=================== Available Models =================== This page provides an overview of the brain encoding models currently available in BERG. Model Naming Convention ---------------------- BERG contains several encoding models, defined by the following model ID naming convention: ``{modality}-{dataset}-{model}`` where * ``modality``: The neural recording modality on which the encoding model was trained. * ``dataset``: The neural dataset on which the encoding model was trained. * ``model``: The type of encoding model used. For example: - ``fmri-nsd-fwrf``: An fMRI encoding model trained on the NSD using feature-weighted receptive fields. - ``eeg-things_eeg_2-vit_b_32``: An EEG model trained on the THINGS-EEG2 dataset using the ViT-B/32 visual transformer architecture. Model Naming Convention (BrainScore models) ---------------------- BERG additionally contains a special class of encoding models that are trained on `BrainScore `_ benchmarks. These models have a slightly different naming convention: ``brainscore_{modality}-{model}`` where * ``modality``: The neural recording modality of the BrainScore benchmark on which the encoding model was trained (i.e. vision, language). * ``model``: The specific BrainScore model used (e.g. AlexNet, GPT2-XL). For example: - ``brainscore_vision-alexnet``: A vision model trained on the BrainScore vision benchmark using the AlexNet architecture. - ``brainscore_language-gpt2``: A language model trained on the BrainScore language benchmark using GPT-2. Get Model Information ------------------------ You can get detailed information about any model using: .. code-block:: python from berg import BERG berg = BERG("path/to/brain-encoding-response-generator") # List all available models all_models = berg.list_models() # Get detailed model information model_info = berg.describe("fmri-nsd-fwrf") Available models ---------------------- Following is a list of all available models, grouped by ``modality``. The ✅ icon indicates the best model for each ``dataset``. modality-fmri ~~~~~~~~~~~~~ Encoding models trained on neural responses recorded with functional Magnetic Resonance Imaging (fMRI). .. list-table:: :header-rows: 1 :widths: 3 60 40 20 10 10 10 :class: wrap-table * - Best model - Model ID - Description - Training dataset - Species - Stimuli - Encoding accuracy * - ✅ - :doc:`model_cards/fmri-nsd_fsaverage-huze` - Mapping of vision transformer image features onto fMRI responses. - Natural Scenes Dataset (surface space) - Human - Images - `Accuracy plots `_ * - - :doc:`model_cards/fmri-nsd_fsaverage-vit_b_32` - Linear mapping of vision transformer image features onto fMRI responses. - Natural Scenes Dataset (surface space) - Human - Images - `Accuracy plots `_ * - - :doc:`model_cards/fmri-nsd_fsaverage-alexnet` - Linear mapping of AlexNet image features onto fMRI responses. - Natural Scenes Dataset (surface space) - Human - Images - `Accuracy plots `_ * - - :doc:`model_cards/fmri-nsd_fsaverage-alexnet_untrained` - Linear mapping of an untrained AlexNet image features onto fMRI responses. - Natural Scenes Dataset (surface space) - Human - Images - `Accuracy plots `_ * - ✅ - :doc:`model_cards/fmri-nsd-fwrf` - Feature-weighted receptive fields, convolutional neural networks trained end-to-end to predict fMRI responses from input images. - Natural Scenes Dataset (volume space) - Human - Images - `Accuracy plots `_ * - ✅ - :doc:`model_cards/fmri-bmd-s3d` - Linear mapping of 3D CNN video features onto fMRI responses. - BOLD Moments Dataset (MNI152 volume space) - Human - Videos - `Accuracy plots `_ * - ✅ - :doc:`model_cards/fmri-things_fmri_1-vit_b_32` - Linear mapping of vision transformer image features onto whole-brain fMRI responses. - THINGS fMRI1 - Human - Images - `Accuracy plots `_ * - ✅ - :doc:`model_cards/fmri-mosaic-CNN8_multihead_subAll_verticesVisual` - CNN predicting visual cortex responses (7,831 vertices) for 93 subjects across 8 datasets. - MOSAIC (all datasets) - Human - Images - `Accuracy plots `_ * - ✅ - :doc:`model_cards/fmri-mosaic-CNN8_multihead_subNSD_verticesAll` - CNN predicting whole-cortex responses (57,051 vertices) for 8 NSD subjects. - MOSAIC (NSD) - Human - Images - `Accuracy plots `_ * - - :doc:`model_cards/fmri-tuckute_2024-GPT2_XL` - GPT2-XL–based linear encoding model (LLM embeddings + ridge regression) predicting brain responses to sentences. - `Tuckute et al., 2024 `_ - Human - Text - `Accuracy plots `_ * - - :doc:`model_cards/fmri-cneuromod_algo2025-text2fmri` - Transformer-based encoding model predicting whole-brain fMRI from video transcripts using LLM features - `CNeuromod/Algonauts 2025 `_ - Human - Text - `Hugging Face Collection `_ * - ✅ - :doc:`model_cards/fmri-cneuromod_algo2025-vibe` - Multimodal