{"id":"79af1e06-3d80-44f7-a885-3f9bfa7eaa7e","arxiv_id":"2505.11507","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"A Julia package that bundles model-free and Bayesian estimators for intrinsic neural timescales into a single API.","lead":"IntrinsicTimescales.jl is a Julia toolbox that estimates the brain's internal integration timescales from time-series recordings using autocorrelation, spectral, and Bayesian model-based methods. It matters because researchers studying brain dynamics currently lack one standard, open tool for comparing these estimators.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"No code repository or validation evidence supports the central software claim; reliability of the package is unverified.","rationale":"Agree with the conditional posture, but the most load-bearing gap is not the OU assumption; it is the absence of any demonstration that the software works. The preprint is a software paper; the standard of evidence is a repository, tests, and benchmark. The reader correctly notes this in the rationale, but the formal 'weakest assumption' field names the OU model instead. The OU issue matters for interpretation, not for whether the implementation is correct. We therefore focus the stress test on the missing artifact. If the benchmark in the concrete test passes, the package can be accepted conditionally; if it fails, the central claim collapses. The lack of code is a verification gap rather than a demonstration of error; a concrete benchmark would settle whether the concern lands.","tokens_in":3146,"tokens_out":3397,"duration_ms":34312,"concrete_test":"One decisive check: from the manuscript authors request the Julia package repository and a tagged release with a commit hash, then run a preregistered simulation benchmark. Generate OU/AR(1) ground-truth timescales spanning tau = 0.1, 0.5, 1, 2, 5, and 10 time units, with and without 30% random missing samples and with an added 0.1 Hz oscillation in one arm. Apply every package estimator (ACW-50, ACW-0, ACW-e, AUC, tau, knee, aABC, ADVI) and report bias, root-mean-square error, and 95% credible-interval coverage against the known tau. If any Bayesian estimator shows greater than 10% median bias or coverage below 80%, or any model-free estimator is biased by more than 20%, the central reliability claim does not hold.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim is that IntrinsicTimescales.jl is a working Julia package offering reliable INT estimation, including aABC and ADVI. The only condition needed for that claim to hold is that the code exists, runs, and correctly recovers known timescales. The manuscript provides no Julia repository link, no commit hash, no unit tests, and no simulation-based validation. The sentence 'rigorous testing and documentation' in the Statement of Need is self-report with no supporting artifact. The one URL cited points to a future Python frontend, not to the package itself. Without ground-truth benchmarks, every estimator could be affected by an undetected implementation error: the fast-path ACF via inverse FFT of squared magnitude of the Fourier transform, the Lomb-Scargle periodogram under missingness, or the fitting of exponential and Lorentzian curves. Such errors would invalidate the headline claim of a 'comprehensive, high-performance toolbox'. The Ornstein-Uhlenbeck modeling assumption is a second, interpretive concern, but it is secondary: even if the model is correct, there is no evidence the package estimates its parameters correctly.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"This manuscript announces IntrinsicTimescales.jl, a Julia package designed to estimate intrinsic neural timescales (INTs) from time-series data. The package would implement model-free estimators (ACW-50, ACW-0, ACW-e, AUC, tau, knee) and Bayesian estimators based on an Ornstein-Uhlenbeck process using aABC and ADVI, with specialized handling for missing data and oscillatory artifacts. The paper describes the stated need for such a unified toolbox, lists the package's features, and outlines the documentation structure. No code, repository link, validation results, or usage examples are provided in the manuscript.","tokens_in":3289,"tokens_out":3103,"duration_ms":29980,"significance":"If the package is real, functional, and correct, it would fill a genuine gap: existing INT estimation tools are scattered across languages and often lack uncertainty quantification or missing-data support. A unified, Julia-based toolbox with both model-free and Bayesian estimators, plus posterior predictive checks, would be a useful contribution to the neuroscience community. However, the manuscript as submitted provides no artifact or evidence that the package works, so the significance is prospective rather than demonstrated. The paper does not include machine-checked proofs, reproducible code, parameter-free derivations, or falsifiable predictions; the strengths it claims (high performance, rigorous testing, comprehensive documentation) are assertions only.","major_comments":[{"comment":"The manuscript provides no access to the package artifact: there is no repository URL, DOI, commit hash, or version for IntrinsicTimescales.jl itself. The only URL cited in the text points to INTpy, a future Python frontend. The central claim that the package exists and estimates INTs cannot be verified by any reader. Please provide a permanent, versioned link to the package repository and the specific version described.","section":"Major Features"},{"comment":"The assertion of 'rigorous testing and documentation' is self-reported and unsupported by any evidence in the manuscript. There are no