This dataset contains derived electrophysiological features, trained machine-learning inverse models, test-set predictions, and evaluation metrics generated from large-scale simulations of local LIF cortical circuit models. The resources were produced using the ncpi Python package. The original raw simulation signals are not included in this deposit. Instead, this record contains derived feature matrices, reusable trained inference models, test-set outputs, and model-evaluation summaries. Important note The included features and machine-learning models have not yet been experimentally tested or independently validated. They are provided as research resources for reproducibility, benchmarking, and further methodological development. Simulation datasets Results are provided for three simulation datasets. Hagen_v1 Current-dipole-moment signals generated using the first version of the Hagen neural circuit model. This model uses current-based synapses. The Hagen_v1 dataset comprises approximately 2 million simulations generated by sampling neural circuit parameters within biologically plausible ranges. These ranges were constrained a priori according to biological principles. Model description: https://doi.org/10.1371/journal.pcbi.1010353 Hagen_v2 Current-dipole-moment signals generated using the second version of the Hagen neural circuit model. As in Hagen_v1, this model uses current-based synapses. The Hagen_v2 dataset is a more constrained simulation set, comprising approximately 100,000 simulations. In addition to tighter parameter-range constraints, simulations were further filtered post hoc based on firing rates, spike-based metrics, and power-spectrum properties. Model description: https://doi.org/10.1371/journal.pcbi.1010353 Cavallari Proxy electrophysiological signals generated using the Cavallari neural circuit model. In contrast to the Hagen datasets, the Cavallari model uses conductance-based synapses. Model description: https://doi.org/10.3389/fncir.2014.00012 All signals were processed assuming a sampling frequency of 1600 Hz. Inferred neural circuit parameters Separate inverse models were trained for the Hagen and Cavallari simulation datasets. All inverse models estimate two target variables: E_I (Excitation/inhibition ratio) J_ext/ext_input_scale (External input strength) Hagen model parameters Each Hagen simulation directly varies seven neural circuit parameters: Parameter Description Range J_EE Strength of recurrent excitatory-to-excitatory connections 0.5–4.0 J_IE Strength of excitatory-to-inhibitory connections 0.5–4.0 J_EI Strength of inhibitory-to-excitatory connections −40.0 to −1.0 J_II Strength of recurrent inhibitory-to-inhibitory connections −40.0 to −1.0 tau_syn_E Excitatory synaptic time constant applied to excitatory inputs received by both neuronal populations 0.1–2.0 ms tau_syn_I Inhibitory synaptic time constant applied to inhibitory inputs received by both neuronal populations 0.1–8.0 ms J_ext Synaptic weight of the external Poisson input received by the network 10.0–50.0 The four recurrent coupling strengths are combined into the inferred excitatory-to-inhibitory target: E_I = (J_EE / J_EI) / (J_IE / J_II) Therefore, each Hagen inverse model estimates: E_I J_ext The individual recurrent coupling strengths and synaptic time constants are varied during simulation but are not estimated separately by the inverse models. Cavallari model parameters Each Cavallari simulation modifies seven parameters relative to the default Cavallari configuration available in the ncpi repository: Parameter Description Range g_EE Directly replaces exc_exc_recurrent, the excitatory-to-excitatory recurrent synaptic conductance 0.5–2.0 × default value of 0.178 g_IE Directly replaces exc_inh_recurrent, the excitatory-to-inhibitory recurrent synaptic conductance 0.5–2.0 × default value of 0.233 g_EI Directly replaces inh_exc_recurrent, the inhibitory-to-excitatory recurrent synaptic conductance 0.5–2.0 × default value of −2.01 g_II Directly replaces inh_inh_recurrent, the inhibitory-to-inhibitory recurrent synaptic conductance 0.5–2.0 × default value of −2.70 tau_syn_AMPA_scale Dimensionless scaling factor applied to tau_decay_AMPA in both excitatory and inhibitory neurons 0.5–2.0 tau_syn_GABA_scale Dimensionless scaling factor applied to tau_decay_GABA_A in both excitatory and inhibitory neurons 0.5–2.0 ext_input_scale Dimensionless scaling factor applied to both thalamic external-input conductances, th_exc_external and th_inh_external 0.5–4.0 The default AMPA decay constants are 2.0 ms for excitatory neurons and 1.0 ms for inhibitory neurons. The default GABA-A decay constant is 5.0 ms for both populations. The default