Feedback inhibition derived from the posterior parietal cortex regulates the neural properties of the mouse visual cortex

2019 ◽  
Vol 50 (6) ◽  
pp. 2970-2987 ◽  
Author(s):  
Ryuichi Hishida ◽  
Masao Horie ◽  
Hiroaki Tsukano ◽  
Manavu Tohmi ◽  
Kohei Yoshitake ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Parietal Cortex ◽  
Posterior Parietal Cortex ◽  
Feedback Inhibition ◽  
Posterior Parietal ◽  
Mouse Visual Cortex

scholarly journals Feedback from human posterior parietal cortex enables visuospatial category representations as early as primary visual cortex

2017 ◽  
Vol 8 (1) ◽  
pp. e00886
Author(s):  
Yanyan Li ◽  
Xiaopeng Hu ◽  
Yongqiang Yu ◽  
Ke Zhao ◽  
Yuri B. Saalmann ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Parietal Cortex ◽  
Posterior Parietal Cortex ◽  
Posterior Parietal

scholarly journals Task-dependent representations of stimulus and choice in mouse parietal cortex

2017 ◽  
Author(s):  
Gerald N. Pho ◽  
Michael J. Goard ◽  
Jonathan Woodson ◽  
Benjamin Crawford ◽  
Mriganka Sur
Keyword(s):  
Visual Cortex ◽  
Parietal Cortex ◽  
Sensory Processing ◽  
Posterior Parietal Cortex ◽  
Task Engagement ◽  
Perceptual Decision Making ◽  
Sensorimotor Transformation ◽  
Motor Commands ◽  
Posterior Parietal ◽  
Sensorimotor Contingency

AbstractThe posterior parietal cortex (PPC) has been implicated in perceptual decisions, but whether its role is specific to sensory processing or sensorimotor transformation is not well understood. To distinguish these possibilities, we trained mice of either sex to perform a visual discrimination task and imaged the activity of PPC populations during both engaged behavior and passive viewing. Unlike neurons in primary visual cortex (V1), which responded robustly to stimuli in both conditions, most neurons in PPC responded exclusively during task engagement. However, PPC responses were heterogeneous, with a smaller subset of neurons exhibiting stimulus-driven, contrast-dependent responses in both conditions. Neurons in PPC also exhibit stronger modulation by noise correlations relative to V1, as illustrated by a generalized linear model that takes into account both task variables and between-neuron correlations. To test whether PPC responses primarily encoded the stimulus or the learned sensorimotor contingency, we imaged the same neurons before and after re-training mice on a reversed task contingency. Unlike V1 neurons, most PPC neurons exhibited a dramatic shift in selectivity after re-training and reflected the new sensorimotor contingency, while a smaller subset of neurons preserved their stimulus selectivity. Mouse PPC is therefore strongly task-dependent, contains heterogeneous populations sensitive to stimulus and choice, and may play an important role in the flexible transformation of sensory inputs into motor commands.Significance StatementPerceptual decision making involves both processing of sensory information and mapping that information onto appropriate motor commands via learned sensorimotor associations. While visual cortex (V1) is known to be critical for sensory processing, it is unclear what circuits are involved in the process of sensorimotor transformation. While the mouse posterior parietal cortex (PPC) has been implicated in visual decisions, its specific role has been controversial. By imaging population activity while manipulating task engagement and sensorimotor contingencies, we demonstrate that PPC, unlike V1, is highly task-dependent, heterogeneous, and sensitive to the learned task demands. Our results suggest that PPC is more than a visual area, and may instead be involved in the flexible mapping of visual information onto appropriate motor actions.

scholarly journals Dynamic changes in spatial representation within the posterior parietal cortex in response to visuomotor adaptation

2021 ◽  
Author(s):  
Selene Schintu ◽  
Dwight J. Kravitz ◽  
Edward H. Silson ◽  
Catherine A. Cunningham ◽  
Eric M. Wassermann ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Visual Field ◽  
Receptive Field ◽  
Parietal Cortex ◽  
Spatial Representation ◽  
Posterior Parietal Cortex ◽  
Visuomotor Adaptation ◽  
Prism Adaptation ◽  
Attentional Deployment ◽  
Posterior Parietal