unit tests, no synthetic-data experiments, no ground-truth recovery checks, and no comparisons against existing reference implementations such as Zeraati et al. (2022) or FOOOF. Since the correctness of the estimators is the central claim of a software paper, add a validation section that simulates OU processes and other ground-truth signals with known timescales and reports bias, root-mean-square error, and coverage for every estimator.","section":"Statement of Need"},{"comment":"The description of the knee procedure is ambiguous. It states that a Lorentzian is fitted to the PSD, then subtracted; gaussians for oscillatory peaks are fitted to the residual; then the gaussians are subtracted from the base PSD and a Lorentzian is fitted 'one more time.' This does not match the FOOOF procedure cited (Gao et al., 2020), where the aperiodic component (including the knee) and the Gaussian peaks are estimated jointly within a single model. Specify the exact objective function, the parameters being fitted in each step, and how the final timescale is derived. Provide a worked example with data.","section":"Major Features, knee estimator"},{"comment":"The Bayesian estimators treat the intrinsic timescale as the decay constant of a single Ornstein-Uhlenbeck process, but the manuscript provides no justification that real cortical signals satisfy this generative model. If the model is misspecified, the posterior timescale is a property of the model, not of the brain. At minimum, state this assumption explicitly as a limitation and include posterior predictive checks on surrogate or real data that demonstrate whether the model captures the observed ACF/PSD.","section":"Major Features, Bayesian Parameter Estimation"}],"minor_comments":[{"comment":"The phrase 'time to each 1/e' should be 'time to reach 1/e'.","section":"Major Features, ACW-e bullet"},{"comment":"Figure 1 is referenced in the text but not included or displayed in the manuscript. Ensure the actual figure appears in the final version.","section":"Figure 1"},{"comment":"The sentence 'A diagram shows the major features of the package can be seen in Figure 1' is grammatically awkward; rephrase to something like 'Figure 1 shows the major features of the package.'","section":"Major Features"},{"comment":"The term 'novel techniques' for aABC and ADVI is misleading because both methods are already published (Beaumont et al., 2009; Kucukelbir et al., 2017) and have been applied to OU-based timescale estimation in Zeraati et al. (2022). The novelty is in the implementation, not the methodology.","section":"Abstract / Major Features"},{"comment":"The Documentation section states that all documentation is hosted online but does not provide a URL. State the documentation URL explicitly.","section":"Documentation"},{"comment":"The abstract gives a conceptual definition of INTs as 'the time window in which prior information from an ongoing stimulus can affect the processing of newly arriving information,' but the package's operational definitions (e.g., ACF decay, knee frequency) should be clearly distinguished from this conceptual definition in the main text.","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":"This manuscript is closer to a project announcement than a software paper: it describes a package that is not accessible or validated. The missing code repository and absence of any simulation or benchmark are the main issues; they are fixable within scope, so I recommend major revision rather than rejection. The editor may wish to verify whether the authors have a working repository that was omitted from the submission, and whether the journal requires a functional software artifact for acceptance."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"If you need a quick read: this is a software-description preprint with no software attached. The idea is sensible and the feature list is genuinely useful for the INT community—unified access to ACW variants, AUC, tau, knee, and two Bayesian OU-based estimators (aABC and ADVI) with missing-data handling is a real gap. The novelty is the integration, not the estimators, and the authors are clear about that. The statement of need is well-grounded in the literature, and the citation pattern looks honest.\n\nThe problem is that the central claim—\"IntrinsicTimescales.jl is a high-performance, rigorously tested toolbox\"—is unsupported in the manuscript. There is no repository link, no commit hash, no unit tests, no simulation-based validation, no comparison against existing implementations, and no ground-truth recovery experiment. The only URL points to a future Python frontend. The sentence about \"rigorous testing\" is self-report. For a software paper, that's the load-bearing wall. Several estimators involve nontrivial numerics (inverse-FFT-based ACF, Lomb-Scargle periodogram under missingness, exponential/Lorentzian fitting), and any implementation bug would silently invalidate downstream estimates. The stress-test note is right: even if the Bayesian OU model is exactly what the field wants, we have no evidence the package estimates its parameters correctly.\n\nOn the Bayesian side, the single-OU assumption is introduced without discussion. That's a modeling choice, not a fatal flaw—but it deserves at least a paragraph on when it's appropriate and what it means if the data have multiple timescales or nonstationarity.\n\nWhat's good: the feature list is precise, the methods are described consistently with their cited sources, and the package fills a real practical need. The paper does not overclaim theory; it claims software, and that's exactly what needs proof.