docs: update repository links and revise paper materials

README.md

@@ -39,7 +39,7 @@ pip install pygpla
 ### From source
 
 ```bash
-git clone https://github.com/CMC-lab/PyGPLA.git
+git clone https://github.com/CMC-unit/PyGPLA.git
 cd PyGPLA
 pip install -e .
 ```

docs/contributing.md

@@ -43,7 +43,7 @@ If it’s a larger change, opening an issue first is usually better than startin
 ### Clone + environment
 
 ```bash
-git clone https://github.com/CMC-lab/PyGPLA.git
+git clone https://github.com/CMC-unit/PyGPLA.git
 cd PyGPLA
 python -m venv .venv
 source .venv/bin/activate

docs/installation.md

@@ -42,7 +42,7 @@ pip install pygpla
 ### From source
 
 ```bash
-git clone https://github.com/CMC-lab/PyGPLA.git
+git clone https://github.com/CMC-unit/PyGPLA.git
 cd PyGPLA
 pip install -e .
 ```

paper/package_organization_revised.md

@@ -0,0 +1,7 @@
+# Package organization
+
+PyGPLA follows a function-oriented, layered design that mirrors the GPLA workflow, from preparation of the input data to statistical assessment. The main entry point is the high-level `pygpla.api.gpla` function, which coordinates preprocessing, coupling estimation, singular-value decomposition, and optional significance testing in one call. This keeps the default workflow compact while preserving access to lower-level components for advanced analyses, methodological modifications, and extensions.
+
+Core numerical routines are separated by responsibility. The `pygpla.preprocessing` module handles trial concatenation, temporal and unit selection, spike-count filtering, optional LFP normalization, and the invocation of optional whitening. The `pygpla.core` package implements whitening, coupling-matrix construction, and singular-value decomposition, including post-processing operations such as phase alignment, spike-vector normalization, and unwhitening-aware handling of LFP vectors. Statistical procedures are collected in `pygpla.stats`, which provides surrogate-based testing using multiple spike-jittering schemes [e.g., @grun2009data] and an analytical test based on the Marchenko--Pastur random-matrix-theory threshold [@safaviUnivariateMultivariateCoupling2021].
+
+To support reproducibility and method validation, PyGPLA includes dedicated simulation utilities in `pygpla.simulations`. These include homogeneous and inhomogeneous Poisson generators, phase-locked spike simulations, and a transient-coupling simulator that generates paired spike and LFP signals. The repository additionally provides a figure-reproduction and visualization script and an automated test suite for validating individual numerical components and end-to-end behavior. Finally, the configuration dataclasses in `pygpla.config` provide structured parameter containers for preprocessing, whitening, and statistical testing.

paper/package_organization_revised.tex

@@ -0,0 +1,30 @@
+\subsection{Package organization}
+
+PyGPLA follows a function-oriented, layered design that mirrors the GPLA workflow,
+from preparation of the input data to statistical assessment. The main entry point is
+the high-level \texttt{pygpla.api.gpla} function, which coordinates preprocessing,
+coupling estimation, singular-value decomposition, and optional significance testing
+in one call. This keeps the default workflow compact while preserving access to
+lower-level components for advanced analyses, methodological modifications, and
+extensions.
+
+Core numerical routines are separated by responsibility. The
+\texttt{pygpla.preprocessing} module handles trial concatenation, temporal and unit
+selection, spike-count filtering, optional LFP normalization, and the invocation of
+optional whitening. The \texttt{pygpla.core} package implements whitening,
+coupling-matrix construction, and singular-value decomposition, including
+post-processing operations such as phase alignment, spike-vector normalization, and
+unwhitening-aware handling of LFP vectors. Statistical procedures are collected in
+\texttt{pygpla.stats}, which provides surrogate-based testing using multiple
+spike-jittering schemes \citep[e.g., multiple jittering schemes; also, see][]{grun2009data}
+and an analytical test based on the Marchenko--Pastur random matrix theory threshold
+\citep{safaviUnivariateMultivariateCoupling2021}.
+
+To support reproducibility and method validation, PyGPLA includes dedicated simulation
+utilities in \texttt{pygpla.simulations}. These include homogeneous and inhomogeneous
+Poisson generators, phase-locked spike simulations, and a transient-coupling simulator
+that generates paired spike and LFP signals. The repository additionally provides a