values of the thalamic external-input conductances are 0.234 and 0.317, respectively. Cortico-cortical external inputs are not modified. The four recurrent conductances are combined into the inferred excitatory-to-inhibitory target: E_I = (g_EE / g_EI) / (g_IE / g_II) Therefore, each Cavallari inverse model estimates: E_I J_ext, corresponding to ext_input_scale The individual recurrent conductances, synaptic time-constant scaling factors, and external-input components are varied during simulation but are not estimated separately by the inverse models. Feature configurations Three feature configurations are included: Feature set Description catch22_22 Complete set of 22 canonical catch22 time-series features specparam_3 Three spectral-parameterization features: aperiodic slope, dominant peak frequency, and dominant peak power catch22_specparam_25 Concatenation of the 22 catch22 features and the 3 specparam-derived features Spectral parameterization was performed over 5–200 Hz, with a goodness-of-fit threshold of R² ≥ 0.9. Invalid or non-finite feature rows were excluded before model training. Trained inverse models Four inverse-model types were trained for each dataset and feature configuration: Model Description Ridge regression (Ridge) Linear regression model with L2 regularization. The regularization parameter alpha was predefined according to feature dimensionality. Multilayer perceptron regression (MLPRegressor) Feedforward neural-network regressor using ReLU activation, the Adam optimizer, early stopping, adaptive hidden-layer sizes, regularization, batch size, and validation fraction according to feature dimensionality, and a maximum of 1000 iterations. Random forest regression (RandomForestRegressor) Ensemble tree-based regressor using bootstrap aggregation, with predefined tree number, depth, leaf-size, feature-subsampling, and sample-subsampling settings according to feature dimensionality. Neural posterior estimation (NPE) Simulation-based inference model using a neural spline flow estimator, with predefined hidden features, number of transforms, and batch size according to feature dimensionality. Models were trained for up to 100 epochs with early stopping. This produces up to 36 trained dataset–feature–model combinations: 3 datasets × 3 feature configurations × 4 model types Models were trained using a reproducible random 85% training / 15% testing split with random seed 0. For ridge regression, multilayer perceptron regression, and random forest regression, test-set predictions and mean squared error metrics were computed on the held-out test set. For neural posterior estimation, the posterior estimator and related inference assets were trained and saved, but held-out test-set prediction and evaluation were skipped by design to avoid expensive posterior sampling. Included files The archive contains two main directories: new_features new_models new_features This directory includes: Computed feature matrices in NumPy format Valid-row masks Reproducible training and testing indices Cached feature matrices generated by the training workflow Precomputed per-batch Hagen_v1 feature and target files produced in streaming mode new_models This directory includes: Serialized trained models and feature scalers NPE inference, density-estimator, and posterior assets Model configuration files Test target arrays, y_test.npy, for evaluated scikit-learn models Model predictions, y_pred.npy, for evaluated scikit-learn models Per-parameter and mean squared error metrics, metrics.json NPE metrics files indicating that test evaluation was skipped Dataset-level result summaries, summary.json Software The processing and training workflow was implemented using ncpi and the script: run_massive_training_v3.py The ncpi source code is available at: https://github.com/necolab-ugr/ncpi The script used to generate these results is also included in this deposit. Recommended use This dataset is intended for: Reproducibility of the ncpi inverse-model training workflow Benchmarking feature-based and simulation-based inference methods Reuse of trained inverse models for methodological development Evaluation of derived electrophysiological feature representations Further validation against experimental EEG, MEG, or related electrophysiological datasets Limitations The raw simulation signals are not included in this record. The deposit contains derived features, trained models, predictions, metrics, and workflow assets. The included inverse models should be considered research tools. They have not yet been experimentally validated and should not be interpreted as clinically or biologically validated estimators without further independent testing.