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    Area of Science:

    • Neuroscience
    • Computational Neuroscience
    • Systems Neuroscience

    Background:

    • Neural population activity underlies brain function.
    • Noise correlations (NCs), or correlated neuronal spiking activity, are linked to synaptic connectivity and information capacity.
    • The role of NCs in neural coding, particularly independent of spike counts (SCs), is not fully understood.

    Purpose of the Study:

    • To investigate whether condition-dependent information exists in NCs independently of SCs.
    • To examine the relationship between SC selectivity and NC selectivity in prefrontal cortex.
    • To determine if NCs contribute to neural coding even when SCs lack condition-dependent information.

    Main Methods:

    • Recorded activity from large neuronal populations in the prefrontal cortex of macaques.
    • Utilized a spatial delayed response task with visual, memory, and motor epochs.
    • Analyzed spike counts (SCs) and noise correlations (NCs) for neuronal pairs across task conditions.

    Main Results:

    • Neuron pairs with selective SCs often showed selective NCs, independent of spike count.
    • Pairs of neurons without SC selectivity still exhibited condition-dependent NCs.
    • The magnitude of condition-dependent NCs was similar in both selective and non-selective neuronal pairs.

    Conclusions:

    • Correlated neuronal variability (NCs) can be condition-dependent irrespective of condition-dependent spike counts (SCs).
    • NCs represent a significant source of information in neural populations, separate from SCs.
    • This highlights the importance of considering neural correlations for understanding brain function and information processing.