Dataset Information

Multiple component networks support working memory in prefrontal cortex.

ABSTRACT: Lateral prefrontal cortex (PFC) is regarded as the hub of the brain's working memory (WM) system, but it remains unclear whether WM is supported by a single distributed network or multiple specialized network components in this region. To investigate this problem, we recorded from neurons in PFC while monkeys made delayed eye movements guided by memory or vision. We show that neuronal responses during these tasks map to three anatomically specific modes of persistent activity. The first two modes encode early and late forms of information storage, whereas the third mode encodes response preparation. Neurons that reflect these modes are concentrated at different anatomical locations in PFC and exhibit distinct patterns of coordinated firing rates and spike timing during WM, consistent with distinct networks. These findings support multiple component models of WM and consequently predict distinct failures that could contribute to neurologic dysfunction.

SUBMITTER: Markowitz DA

PROVIDER: S-EPMC4568266 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Multiple component networks support working memory in prefrontal cortex.

Markowitz David A DA Curtis Clayton E CE Pesaran Bijan B

Proceedings of the National Academy of Sciences of the United States of America 20150817 35

Lateral prefrontal cortex (PFC) is regarded as the hub of the brain's working memory (WM) system, but it remains unclear whether WM is supported by a single distributed network or multiple specialized network components in this region. To investigate this problem, we recorded from neurons in PFC while monkeys made delayed eye movements guided by memory or vision. We show that neuronal responses during these tasks map to three anatomically specific modes of persistent activity. The first two mode ...[more]

PMID: 26283366

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Project description:Working memory (WM) provides the stability necessary for high-level cognition. Influential theories typically assume that WM depends on the persistence of stable neural representations, yet increasing evidence suggests that neural states are highly dynamic. Here we apply multivariate pattern analysis to explore the population dynamics in primate lateral prefrontal cortex (PFC) during three variants of the classic memory-guided saccade task (recorded in four animals). We observed the hallmark of dynamic population coding across key phases of a working memory task: sensory processing, memory encoding, and response execution. Throughout both these dynamic epochs and the memory delay period, however, the neural representational geometry remained stable. We identified two characteristics that jointly explain these dynamics: (1) time-varying changes in the subpopulation of neurons coding for task variables (i.e., dynamic subpopulations); and (2) time-varying selectivity within neurons (i.e., dynamic selectivity). These results indicate that even in a very simple memory-guided saccade task, PFC neurons display complex dynamics to support stable representations for WM.SIGNIFICANCE STATEMENT Flexible, intelligent behavior requires the maintenance and manipulation of incoming information over various time spans. For short time spans, this faculty is labeled "working memory" (WM). Dominant models propose that WM is maintained by stable, persistent patterns of neural activity in prefrontal cortex (PFC). However, recent evidence suggests that neural activity in PFC is dynamic, even while the contents of WM remain stably represented. Here, we explored the neural dynamics in PFC during a memory-guided saccade task. We found evidence for dynamic population coding in various task epochs, despite striking stability in the neural representational geometry of WM. Furthermore, we identified two distinct cellular mechanisms that contribute to dynamic population coding.

Project description:Distinct components of working memory are coordinated by different classes of inhibitory interneurons in the PFC, but the role of cholecystokinin (CCK)-positive interneurons remains enigmatic. In humans, this major population of interneurons shows histological abnormalities in schizophrenia, an illness in which deficient working memory is a core defining symptom and the best predictor of long-term functional outcome. Yet, CCK interneurons as a molecularly distinct class have proved intractable to examination by typical molecular methods due to widespread expression of CCK in the pyramidal neuron population. Using an intersectional approach in mice of both sexes, we have succeeded in labeling, interrogating, and manipulating CCK interneurons in the mPFC. Here, we describe the anatomical distribution, electrophysiological properties, and postsynaptic connectivity of CCK interneurons, and evaluate their role in cognition. We found that CCK interneurons comprise a larger proportion of the mPFC interneurons compared with parvalbumin interneurons, targeting a wide range of neuronal subtypes with a distinct connectivity pattern. Phase-specific optogenetic inhibition revealed that CCK, but not parvalbumin, interneurons play a critical role in the retrieval of working memory. These findings shine new light on the relationship between cortical CCK interneurons and cognition and offer a new set of tools to investigate interneuron dysfunction and cognitive impairments associated with schizophrenia.SIGNIFICANCE STATEMENT Cholecystokinin-expressing interneurons outnumber other interneuron populations in key brain areas involved in cognition and memory, including the mPFC. However, they have proved intractable to examination as experimental techniques have lacked the necessary selectivity. To the best of our knowledge, the present study is the first to report detailed properties of cortical cholecystokinin interneurons, revealing their anatomical organization, electrophysiological properties, postsynaptic connectivity, and behavioral function in working memory.

Dataset Information

Multiple component networks support working memory in prefrontal cortex.

Publications

Multiple component networks support working memory in prefrontal cortex.

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