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Single neurons may encode simultaneous stimuli by switching between activity patterns.

ABSTRACT: How the brain preserves information about multiple simultaneous items is poorly understood. We report that single neurons can represent multiple stimuli by interleaving signals across time. We record single units in an auditory region, the inferior colliculus, while monkeys localize 1 or 2 simultaneous sounds. During dual-sound trials, we find that some neurons fluctuate between firing rates observed for each single sound, either on a whole-trial or on a sub-trial timescale. These fluctuations are correlated in pairs of neurons, can be predicted by the state of local field potentials prior to sound onset, and, in one monkey, can predict which sound will be reported first. We find corroborating evidence of fluctuating activity patterns in a separate dataset involving responses of inferotemporal cortex neurons to multiple visual stimuli. Alternation between activity patterns corresponding to each of multiple items may therefore be a general strategy to enhance the brain processing capacity, potentially linking such disparate phenomena as variable neural firing, neural oscillations, and limits in attentional/memory capacity.

SUBMITTER: Caruso VC

PROVIDER: S-EPMC6045601 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Single neurons may encode simultaneous stimuli by switching between activity patterns.

Caruso Valeria C VC Mohl Jeff T JT Glynn Christopher C Lee Jungah J Willett Shawn M SM Zaman Azeem A Ebihara Akinori F AF Estrada Rolando R Freiwald Winrich A WA Tokdar Surya T ST Groh Jennifer M JM

Nature communications 20180713 1

How the brain preserves information about multiple simultaneous items is poorly understood. We report that single neurons can represent multiple stimuli by interleaving signals across time. We record single units in an auditory region, the inferior colliculus, while monkeys localize 1 or 2 simultaneous sounds. During dual-sound trials, we find that some neurons fluctuate between firing rates observed for each single sound, either on a whole-trial or on a sub-trial timescale. These fluctuations a ...[more]

PMID: 30006598

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Project description:New technologies make it possible to measure activity from many neurons simultaneously. One approach is to analyze simultaneously recorded neurons individually, then group together neurons which increase their activity during similar behaviors into an "ensemble." However, this notion of an ensemble ignores the ability of neurons to act collectively and encode and transmit information in ways that are not reflected by their individual activity levels. We used microendoscopic GCaMP imaging to measure prefrontal activity while mice were either alone or engaged in social interaction. We developed an approach that combines a neural network classifier and surrogate (shuffled) datasets to characterize how neurons synergistically transmit information about social behavior. Notably, unlike optimal linear classifiers, a neural network classifier with a single linear hidden layer can discriminate network states which differ solely in patterns of coactivity, and not in the activity levels of individual neurons. Using this approach, we found that surrogate datasets which preserve behaviorally specific patterns of coactivity (correlations) outperform those which preserve behaviorally driven changes in activity levels but not correlated activity. Thus, social behavior elicits increases in correlated activity that are not explained simply by the activity levels of the underlying neurons, and prefrontal neurons act collectively to transmit information about socialization via these correlations. Notably, this ability of correlated activity to enhance the information transmitted by neuronal ensembles is diminished in mice lacking the autism-associated gene Shank3. These results show that synergy is an important concept for the coding of social behavior which can be disrupted in disease states, reveal a specific mechanism underlying this synergy (social behavior increases correlated activity within specific ensembles), and outline methods for studying how neurons within an ensemble can work together to encode information.

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Single neurons may encode simultaneous stimuli by switching between activity patterns.

Publications

Single neurons may encode simultaneous stimuli by switching between activity patterns.

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