+figure-reproduction and visualization script and an automated test suite for validating
+individual numerical components and end-to-end behavior. Finally, the configuration
+dataclasses in \texttt{pygpla.config} provide structured parameter containers for
+preprocessing, whitening, and statistical testing.

paper/paper.md

@@ -36,7 +36,7 @@ affiliations:
 
 date: "22 March 2026"
 bibliography: paper.bib
-repository: "https://github.com/CMC-lab/PyGPLA"
+repository: "https://github.com/CMC-unit/PyGPLA"
 crossref: true
 url: "https://pygpla.readthedocs.io/en/latest/"
 
@@ -46,7 +46,6 @@ url: "https://pygpla.readthedocs.io/en/latest/"
 
 PyGPLA is a Python implementation of Generalized Phase Locking Analysis (GPLA) for multivariate analysis of coupling between spikes and local field potentials (LFPs) [@safavi2023uncovering]. For a given frequency, GPLA constructs a complex coupling matrix $\hat{C}(f) \in \mathbb{C}^{N_c \times N_u}$ between LFP channels ($N_c$) and spike units ($N_u$), then applies singular value decomposition (SVD) to reduce the dimensionality of data. The leading singular value summarizes population-level coupling strength, while the corresponding singular vectors describe dominant LFP and spike coupling modes. PyGPLA accepts a user-provided frequency-specific analytic LFP signal or phase representation and provides data selection, optional whitening and normalization, coupling-matrix construction, SVD-based decomposition, and statistical significance testing [@safavi2021univariate].
 
-
 ## Statement of need
 
 Neural recordings are becoming increasingly high-dimensional and multimodal, demanding more sophisticated analysis tools. Simultaneous analysis of spiking activity and LFPs is among the most informative multi-modal approaches in systems neuroscience, providing insight into the multi-scale mechanisms underlying cognitive functions such as attention and memory [@buzsaki2012origin; @einevoll2013modelling; @herreras2016local]. LFP oscillatory activity partly reflects subthreshold processes shared by neuronal ensembles, and the synchronization between this activity and spiking is hypothesized to coordinate neural populations during cognitive processes [@buzsaki2012origin; @hagen2016hybrid].
@@ -100,11 +99,29 @@ The GPLA methodology has been evaluated in published simulations, biophysical ne
 
 ## AI usage disclosure
 
-Generative AI tools were used only to assist with drafting documentation and
-refactoring code for style. All AI-assisted output was reviewed, tested, and
-validated by the authors, who take full responsibility for the correctness of the
-software and the content of this paper. The scientific method, algorithmic design,
-and numerical implementation of GPLA were carried out by the authors.
+Over the course of PyGPLA’s development, the authors used OpenAI GPT-family
+models through ChatGPT and OpenAI Codex, including models from the GPT-5
+family. This assistance occurred over an extended period during which the
+available models were updated; consequently, exact model snapshots and version
+identifiers were not recorded for every interaction.
+
+The models were used to assist with porting selected MATLAB scripts to Python,
+refactoring code, drafting and restructuring documentation, and conducting
+language editing and grammatical review of the manuscript. AI-generated outputs
+were treated as preliminary suggestions rather than authoritative
+implementations.
+
+All AI-assisted code and text were reviewed and edited by the authors.
+Translated code was reviewed against the original MATLAB implementation and
+the published mathematical description of GPLA. Its behavior was evaluated
+through tests, reproducible simulations, and manual inspection. As an
+additional human-led validation step, the authors reproduced Figure 2 from the
+original GPLA publication using PyGPLA and compared the resulting coupling
+patterns with the published results, providing a further check of the ported
+implementation. The authors made the scientific, architectural, and
+methodological decisions and take full responsibility for the accuracy,
+originality, licensing, and integrity of the software, documentation, and
+paper.
 
 ## Acknowledgments
 

pyproject.toml

@@ -47,8 +47,8 @@ figures = [
 ]
 
 [project.urls]
-Repository = "https://github.com/CMC-lab/PyGPLA"
-Documentation = "https://github.com/CMC-lab/PyGPLA#readme"
+Repository = "https://github.com/CMC-unit/PyGPLA"
+Documentation = "https://github.com/CMC-unit/PyGPLA#readme"
 
 [tool.hatch.version]
 path = "src/pygpla/_version.py"