Distinct timescales of population coding across cortex

Abstract

The cortex represents information across widely varying timescales1–5. For instance, sensory cortex encodes stimuli that fluctuate over few tens of milliseconds6,7, whereas in association cortex behavioral choices can require the maintenance of information over seconds8,9. However, it remains poorly understood if diverse timescales result mostly from features intrinsic to individual neurons or from neuronal population activity. This question is unanswered because the timescales of coding in populations of neurons have not been studied extensively, and population codes have not been compared systematically across cortical regions. Here we discovered that population codes can be essential to achieve long coding timescales. Furthermore, we found that the properties of population codes differ between sensory and association cortices. We compared coding for sensory stimuli and behavioral choices in auditory cortex (AC) and posterior parietal cortex (PPC) as mice performed a sound localization task. Auditory stimulus information was stronger in AC than in PPC, and both regions contained choice information. Although AC and PPC coded information by tiling in time neurons that were transiently informative for ~200 milliseconds, the areas had major differences in functional coupling between neurons, measured as activity correlations that could not be explained by task events. Coupling among PPC neurons was strong and extended over long time lags, whereas coupling among AC neurons was weak and short-lived. Stronger coupling in PPC led to a population code with long timescales and a representation of choice that remained consistent for approximately one second. In contrast, AC had a code with rapid fluctuations in stimulus and choice information over hundreds of milliseconds. Our results reveal that population codes differ across cortex and that coupling is a variable property of cortical populations that affects the timescale of information coding and the accuracy of behavior.

Intrinsic dynamics enhance temporal stability of stimulus representation along rodent visual cortical hierarchies

“…We also found a mild dependence on the timescale of the movie (slope: 0.17±0.07, p < 0.05, two-tailed t-test), much weaker than that observed for the stimulus-driven response (compare Figures 2B and D). Interestingly, the range of intrinsic timescales we recorded in our experiment quantitatively matched that reported for sensory cortex in behaving monkeys by Murray et al (2014) and in behaving mice by Runyan et al (2017). Overall, these results show that the temporal scale of the intrinsic activity increases along rat ventral stream.…”

“…As illustrated in the previous section, our results suggest that the timescale of stimulus-locked, trialaveraged neural representations increases from V1 to extrastriate cortex. The temporal scale of intrinsic neural processing has also been suggested to increase across cortical hierarchies at the single cell (Murray et al 2014) and at the population level (Runyan et al 2017). We tested whether this applied to the rat ventral-like pathway by using the method described by Murray et al (2014), which is mathematically similar to the procedure used above to compute the timescale of the population response vectors, but considers the responses of a single cell across multiple trials, rather than the average responses of multiple neurons (see Methods).…”

“…Recently it has been shown that models of the ventral stream based on deep convolutional neural networks can be improved in their predictive power for perception and neural activity by including adaptation mechanisms (Vinken et al 2019) and recurrent processing (Kar et al 2019;Kietzmann et al 2019;Tang et al 2018). Moreover, a progressive increase of the importance of intrinsic processing along the ventral stream may be expected, given that intrinsic temporal scales increase along various cortical hierarchies in primates and rodents (Chaudhuri et al 2015;Himberger et al 2018;Murray et al 2014;Runyan et al 2017). However, it is not known how the interaction between invariant encoding of object information and intrinsic processing unfolds along the ventral stream, and whether or not the net result is an ordered progression of temporal scales of neural processing.…”

Three brain states in the hippocampus and cortex

“…To specify, episodic memory involves (a) single episodes encompassing longer periods of time (seconds to minutes and longer) (Ferbinteanu, Kennedy, & Shapiro, ; Shapiro, Kennedy, & Ferbinteanu, ; Smith & Mizumori, ), (b) knowledge of temporal relationships across episodes, and (c) retrieval of episodes long after initial storage. Although recent work has provided promising clues (Attardo, Fitzgerald, & Schnitzer, ; Cai et al, ; Driscoll, Pettit, Minderer, Chettih, & Harvey, ; Eichenbaum, ; Eichenbaum, ; Eichenbaum, ; Ezzyat & Davachi, ; Ferbinteanu & Shapiro, ; Frank, Stanley, & Brown, ; Hsieh, Gruber, Jenkins, & Ranganath, ; Karlsson & Frank, ; Kitamura et al, ; Ludvig, ; Mankin et al, ; Mankin et al, ; Manning, Polyn, Baltuch, Litt, & Kahana, ; Manns, Howard, & Eichenbaum, ; Murray et al, ; Ranganath & Hsieh, ; Rubin, Geva, Sheintuch, & Ziv, ; Runyan, Piasini, Panzeri, & Harvey, ; Suh, Rivest, Nakashiba, Tominaga, & Tonegawa, ; Ziv et al, ), the neural representation of events at longer timescales remains unclear. More knowledge and insight are needed.…”

Invariant Components of Synergy, Redundancy, and Unique Information among Three Variables

“…This work can contribute to the study of neural population coding, that is the study of how the concerted activity of many neurons encodes information about ecologically relevant variables such as sensory stimuli [40,42]. In particular, a key characteristic of a neural population code is the degree to which pairwise or higher-order cross-neuron statistical dependencies are used by the brain to encode and process information, in a potentially redundant or synergistic way, across neurons [7][8][9] and across time [43]. Moreover, characterizing different types of redundancy may also be relevant to further study the relation between the information of neural responses and neural connectivity [44].…”