Strongly correlated spatiotemporal encoding and simple decoding in the prefrontal cortex

Abstract

We studied the fine temporal structure of spiking patterns of groups of up to 100 simultaneously recorded units in the prefrontal cortex of monkeys performing a visual discrimination task. We characterized the vocabulary of population activity patterns using 10 ms time bins and found that different sets of population activity patterns (codebooks) are used in different task epochs and that spiking correlations between units play a large role in defining those codebooks. Models that ignore those correlations fail to capture the population codebooks in all task epochs. Further, we show that temporal sequences of population activity patterns have strong history-dependence and are governed by different transition probabilities between patterns and different correlation time scales, in the different task epochs, suggesting different computational dynamics governing each epoch. Together, the large impact of spatial and temporal correlations on the dynamics of the population code makes the observed sequences of activity patterns many orders of magnitude more likely to appear than predicted by models that ignore these correlations and rely only on the population rates. Surprisingly, however, models that ignore these correlations perform quite well for decoding behavior from population responses. The difference of encoding and decoding complexity of the neural codebook suggests that one of the goals of the complex encoding scheme in the prefrontal cortex is to accommodate simple decoders that do not have to learn correlations.

The geometry of neuronal representations during rule learning reveals complementary roles of cingulate cortex and putamen

“…Our analyses were focused on single-neuron activity, demonstrating that we can track dynamics of representations even at the single-cell level. This complements the recent emphasis on neural ensembles to decode behavior from mixed representations (Golub et al, 2018;Grewe et al, 2017;Gr€ undemann et al, 2019;Karpas et al, 2019;Levy et al, 2019;Mante et al, 2013;Maoz et al, 2020;Rigotti et al, 2013). The readout of a neural ensemble can improve performance because of a change in individual neuron properties or because of a change in weights given to each neuron by a readout node.…”

Correlations enhance the behavioral readout of neural population activity in association cortex

“…Our work provides a measure of both the nature of readout non-optimality and its implication for the behavioral relevance of a neural code. Previous work on the optimality of readouts has examined whether a decoder of correlated population activity could be trained suboptimally to decode separately single-cell data and then join together their evidence [40][41][42] . Several of these studies have shown that even relatively simple decoders trained on single cells can decode stimulus information from population activity, so that correlations among cells do not greatly complicate the extraction of information.…”

Correlations enhance the behavioral readout of neural population activity in association cortex

“…Our work provides a measure of both the nature of readout non-optimality and its implication for the behavioral relevance of a neural code. Previous work on the optimality of readouts has examined whether a decoder of correlated population activity could be trained sub-optimally to decode separately single-cell data and then join together their evidence 40–42 . Several of these studies have shown that even relatively simple decoders trained on single cells can decode stimulus information from population activity, so that correlations among cells do not greatly complicate the extraction of information.…”