\n\nVerdict: this should go to peer review, but only with the strong expectation that the authors provide the repository, a commit hash, and simulation-based validation recovering known timescales, plus some comparison against existing tools. If the code is real and tested, this becomes a practical contribution many labs will use. If not, it's an abstract for a package that may never ship. Recommend conditional acceptance contingent on the artifact and validation. I would not cite it for the package itself until I can run it, but I would cite it as a reference point for the feature landscape.","headline":"A useful software-tool preprint whose central claim—that the package actually works—rests entirely on the authors' word, since no code, repository, or validation is included.","tokens_in":3801,"tokens_out":622,"would_cite":false,"duration_ms":7923,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"IntrinsicTimescales.jl bundles eight ways to estimate neural timescales in one Julia package.","keywords":["intrinsic neural timescales","Julia package","autocorrelation function","power spectral density","Ornstein-Uhlenbeck process","approximate Bayesian computation","variational inference","missing data"],"falsifier":"Take a long Ornstein-Uhlenbeck simulation with known decay time $\\tau$, mask about 30 percent of the samples to imitate data loss, and run both Bayesian estimators; if either posterior fails to concentrate near the true $\\tau$, or the two methods disagree sharply, the package's claim to deliver unbiased timescale estimates would be contradicted.","tokens_in":2939,"feed_emoji":"🧠","tokens_out":18608,"duration_ms":167638,"temperature":0.7,"pith_summary":"IntrinsicTimescales.jl is a Julia package that collects the standard and newer ways of estimating intrinsic neural timescales (INTs), the time window over which a brain region's past activity shapes its response to new input. The central claim is that the package gives researchers one standardized, high-performance tool that computes six model-free INT estimates from the autocorrelation function or power spectrum, plus two Bayesian estimates from an Ornstein-Uhlenbeck process fit to the data. This matters because INTs are increasingly used in cognitive and clinical neuroscience, while existing software is scattered across methods, often lacks uncertainty quantification, and does not cleanly handle missing data or oscillatory artifacts. If the package works as described, a researcher can estimate and compare all these measures on the same recordings, with posterior-predictive checks for the Bayesian routes.","feed_headline":"One Julia package estimates neural timescales eight ways","feed_subtitle":"Autocorrelation, spectral, and Bayesian estimators now share one API with missing-data handling.","key_machinery":"The central object is the package's unified estimation interface, backed by two families of mechanisms. For the model-free methods, the machinery is the autocorrelation function (ACF) and the power spectral density (PSD): the ACF is computed by inverse Fourier transform of the squared Fourier magnitude when the data are complete and by a direct time-domain sum that respects missingness when they are not; the PSD is computed as a windowed periodogram, or with a spectral estimator for unevenly sampled data when samples are missing. The knee estimator relies on the Fourier relation between ACF decay and spectral knee, plus a Lorentzian-with-peaks fitting procedure from reference [6]. For the Bayesian methods, the load-bearing model is the Ornstein-Uhlenbeck process, a mean-reverting random process whose decay constant is taken to be the intrinsic timescale; adaptive approximate Bayesian computation and automatic-differentiation variational inference give two ways to form a posterior over that decay constant, and the generative model itself is what lets the package handle missing data and oscillatory artifacts without discarding recordings.","core_discovery":"The paper's central claim is that IntrinsicTimescales.jl is a complete, modular toolbox for estimating intrinsic neural timescales from time-series data. On the model-free side, it computes the autocorrelation function by inverse Fourier transform of the squared spectrum when data are complete, or directly in the time domain when they are not, and derives from it ACW-50, ACW-0, ACW-e, AUC, and tau; the knee estimator instead fits a Lorentzian to the power spectral density and converts the knee frequency into a decay rate through the Fourier relation between autocorrelation and spectrum, following a three-step peak-cleaning procedure from reference [6]. On the Bayesian side, the package estimates the decay constant of an Ornstein-Uhlenbeck process using adaptive approximate Bayesian computation and automatic-differentiation variational inference, with the generative model extended to cover missing samples and oscillatory artifacts. Plotting utilities and posterior-predictive checks complete the package. The claim is that this fills the gap of a missing standardized tool for INT estimation.","pith_inferences":["Inference: because the package exposes several estimators through one interface, any systematic disagreement among them on real data will become visible; turning that disagreement into a benchmark is a natural next step the paper does not take.","Inference: the two Bayesian estimators should agree on data that genuinely follow an Ornstein-Uhlenbeck process; where they diverge, the divergence points to model misspecification or numerical issues rather than to a brain property.","Inference: the same estimators transfer to non-neural time series, such as climate or financial records, since