fMRI encoding model fusing video, audio, and text features via a two-stage Transformer architecture - `Algonauts 2025 Challenge `_ - Human - Video + Audio + Text - `Accuracy Plots `_ * - ✅ - :doc:`model_cards/brainscore_language` - Gateway to 10+ GPT-family language models from BrainScore, mapped to human fMRI responses (9 subjects, 384 sentences) via PLS regression. Model weights hosted by BrainScore — not BERG. - `Pereira et al., 2018 `_ - Human - Text - `BrainScore leaderboard `_ * - ✅ - :doc:`model_cards/fmri-dascoli_2026-tribe_v2` - Tri-modal (video, audio, language) Transformer encoding model predicting whole-brain fMRI (20,484 vertices). First place in Algonauts 2025 (263 teams). - `Multi-study naturalistic fMRI `_ - Human - Video + Audio + Text - `TRIBE v2 paper `_ * - ✅ - :doc:`model_cards/fmri-lebel2023-opt_1_3b` - OPT-1.3B–based linear encoding model (contextual LLM embeddings + ridge regression) predicting brain responses to spoken narratives, following `Antonello et al., 2023 `_. - `LeBel et al., 2023 `_ - Human - Text - `Accuracy plots `_ modality-eeg ~~~~~~~~~~~~ Encoding models trained on neural responses recorded with Electroencephalography (EEG). .. list-table:: :header-rows: 1 :widths: 3 60 40 20 10 10 10 :class: wrap-table * - Best model - Model ID - Description - Training dataset - Species - Stimuli - Encoding accuracy * - ✅ - :doc:`model_cards/eeg-things_eeg_2-vit_b_32` - Linear mapping of vision transformer image features onto EEG responses. - THINGS EEG2 - Human - Images - `Accuracy plots `_ * - - :doc:`model_cards/eeg-things_eeg_2-alexnet` - Linear mapping of AlexNet image features onto EEG responses. - THINGS EEG2 - Human - Images - `Accuracy plots `_ * - - :doc:`model_cards/eeg-things_eeg_2-alexnet_untrained` - Linear mapping of an untrained AlexNet image features onto EEG responses. - THINGS EEG2 - Human - Images - `Accuracy plots `_ modality-meg ~~~~~~~~~~~~ Encoding models trained on neural responses recorded with Magnetoencephalography (MEG). .. list-table:: :header-rows: 1 :widths: 3 60 40 20 10 10 10 :class: wrap-table * - Best model - Model ID - Description - Training dataset - Species - Stimuli - Encoding accuracy * - ✅ - :doc:`model_cards/meg-things_meg_1-vit_b_32` - Linear mapping of vision transformer image features onto time-resolved whole-brain MEG responses. - THINGS MEG1 - Human - Images - `Accuracy plots `_ modality-utah_array ~~~~~~~~~~~~~~~~~~~ Encoding models trained on neural responses recorded with Utah arrays (intracortical electrophysiology). .. list-table:: :header-rows: 1 :widths: 3 60 40 20 10 10 10 :class: wrap-table * - Best model - Model ID - Description - Training dataset - Species - Stimuli - Encoding accuracy * - ✅ - :doc:`model_cards/utah_array-tvsd-vit_b_32` - Linear mapping of vision transformer image features onto time-resolved intracortical spiking activity. - THINGS Ventral Stream Spiking Dataset (TVSD) - Macaque - Images - `Accuracy plots `_ modality-calcium_2p ~~~~~~~~~~~~~~~~~~~ Encoding models trained on neural responses recorded with two-photon calcium imaging. .. list-table:: :header-rows: 1 :widths: 3 60 40 20 10 10 10 :class: wrap-table * - Best model - Model ID - Description - Training dataset - Species - Stimuli - Encoding accuracy * - ✅ - :doc:`model_cards/calcium_2p-wang_2025-3DCNN` - Foundation model of mouse visual cortex, based on a spatiotemporal convolutional neural network (3D CNN + ConvLSTM). - `Wang et al., 2025 `_ - Mouse - Videos - `Accuracy plots `_ modality-ephys ~~~~~~~~~~~~~~ Encoding models trained on extracellular electrophysiology recordings from macaque visual cortex. .. list-table:: :header-rows: 1 :widths: 3 60 40 20 10 10 10 :class: wrap-table * - Best model - Model ID - Description - Training dataset - Species - Stimuli - Encoding accuracy * - ✅ - :doc:`model_cards/brainscore_vision` - Gateway to 440+ vision models from BrainScore, mapped to macaque electrophysiology recordings (V1, V2, V4, IT) via PLS regression. Model weights hosted by BrainScore — not BERG. - `Freeman et al., 2013 `_ (V1, V2); `Majaj et al., 2015 `_ (V4, IT) - Macaque - Images - `BrainScore leaderboard `_ modality-ecog ~~~~~~~~~~~~~ Encoding models trained on neural responses recorded with electrocorticography (ECoG). .. list-table:: :header-rows: 1 :widths: 3 60 40 20 10 10 10 :class: wrap-table * - Best model - Model ID - Description - Training dataset - Species - Stimuli - Encoding accuracy * - ✅ - :doc:`model_cards/ecog-zada2025-gpt2_xl` - Linear mapping of GPT-2 XL contextual word embeddings onto time-resolved high-gamma ECoG activity during natural speech comprehension. - `Zada et al., 2025 `_ - Human - Text - `Accuracy plots `_ .. raw:: html