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Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

A modular semantic-structural pipeline for visual decoding from primate spiking data via selective temporal

Matteo Ciferri¹, Matteo Ferrante^1,2, Nicola Toschi^1,3

¹Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy.

Imaging Neuroscience (Cambridge, Mass.)

|July 15, 2026

Summary

Decoding visual information from primate brain activity is possible with simple models. Selective temporal aggregation and non-linearity are key for accurate image retrieval, informing brain-computer interface design.

Keywords:

brain–computer interfaces generative reconstruction intracortical recordings machine learning primate visual cortex visual decoding

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Published on: March 19, 2021

Area of Science:

Systems Neuroscience
Computational Neuroscience
Machine Learning

Background:

Decoding neural signals for visual information is a key challenge.
Understanding intracortical signals during visual processing is crucial.

Purpose of the Study:

To decode visual information from high-density intracortical recordings in primates.
To evaluate model architectures, training objectives, and data scaling for decoding performance.

Main Methods:

Utilized the THINGS Ventral Stream Spiking Dataset.
Systematically evaluated model architectures, training objectives, and data scaling.
Developed a modular generative decoding pipeline using latent reconstruction and diffusion models.

Main Results:

Decoding accuracy depends on non-linearity and selective temporal aggregation.
A shallow MLP with temporal attention achieved 70% top-1 image retrieval accuracy.
Identified diminishing returns with increased input dimensionality and dataset size.

Conclusions:

Simple models outperform complex ones for this specific neural decoding task.
The developed framework generates plausible images from neural activity.
Provides principles for brain-computer interfaces and semantic neural decoding.