Monkey Perceptogram: Reconstructing Visual Representation and Presumptive Neural Preference from Monkey Multi-electrode Arrays
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916997902630912 |
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| author | Fei, Teng Ravishankar, Srinivas Nakada, Hoko Uppal, Abhinav Jackson, Ian Cottrell, Garrison W. Hayashi, Ryusuke de Sa, Virginia R. |
| author_facet | Fei, Teng Ravishankar, Srinivas Nakada, Hoko Uppal, Abhinav Jackson, Ian Cottrell, Garrison W. Hayashi, Ryusuke de Sa, Virginia R. |
| contents | Understanding how the primate brain transforms complex visual scenes into coherent perceptual experiences remains a central challenge in neuroscience. Here, we present a comprehensive framework for interpreting monkey visual processing by integrating encoding and decoding approaches applied to two large-scale spiking datasets recorded from macaque using THINGS images (THINGS macaque IT Dataset (TITD) and THINGS Ventral Stream Spiking Dataset (TVSD)). We leverage multi-electrode array recordings from the ventral visual stream--including V1, V4, and inferotemporal (IT) cortex--to investigate how distributed neural populations encode and represent visual information. Our approach employs linear models to decode spiking activity into multiple latent visual spaces (including CLIP and VDVAE embeddings) and reconstruct images using state-of-the-art generative models. We further utilize encoding models to map visual features back to neural activity, enabling visualization of the "preferred stimuli" that drive specific neural ensembles. Analyses of both datasets reveal that it is possible to reconstruct both low-level (e.g., color, texture) and high-level (e.g., semantic category) features of visual stimuli from population activity, with reconstructions preserving key perceptual attributes as quantified by feature-based similarity metrics. The spatiotemporal spike patterns reflect the ventral stream's hierarchical organization with anterior regions representing complex objects and categories. Functional clustering identifies feature-specific neural ensembles, with temporal dynamics show evolving feature selectivity post-stimulus. Our findings demonstrate feasible, generalizable perceptual reconstruction from large-scale monkey neural recordings, linking neural activity to perception. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07576 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Monkey Perceptogram: Reconstructing Visual Representation and Presumptive Neural Preference from Monkey Multi-electrode Arrays Fei, Teng Ravishankar, Srinivas Nakada, Hoko Uppal, Abhinav Jackson, Ian Cottrell, Garrison W. Hayashi, Ryusuke de Sa, Virginia R. Neurons and Cognition Understanding how the primate brain transforms complex visual scenes into coherent perceptual experiences remains a central challenge in neuroscience. Here, we present a comprehensive framework for interpreting monkey visual processing by integrating encoding and decoding approaches applied to two large-scale spiking datasets recorded from macaque using THINGS images (THINGS macaque IT Dataset (TITD) and THINGS Ventral Stream Spiking Dataset (TVSD)). We leverage multi-electrode array recordings from the ventral visual stream--including V1, V4, and inferotemporal (IT) cortex--to investigate how distributed neural populations encode and represent visual information. Our approach employs linear models to decode spiking activity into multiple latent visual spaces (including CLIP and VDVAE embeddings) and reconstruct images using state-of-the-art generative models. We further utilize encoding models to map visual features back to neural activity, enabling visualization of the "preferred stimuli" that drive specific neural ensembles. Analyses of both datasets reveal that it is possible to reconstruct both low-level (e.g., color, texture) and high-level (e.g., semantic category) features of visual stimuli from population activity, with reconstructions preserving key perceptual attributes as quantified by feature-based similarity metrics. The spatiotemporal spike patterns reflect the ventral stream's hierarchical organization with anterior regions representing complex objects and categories. Functional clustering identifies feature-specific neural ensembles, with temporal dynamics show evolving feature selectivity post-stimulus. Our findings demonstrate feasible, generalizable perceptual reconstruction from large-scale monkey neural recordings, linking neural activity to perception. |
| title | Monkey Perceptogram: Reconstructing Visual Representation and Presumptive Neural Preference from Monkey Multi-electrode Arrays |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2510.07576 |