Triple-N Dataset

The Triple-N dataset (Non-human Primate Neural Responses to Natural Scenes) is a large-scale, multimodal resource extending the Natural Scenes Dataset (NSD) framework to macaques. It combines functional magnetic resonance imaging (fMRI) with dense Neuropixels electrophysiological recordings in the inferotemporal (IT) cortex and early visual areas during the viewing of 1,000 natural scene images.

Overview

• Species: Macaque monkey (Macaca mulatta)
• Stimuli: 1,000 images from the NSD stimulus set (Allen et al. 2022)
• Recording modalities:
  • fMRI: Whole-brain functional MRI for defining functional regions of interest (category-selective patches)
  • Neuropixels: High-density silicon probes targeting IT cortex and early visual areas, providing hundreds of simultaneously isolated single units with millisecond temporal precision
• Publisher: Li et al. (2026), Nature Neuroscience — Resource article ¹

Key Findings

Category Selectivity in IT

IT category-selective regions (face patches, body patches, place patches) identified via fMRI exhibit robust tuning for their preferred categories at the single-neuron level, consistent with prior work by ref?

Temporal Response Dynamics

Dense Neuropixels sampling reveals diverse temporal response patterns across the IT neuronal population:

• Image-dependent latency variations
• Response patterns reflect both intrinsic neuronal properties and stimulus features
• Demonstrates that temporal coding carries information beyond mean firing rate

Cross-Species Alignment

Alignment of macaque electrophysiology with human NSD fMRI data reveals:

• Correspondences: Shared representational geometry for category-level organization (faces, bodies, places, objects) across species
• Divergences: Fine-grained differences in representational structure that may reflect species-specific processing or the fMRI-ephys resolution gap

Significance

The Triple-N dataset bridges a critical gap between:

1. Human fMRI (NSD): Large-scale but limited to hemodynamic resolution, no single-neuron access
2. Macaque electrophysiology: Single-neuron resolution but traditionally limited to small sample sizes and simplified stimuli

By using identical natural scene stimuli across species, the dataset enables principled cross-species comparisons and provides a foundation for unifying single-neuron dynamics with population-level and fMRI-voxel resolution measurements.

Data Access

Published in Nature Neuroscience as a Resource article. Expected data repository: likely CRCNS.org, OpenNeuro, or a dedicated institutional repository (details TBD at publication).

Related Pages

• natural-scenes-dataset — The human 7T fMRI NSD (Allen et al. 2022) that this dataset extends to macaques
• covariant-receptive-fields — Complementary work on spatio-temporal receptive field modelling for visual processing
• spiking-neural-networks — Computational frameworks for modelling neural response dynamics in vision

References

• Li, Y. et al. (2026). Triple-N dataset: large-scale fMRI-guided dense recordings of nonhuman primate neural responses to natural scenes. Nature Neuroscience. 10.1038/s41593-026-02322-z ¹
• Allen, E.J. et al. (2022). A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence. Nat. Neurosci. 25, 116–126. 10.1038/s41593-021-00962-x
• Bao, P. et al. (2020). A map of object space in primate inferotemporal cortex. Nature 583, 103–108. 10.1038/s41586-020-2350-5
• Kriegeskorte, N. et al. (2008). Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron 60, 1126–1141. 10.1016/j.neuron.2008.10.043

Footnotes

1. raw/papers/li-2026-triple-n-dataset.md ↩ ↩²