Dataset Information

A recurrent network model of somatosensory parametric working memory in the prefrontal cortex.

ABSTRACT: A parametric working memory network stores the information of an analog stimulus in the form of persistent neural activity that is monotonically tuned to the stimulus. The family of persistent firing patterns with a continuous range of firing rates must all be realizable under exactly the same external conditions (during the delay when the transient stimulus is withdrawn). How this can be accomplished by neural mechanisms remains an unresolved question. Here we present a recurrent cortical network model of irregularly spiking neurons that was designed to simulate a somatosensory working memory experiment with behaving monkeys. Our model reproduces the observed positively and negatively monotonic persistent activity, and heterogeneous tuning curves of memory activity. We show that fine-tuning mathematically corresponds to a precise alignment of cusps in the bifurcation diagram of the network. Moreover, we show that the fine-tuned network can integrate stimulus inputs over several seconds. Assuming that such time integration occurs in neural populations downstream from a tonically persistent neural population, our model is able to account for the slow ramping-up and ramping-down behaviors of neurons observed in prefrontal cortex.

SUBMITTER: Miller P

PROVIDER: S-EPMC4632206 | biostudies-literature | 2003 Nov

REPOSITORIES: biostudies-literature

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Publications

A recurrent network model of somatosensory parametric working memory in the prefrontal cortex.

Miller Paul P Brody Carlos D CD Romo Ranulfo R Wang Xiao-Jing XJ

Cerebral cortex (New York, N.Y. : 1991) 20031101 11

A parametric working memory network stores the information of an analog stimulus in the form of persistent neural activity that is monotonically tuned to the stimulus. The family of persistent firing patterns with a continuous range of firing rates must all be realizable under exactly the same external conditions (during the delay when the transient stimulus is withdrawn). How this can be accomplished by neural mechanisms remains an unresolved question. Here we present a recurrent cortical netwo ...[more]

PMID: 14576212

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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

A recurrent network model of somatosensory parametric working memory in the prefrontal cortex.

Publications

A recurrent network model of somatosensory parametric working memory in the prefrontal cortex.

Similar Datasets

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