Monkeys learn to report their own sensory cortical population activity

Authors

Hu, J., Okazawa, G.

Abstract

How sensory cortical activity is read out by downstream circuits to guide behavior is a fundamental unsolved problem. Substantial work has examined correlations between sensory neural responses and animals' perceptual judgments, but their interpretations remained controversial due to many intervening variables, such as responses of unrecorded neurons. Furthermore, stimulus and choice encoding in sensory populations are often not well aligned, and it remains contested whether this misalignment indicates a limitation in sensory readout. Here, we introduce a closed-loop, neurofeedback paradigm that directly interrogates the capacity of sensory readout: the key idea is to train subjects to report specific patterns of population activity in a sensory area recorded online, rather than the actual stimuli presented. To test this, we trained macaque monkeys on a visual change-detection task using shape stimuli, implanted an electrode array in visual area V4, and tested whether they could be further trained to rely on their own V4 activity along specific axes in neural state space. Strikingly, monkeys successfully increased the neuron-choice correlation along trained axes in neural population state space. No detectable changes in stimulus selectivity or noise correlations were found within the recorded population, and further model simulations confirmed that adjustment of sensory readouts best accounted for the results. A control experiment that merely disrupted the stimulus-reward contingency without a closed loop failed to enhance neuron-choice correlation. Together, these results demonstrate that closed-loop neural feedback achieves neuron-choice alignment beyond the ceiling of natural perceptual training, suggesting that the misalignment in perceptual tasks reflects constraints on learning within naturally available training regimes.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 14 Aug 2026.

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