Fix fabricated citations + add Transformer ref & RTD API links

deepSTRF/datasets/audio/crcns_aa1.py

@@ -133,8 +133,8 @@ class CRCNSAA1Dataset(AudioNeuralDataset):
     Woolley et al. (2005). "Tuning for Spectro-temporal Modulations: a
     Mechanism for Auditory Discrimination of Natural Sound."
 
-    Hsu et al. (2004). "Modulation and phase spectrum of natural sounds
-    enhance neural discrimination performed by single auditory neurons."
+    Hsu et al. (2004). "Modulation power and phase spectrum of natural
+    sounds enhance neural encoding performed by single auditory neurons."
 
     Singh & Theunissen (2003). "Modulation spectra of natural sounds and
     ethological theories of auditory processing."

deepSTRF/datasets/audio/ns1.py

@@ -30,7 +30,7 @@
 
 # The precomputed mel-spectrogram tensor (X_nfht: S=20, F=34, hopdim=1, T=999)
 # used by the original Harper/Rahman analyses is NOT on OSF, but it IS in the
-# DNet companion repo (Rahman et al. 2018 PLoS Comp Biol, doi: 10.1371/
+# DNet companion repo (Rahman et al. 2019 PLoS Comp Biol, doi: 10.1371/
 # journal.pcbi.1006618 — github.com/monzilur/DNet). The 5 ms version is
 # ``test_data_5ms.mat`` (5.2 MB); the 1 ms version (``test_data.mat``, 52 MB)
 # is also there but we don't use it. The same file also contains a ``y_nt``
@@ -93,7 +93,7 @@ def download_ns1(dest: Optional[str] = None) -> str:
       per-neuron metadata (.mat), and the spike + wav zip (~155 MB total).
     - **DNet GitHub** (https://github.com/monzilur/DNet, master branch): the
       precomputed 5 ms mel-spectrogram tensor ``test_data_5ms.mat``
-      (5.2 MB) accompanying Rahman et al. 2018 PLoS Comp Biol. NOT on OSF.
+      (5.2 MB) accompanying Rahman et al. 2019 PLoS Comp Biol. NOT on OSF.
 
     Idempotent: skips files that already exist; returns the destination path.
 

deepSTRF/models/audio/audio_zoo.py

@@ -548,9 +548,10 @@ class Transformer(AudioEncodingModel):
 
     References
     ----------
-    Rançon, Bornschein, King, Schnupp, Willmore (2025). "Temporal
-    recurrence as a general mechanism to explain neural responses in
-    the auditory system." Comm. Bio. (preprint on BioRxiv).
+    Rançon, Masquelier & Cottereau (2025). "Temporal recurrence as a
+    general mechanism to explain neural responses in the auditory
+    system." Communications Biology 8:1456.
+    https://doi.org/10.1038/s42003-025-08858-3
 
     Vaswani et al. (2017). "Attention Is All You Need." NeurIPS.
 
@@ -710,9 +711,10 @@ class StateNet(AudioEncodingModel):
 
     References
     ----------
-    Rançon, Bornschein, King, Schnupp, Willmore (2025).
-    "Temporal recurrence as a general mechanism to explain neural
-    responses in the auditory system." Comm. Bio. (preprint on BioRxiv).
+    Rançon, Masquelier & Cottereau (2025). "Temporal recurrence as a
+    general mechanism to explain neural responses in the auditory
+    system." Communications Biology 8:1456.
+    https://doi.org/10.1038/s42003-025-08858-3
 
     Notes
     -----

deepSTRF/models/prefiltering.py

@@ -259,10 +259,10 @@ class AdapTrans(nn.Module):
 
     References
     ----------
-    Rançon, Bornschein, King, Schnupp, Willmore (2024). "A general
-    theoretical framework unifying the adaptive, transient and
-    sustained properties of ON and OFF auditory responses." BioRxiv.
-    https://doi.org/10.1101/2024.01.17.576002
+    Rançon, Masquelier & Cottereau (2024). "A general model unifying
+    the adaptive, transient and sustained properties of ON and OFF
+    auditory neural responses." PLOS Computational Biology
+    20(8):e1012288. https://doi.org/10.1371/journal.pcbi.1012288
 
     Notes
     -----

docs/_source/md/README_models.md

@@ -46,22 +46,22 @@ separable kernels. Similar to AdapTrans, prefiltering, hyperparameters for param
 to the class constructor.*
 
 
-Torch class: `Linear(...)`; Parameterization available
+Torch class: [`Linear`](https://deepstrf.readthedocs.io/en/latest/_source/deepSTRF.models.audio.html#deepSTRF.models.audio.audio_zoo.Linear); Parameterization available
 