Recent studies used fMRI population receptive field (pRF) mapping to demonstrate that retinotopic organization extends from primary visual cortex to ventral and dorsal visual pathways by quantifying visual field maps, receptive field size, and laterality throughout multiple areas. Visuospatial representation in the posterior parietal cortex (PPC) is modulated by attentional deployment, raising the question of whether spatial representation in the PPC is dynamic and flexible and that this flexibility contributes to visuospatial learning. To answer this question, changes in spatial representation within PPC, as measured with pRF mapping, were recorded before and after visuomotor adaptation. Visuospatial input was laterally manipulated, rightward or leftward, via prism adaptation, a well-established visuomotor technique that modulates visuospatial performance. Based on existing models of prism adaptation mechanism of action, we predicted left prism adaptation to produce a right visuospatial bias via an increasing pRF size in the left parietal cortex. However, our hypothesis was agnostic as to whether right PPC will show an opposite effect given the bilateral bias to right visual field. Findings show that adaptation to left-shifting prisms increases pRF size in both PPCs, while leaving space representation in early visual cortex unchanged. This is the first evidence that prism adaptation drives a dynamic reorganization of response profiles in the PPC. Our results show that spatial representation in the PPC not only reflects changes driven by attentional deployment but dynamically changes in response to visuomotor adaptation. Furthermore, our results provide support for using prism adaptation as a tool to rehabilitate visuospatial deficits.

scholarly journals Brain stimulation boosts perceptual learning by altering sensory GABAergic plasticity and functional connectivity

2021 ◽  
Author(s):  
Vasilis M Karlaftis ◽  
Polytimi Frangou ◽  
Cameron Higgins ◽  
Diego Vidaurre ◽  
Joseph J Ziminski ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Perceptual Learning ◽  
Parietal Cortex ◽  
Brain Stimulation ◽  
Posterior Parietal Cortex ◽  
Resonance Spectroscopy ◽  
Anodal Tdcs ◽  
Posterior Parietal

AbstractInterpreting cluttered scenes —a key skill for successfully interacting with our environment— relies on our ability to select relevant sensory signals while filtering out noise. Training is known to improve our ability to make these perceptual judgements by altering local processing in sensory brain areas. Yet, the brain-wide network mechanisms that mediate our ability for perceptual learning remain largely unknown. Here, we combine transcranial direct current stimulation (tDCS) with multi-modal brain measures to modulate cortical excitability during training on a signal-in-noise task (i.e. detection of visual patterns in noise) and test directly the link between processing in visual cortex and its interactions with decision-related areas (i.e. posterior parietal cortex). We test whether brain stimulation alters inhibitory processing in visual cortex, as measured by magnetic resonance spectroscopy (MRS) of GABA and functional connectivity between visual and posterior parietal cortex, as measured by resting state functional magnetic resonance imaging (rs-fMRI). We show that anodal tDCS during training results in faster learning and decreased GABA+ during training, before these changes occur for training without stimulation (i.e. sham). Further, anodal tDCS decreases occipito-parietal interactions and time-varying connectivity across the visual cortex. Our findings demonstrate that tDCS boosts learning by accelerating visual GABAergic plasticity and altering interactions between visual and decision-related areas, suggesting that training optimises gain control mechanisms (i.e. GABAergic inhibition) and functional inter-areal interactions to support perceptual learning.

Posterior parietal cortex mediates encoding processes in change blindness

2009 ◽  
Author(s):  
Philip Tseng ◽  
Cassidy Sterling ◽  
Adam Cooper ◽  
Bruce Bridgeman ◽  
Neil G. Muggleton ◽  
...  
Keyword(s):  
Parietal Cortex ◽  
Posterior Parietal Cortex ◽  
Change Blindness ◽  
Posterior Parietal

The Grasp Cell Hypothesis: Near-miss effect points to common neurological machinery in posterior parietal cortex for controllable objects and concepts

2018 ◽  
Author(s):  
Imogen M Kruse
Keyword(s):  
Parietal Cortex ◽  
Posterior Parietal Cortex ◽  
Near Miss ◽  
Illusion Of Control ◽  
Gambling Behaviour ◽  
Drug Seeking ◽  
Spatial Influence ◽  
Reward Probability ◽  
Posterior Parietal ◽  
Action Value

The near-miss effect in gambling behaviour occurs when an outcome which is close to a win outcome invigorates gambling behaviour notwithstanding lack of associated reward. In this paper I postulate that the processing of concepts which are deemed controllable is rooted in neurological machinery located in the posterior parietal cortex specialised for the processing of objects which are immediately actionable or controllable because they are within reach. I theorise that the use of a common machinery facilitates spatial influence on the perception of concepts such that the win outcome which is 'almost complete' is perceived as being 'almost within reach'. The perceived realisability of the win increases subjective reward probability and the associated expected action value which impacts decision-making and behaviour. This novel hypothesis is the first to offer a neurological model which can comprehensively explain many empirical findings associated with the near-miss effect as well as other gambling phenomena such as the ‘illusion of control’. Furthermore, when extended to other compulsive behaviours such as drug addiction, the model can offer an explanation for continued drug-seeking following devaluation and for the increase in cravings in response to perceived opportunity to self-administer, neither of which can be explained by simple reinforcement models alone. This paper therefore provides an innovative and unifying perspective for the study and treatment of behavioural and substance addictions.

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