Cell-specific modulation of plasticity and cortical state by cholinergic inputs to the visual cortex

Sugihara, Hiroki; Chen, Naiyan; Sur, Mriganka

doi:10.1016/j.jphysparis.2016.11.004

Cited by 18 publications

(14 citation statements)

References 144 publications

(196 reference statements)

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“…A prominent view of sensory processing is that cortical networks are desynchronized during active wakefulness and synchronized during quiet wakefulness and sleep 1,[8][9][10][11][12]32 . This view is based on earlier rodent studies in which technical limitations required animals to be restrained [2][3][4]7 , hence preventing the analysis of dynamic states of cortical activity in larger animals freely moving in their environment.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, synchronized fluctuations in the responses of simultaneously recorded neurons have been predominantly observed in several sensory cortical areas of rodent brain [1][2][3][4][5][6][7] . During sleep and rest, cortical populations are intrinsically synchronized in the low-frequency range 1,8,9 , while during wakefulness they are actively desynchronized by cholinergic inputs received from subcortical areas [10][11][12] . Previous recordings from sensory areas have shown that the degree to which populations are desynchronized depends on behavioral context, which modulates sensory coding and perception [2][3][4]7,13,14 .…”

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confidence: 99%

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Dynamic states of population activity in prefrontal cortical networks of freely-moving macaque

Milton¹,

Shahidi²,

Dragoi

2020

Nat Commun

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Neural responses in the cerebral cortex change dramatically between the 'synchronized' state during sleep and 'desynchronized' state during wakefulness. Our understanding of cortical state emerges largely from experiments performed in sensory areas of head-fixed or tethered rodents due to technical limitations of recording from larger freely-moving animals for several hours. Here, we report a system integrating wireless electrophysiology, wireless eye tracking, and real-time video analysis to examine the dynamics of population activity in a high-level, executive areadorsolateral prefrontal cortex (dlPFC) of unrestrained monkey. This technology allows us to identify cortical substates during quiet and active wakefulness, and transitions in population activity during rest. We further show that narrow-spiking neurons exhibit stronger synchronized fluctuations in population activity than broad-spiking neurons regardless of state. Our results show that cortical state is controlled by behavioral demands and arousal by asymmetrically modulating the slow response fluctuations of local excitatory and inhibitory cell populations.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Dynamic states of population activity in prefrontal cortical networks of freely-moving macaque

Milton¹,

Shahidi²,

Dragoi

2020

Nat Commun

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“…These major types of GABAergic interneurons expressing PV and SOM are argued to contribute to cortical signal representation, local and system coordination, and experience-dependent plasticity (14,18,22,(54)(55)(56)(57). For example, recent studies have shown that PV+ neurons in the auditory cortex have markedly faster response latencies than PV-neurons, indicating their critical roles in regulating the temporal precision of cortical responses (55).…”

Section: Discussionmentioning

confidence: 99%

Positive impacts of early auditory training on cortical processing at an older age

Cheng

Jia

Zhang

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Progressive negative behavioral changes in normal aging are paralleled by a complex series of physical and functional declines expressed in the cerebral cortex. In studies conducted in the auditory domain, these degrading physical and functional cortical changes have been shown to be broadly reversed by intensive progressive training that improves the spectral and temporal resolution of acoustic inputs and suppresses behavioral distractors. Here we found older rats that were intensively trained on an attentionally demanding modulation-rate recognition task in young adulthood substantially retained training-driven improvements in temporal rate discrimination abilities over a subsequent 18-mo epoch-that is, forward into their older age. In parallel, this young-adult auditory training enduringly enhanced temporal and spectral information processing in their primary auditory cortices (A1). Substantially greater numbers of parvalbumin-and somatostatin-labeled inhibitory neurons (closer to the numbers recorded in young vigorous adults) were recorded in the A1 and hippocampus in old trained versus untrained age-matched rats. These results show that a simple form of training in young adulthood in this rat model enduringly delays the otherwise expected deterioration of the physical status and functional operations of the auditory nervous system, with evident training impacts generalized to the hippocampus.aging | auditory cortex | behavioral training | cortical processing | inhibition A degradation of the status of physical brain machinery, expressed by a decline in its temporal processing abilities, has been repeatedly associated with its deteriorating functional status in normal aging (1-4). Recent studies have shown that the machinery that supports processing accuracy and speed, as well as the processes supporting attention control and distractor suppression, can be substantially rejuvenated via simple forms of intensive training in the aged-rat model (4, 5). With auditory perceptual training, in parallel with recovery in behavioral abilities to that matching young-animal performance levels, serials of key physical, chemical, and functional aspects of cortical processing machinery in the trained rats were shown to be restored to a physical or functional status that approached that normally recorded in vigorous young-adult animals.In human studies, substantial changes in speed of processing (SOP) and in other spectro-temporal (or spatiotemporal) signal resolution of performance abilities have been shown to result from attention-demanding, speed-challenged auditory (3, 5-7) or visual training (8-11). For example, the accurate behavioral identification and stimulus-order reconstruction of rapidly successive auditory stimuli was restored in human individuals trained in their eighth decade of life to a performance level normally typifying human performance abilities recorded in their third or fourth decade (3, 6).Our goal here was to define the magnitude and endurance of an intense dose of attention-demanding modulation di...

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“…Nonetheless, the “internally generated” mode-switching processes we have described will be strongly modulated by goal state and reinforcement in real behavior ( Box 2 ). It has even been suggested that basal forebrain cholinergic projections, which modulate both cortical circuit state and plasticity (Sugihara, Chen, & Sur, 2016 ), may provide a supervisory signal that modulates local sensory learning (Hangya, Ranade, Lorenc, & Kepecs, 2015 ; see also Poort et al, 2015 ). Diffuse supervision signals, including those from reward, could greatly accelerate the switch-based learning we have described, and so are an important topic for future developments of our framework.…”

Section: Clarifications Predictions and Open Questionsmentioning

confidence: 99%

Switching between internal and external modes: A multiscale learning principle

Honey

Newman

Schapiro

2017

Network Neuroscience

111

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Brains construct internal models that support perception, prediction, and action in the external world. Individual circuits within a brain also learn internal models of the local world of input they receive, in order to facilitate efficient and robust representation. How are these internal models learned? We propose that learning is facilitated by continual switching between internally biased and externally biased modes of processing. We review computational evidence that this mode-switching can produce an error signal to drive learning. We then consider empirical evidence for the instantiation of mode-switching in diverse neural systems, ranging from subsecond fluctuations in the hippocampus to wake-sleep alternations across the whole brain. We hypothesize that these internal/external switching processes, which occur at multiple scales, can drive learning at each scale. This framework predicts that (a) slower mode-switching should be associated with learning of more temporally extended input features and (b) disruption of switching should impair the integration of new information with prior information.

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Cell-specific modulation of plasticity and cortical state by cholinergic inputs to the visual cortex

Cited by 18 publications

References 144 publications

Dynamic states of population activity in prefrontal cortical networks of freely-moving macaque

Dynamic states of population activity in prefrontal cortical networks of freely-moving macaque

Positive impacts of early auditory training on cortical processing at an older age

Switching between internal and external modes: A multiscale learning principle

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