nothing in the mathematical core is specific to the brain; that application is outside the paper's scope.","Inference: a systematic simulation sweep over recording length, missing fraction, and oscillation strength would map each estimator's bias and could give principled guidance for choosing among the eight methods; the package contains the machinery to run such a sweep."],"forward_implications":["A single script can now produce ACW-50, ACW-0, ACW-e, AUC, tau, knee, and two Bayesian posterior estimates from the same recording, making cross-method comparison routine instead of a software-integration project.","Recordings with missing segments or oscillatory contamination, common in clinical and scalp-EEG data, can still yield timescale estimates with quantified uncertainty rather than being discarded.","Posterior-predictive plots give users a direct visual check of whether the Ornstein-Uhlenbeck model is appropriate for a particular signal before interpreting the estimated timescale.","The paper argues that the Julia implementation keeps Bayesian inference tractable on large neuroimaging datasets where slower interpreted-language tools would be impractical.","A Python frontend is planned, which should make the same estimators accessible to researchers whose pipelines are built outside Julia."],"supporting_citations":[{"why":"defines the temporal receptive window concept that the paper takes as the foundation of intrinsic timescales.","marker":"[1]"},{"why":"supplies the working definition of intrinsic neural timescales as temporal integration and segregation.","marker":"[3]"},{"why":"provides the ACW-50 method, the autocorrelation half-decay estimator implemented by the package.","marker":"[5]"},{"why":"provides the Lorentzian knee-frequency procedure for PSD-based timescale estimation used by the knee method.","marker":"[6]"},{"why":"introduces adaptive approximate Bayesian computation, the inference engine behind the aABC estimator.","marker":"[7]"},{"why":"gives the flexible Bayesian framework for unbiased timescale estimation that the package's OU-based aABC implements.","marker":"[8]"},{"why":"introduces automatic-differentiation variational inference, the alternative Bayesian estimator in the package.","marker":"[9]"},{"why":"defines the tau estimate as the inverse decay rate of an exponential fit to the autocorrelation function.","marker":"[10]"}],"fun_headline_variants":["One Julia package: all intrinsic neural timescale estimators","Estimate neural timescales via ACF, PSD, or Bayesian methods","Julia package estimates neural timescales from any time series","Handle missing data: Julia package for neural timescale estimation","All-in-one Julia package for intrinsic neural timescale estimation"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The Bayesian estimates stand on the assumption that each neural time series behaves like a single Ornstein-Uhlenbeck process, a random walk pulled back toward its baseline, whose decay constant is the intrinsic timescale, and the paper does not test whether real brain signals satisfy that model.","fun_headline_variants_meta":{"raw":{"variants":["One Julia package: all intrinsic neural timescale estimators","Estimate neural timescales via ACF, PSD, or Bayesian methods","Julia package estimates neural timescales from any time series","Handle missing data: Julia package for neural timescale estimation","All-in-one Julia package for intrinsic neural timescale estimation"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000489,"raw_usage":{"total_tokens":2368,"prompt_tokens":869,"completion_tokens":1499,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":485,"completion_tokens_details":{"reasoning_tokens":1414}},"tokens_in":485,"tokens_out":1499,"duration_ms":11805,"temperature":1.0,"reasoning_tokens":1414,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-16T04:23:18.053173+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Take a long Ornstein-Uhlenbeck simulation with known decay time $\\tau$, mask about 30 percent of the samples to imitate data loss, and run both Bayesian estimators; if either posterior fails to concentrate near the true $\\tau$, or the two methods disagree sharply, the package's claim to deliver unbiased timescale estimates would be contradicted.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"defines the temporal receptive window concept that the paper takes as the foundation of intrinsic timescales."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"supplies the working definition of intrinsic neural timescales as temporal integration and segregation."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"provides the ACW-50 method, the autocorrelation half-decay estimator implemented by the package."},{"cited_title":"L., Pfeffer, T","cited_arxiv_id":null,"evidence_quote":"provides the Lorentzian knee-frequency procedure for PSD-based timescale estimation used by the knee method."},{"cited_title":"A., Cornuet, J.-M., Marin, J.-M","cited_arxiv_id":null,"evidence_quote":"introduces adaptive approximate Bayesian computation, the inference engine behind the aABC estimator."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"gives the flexible Bayesian framework for unbiased timescale estimation that the package's OU-based aABC implements."},{"cited_title":"& Blei, D","cited_arxiv_id":null,"evidence_quote":"introduces automatic-differentiation variational inference, the alternative Bayesian estimator in the package."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"defines the tau estimate as the inverse decay rate of an exponential fit to the autocorrelation function."}],"review_version":1}