Abstract
Understanding the neural code requires understanding how populations of neurons code information. Theoretical models predict that information may be limited by correlated noise in large neural populations. Nevertheless, analyses based on tens of neurons have failed to find evidence of saturation. Moreover, some studies have shown that noise correlations can be very small, and therefore may not affect information coding. To determine whether information-limiting correlations exist, we implanted 8 Utah arrays in prefrontal cortex (PFC; area 46) of two male macaque monkeys, recording >500 neurons simultaneously. We estimated information in PFC about saccades as a function of ensemble size. Noise correlations were, on average, small (∼10-3). However, information scaled strongly sub-linearly with ensemble size. After shuffling trials, destroying noise correlations, information becomes a linear function of ensemble size. Thus, we provide evidence for the existence of information-limiting noise correlations in large populations of PFC neurons.
SIGNIFICANCE STATEMENT
Recent theoretical work has shown that even small correlations can limit information if they are “differential correlations”, which are difficult to measure directly. However, they can be detected through decoding analyses on recordings from a large number of neurons over a large number of trials. We have achieved both by collecting neural activity in dorsal-lateral prefrontal cortex of macaques using 8 microelectrode arrays (768 electrodes), from which we were able to compute accurate information estimates. We show, for the first time, strong evidence for information-limiting correlations. Despite pair-wise correlations being small (on the order of 10-3) they affect information coding in populations on the order of 100s of neurons.
Footnotes
The authors declare no competing financial interests.
To perform the analyses described in this paper we made use of the computational resources of the NIH/HPC Biowulf cluster (http://hpc.nih.gov). This work was supported by the Intramural Research Program, National Institute of Mental Health/NIH (ZIA MH002928-01).
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