 
 ### Linear-Nonlinear (LN)
 
 *Consists of a Linear model, with an added output activation which makes it nonlinear. The latter often takes the form of 
-a sigmoid or parameterized function (see e.g. [Rahman et al.]() or [Willmore et al.]()).*
+a sigmoid or parameterized function (see e.g. Rahman et al. or Willmore et al.).*
 
-Torch class: `LinearNonlinear(...)`; Parameterization available
+Torch class: [`LinearNonlinear`](https://deepstrf.readthedocs.io/en/latest/_source/deepSTRF.models.audio.html#deepSTRF.models.audio.audio_zoo.LinearNonlinear); Parameterization available
 
 
 ### Network Receptive Field (NRF)
 
 *In a nutshell, a LN model with several hidden units.*
 
-Torch class: `NetworkReceptiveField(...)`; Parameterization available; Original paper: [Harper et al. (2016)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005113)
+Torch class: [`NetworkReceptiveField`](https://deepstrf.readthedocs.io/en/latest/_source/deepSTRF.models.audio.html#deepSTRF.models.audio.audio_zoo.NetworkReceptiveField); Parameterization available; Original paper: [Harper et al. (2016)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005113)
 
 
 
@@ -72,7 +72,7 @@ Torch class: `NetworkReceptiveField(...)`; Parameterization available; Original
 *In a nutshell, a NRF model in which hidden and output units follow leaky dynamics (as in LIF spiking neurons, but 
 without spikes), with learnable time constants.*
 
-Torch class: `DNet(...)`; Parameterization available; Original paper: [Rahman et al. (2016)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006618)
+Torch class: [`DNet`](https://deepstrf.readthedocs.io/en/latest/_source/deepSTRF.models.audio.html#deepSTRF.models.audio.audio_zoo.DNet); Parameterization available; Original paper: [Rahman et al. (2019)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006618)
 
 
 
@@ -83,7 +83,7 @@ Torch class: `DNet(...)`; Parameterization available; Original paper: [Rahman et
 kernels do not entirely span all frequencies of the input spectrogram. Fully connected prediction head after a convlutional 
 extraction stage.
 
-Torch class: `ConvNet2D(...)`;  Original paper: [Pennington et al.](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011110)
+Torch class: [`ConvNet2D`](https://deepstrf.readthedocs.io/en/latest/_source/deepSTRF.models.audio.html#deepSTRF.models.audio.audio_zoo.ConvNet2D);  Original paper: [Pennington & David (2023)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011110)
 
 
 ### Recurrent / state-space network (StateNet)
@@ -92,7 +92,7 @@ Torch class: `ConvNet2D(...)`;  Original paper: [Pennington et al.](https://jour
 / S4 / LMU). It captures long-range temporal dependencies through the recurrent
 state and is the strongest model in the zoo on NS1.*
 
-Torch class: `StateNet(...)`; Original paper: [Rançon et al. (2025)](https://doi.org/10.1038/s42003-025-08858-3)
+Torch class: [`StateNet`](https://deepstrf.readthedocs.io/en/latest/_source/deepSTRF.models.audio.html#deepSTRF.models.audio.audio_zoo.StateNet); Original paper: [Rançon et al. (2025)](https://doi.org/10.1038/s42003-025-08858-3)
 
 
 ### Transformer
@@ -101,7 +101,7 @@ Torch class: `StateNet(...)`; Original paper: [Rançon et al. (2025)](https://do
 self-attention mask, so it generalizes to any sequence length. An optional
 finite `context_window` makes attention band-causal.*
 
-Torch class: `Transformer(...)`
+Torch class: [`Transformer`](https://deepstrf.readthedocs.io/en/latest/_source/deepSTRF.models.audio.html#deepSTRF.models.audio.audio_zoo.Transformer); Architecture: [Vaswani et al. (2017)](https://arxiv.org/abs/1706.03762); designed as the attention baseline in [Rançon et al. (2025)](https://doi.org/10.1038/s42003-025-08858-3)
 
 
 ### ICNet
@@ -112,7 +112,7 @@ audio directly (no precomputed spectrogram). Designed for midbrain (IC)
 recordings; it ports cleanly into deepSTRF but is oversized for small cortical
 datasets like NS1.*
 
-Torch class: `ICNet(...)`; Original paper: [Drakopoulos et al. (2025)](https://doi.org/10.1038/s42256-025-01104-9)
+Torch class: [`ICNet`](https://deepstrf.readthedocs.io/en/latest/_source/deepSTRF.models.audio.html#deepSTRF.models.audio.ICNet); Original paper: [Drakopoulos et al. (2025)](https://doi.org/10.1038/s42256-025-01104-9)
 
 ---