). Spectrum estimation and harmonic analysis. Proceedings of the IEEE 70:1055–1096. doi:10.1109/PROC.1982.12433. The original multitaper (Slepian/DPSS) spectral estimator implemented by SignalObj.MTMspectrum / spectrogram.

  • Mitra PP, Pesaran B (1999). Analysis of dynamic brain imaging data. Biophysical Journal 76:691–708. PMID 9929474 · doi:10.1016/S0006-3495(99)77236-X. Brought multitaper methods to neuroscience; the practical reference for spectrograms of LFP/EEG.

Clinical microelectrode recordings, rhythmic cells, and adaptive DBS

  • Hutchison WD, Allan RJ, Opitz H, Levy R, Dostrovsky JO, Lang AE, Lozano AM (1998). Neurophysiological identification of the subthalamic nucleus in surgery for Parkinson’s disease. Annals of Neurology 44:622–628. PMID 9778260 · doi:10.1002/ana.410440407. The intraoperative-mapping signatures — firing rate and burstiness by nucleus, tremor cells — that frame “where is the electrode?” as the state-estimation problem in the clinical-microelectrode page.

  • Levy R, Hutchison WD, Lozano AM, Dostrovsky JO (2000). High-frequency synchronization of neuronal activity in the subthalamic nucleus of parkinsonian patients with limb tremor. Journal of Neuroscience 20:7766–7775. PMID 11027240 · doi:10.1523/JNEUROSCI.20-20-07766.2000. Tremor cells phase-locked to limb tremor — the canonical rhythmic (oscillatory) cell modeled as a point-process GLM with a periodic covariate.

  • Levy R, Ashby P, Hutchison WD, Lang AE, Lozano AM, Dostrovsky JO (2002). Dependence of subthalamic nucleus oscillations on movement and dopamine in Parkinson’s disease. Brain 125:1196–1209. PMID 12023310 · doi:10.1093/brain/awf128. How tremor- and beta-band oscillations in the STN are modulated by movement and medication — context for the field-potential biomarker.

  • Zaidel A, Spivak A, Grieb B, Bergman H, Israel Z (2010). Subthalamic span of beta oscillations predicts deep brain stimulation efficacy for patients with Parkinson’s disease. Brain 133:2007–2021. PMID 20534648 · doi:10.1093/brain/awq144. The spatial extent of the dorsolateral beta-oscillatory region predicts DBS outcome — why the descent’s spectral profile (a SignalObj.MTMspectrum per depth) is clinically load-bearing.

  • Little S, Pogosyan A, Neal S, et al. (2013). Adaptive deep brain stimulation in advanced Parkinson disease. Annals of Neurology 74:449–457. PMID 23852650 · doi:10.1002/ana.23951. Closed-loop DBS driven by beta-band feedback — a decode-then-actuate loop, the clinical destination of the encoding/decoding tools in nSTAT.

  • Tinkhauser G, Pogosyan A, Little S, Beudel M, Herz DM, Tan H, Brown P (2017). The modulatory effect of adaptive deep brain stimulation on beta bursts in Parkinson’s disease. Brain 140:1053–1067. PMID 28334851 · doi:10.1093/brain/awx010. Beta arrives in bursts whose duration tracks motor state — motivation for the time-resolved SignalObj.spectrogram over a single spectrum.

The toolbox

  • Cajigas I, Malik WQ, Brown EN (2012). nSTAT: Open-source neural spike train analysis toolbox for Matlab. Journal of Neuroscience Methods 211:245–264. PMID 22981419 · doi:10.1016/j.jneumeth.2012.08.009. The toolbox paper. Cite this if you use nSTAT in your work.

  • Daley DJ, Vere-Jones D (2003). An Introduction to the Theory of Point Processes, Vol. I: Elementary Theory and Methods (2nd ed.). Springer. doi:10.1007/b97277. The mathematical reference for point processes and conditional intensity functions.