parietal cortex
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Stress changes risk-taking by altering Bayesian magnitude coding in parietal cortex
Convergence of scene perception and visuospatial memory in posterior cerebral cortex
A neural mechanism for terminating decisions
The brain makes decisions by accumulating evidence until there is enough to stop and choose. Neural mechanisms of evidence accumulation are well established in association cortex, but the site and mechanism of termination is unknown. Here, we elucidate a mechanism for termination by neurons in the primate superior colliculus. We recorded simultaneously from neurons in lateral intraparietal cortex (LIP) and the superior colliculus (SC) while monkeys made perceptual decisions, reported by eye-movements. Single-trial analyses revealed distinct dynamics: LIP tracked the accumulation of evidence on each decision, and SC generated one burst at the end of the decision, occasionally preceded by smaller bursts. We hypothesized that the bursts manifest a threshold mechanism applied to LIP activity to terminate the decision. Focal inactivation of SC produced behavioral effects diagnostic of an impaired threshold sensor, requiring a stronger LIP signal to terminate a decision. The results reveal the transformation from deliberation to commitment.
Multi-level theory of neural representations in the era of large-scale neural recordings: Task-efficiency, representation geometry, and single neuron properties
A central goal in neuroscience is to understand how orchestrated computations in the brain arise from the properties of single neurons and networks of such neurons. Answering this question requires theoretical advances that shine light into the ‘black box’ of representations in neural circuits. In this talk, we will demonstrate theoretical approaches that help describe how cognitive and behavioral task implementations emerge from the structure in neural populations and from biologically plausible neural networks. First, we will introduce an analytic theory that connects geometric structures that arise from neural responses (i.e., neural manifolds) to the neural population’s efficiency in implementing a task. In particular, this theory describes a perceptron’s capacity for linearly classifying object categories based on the underlying neural manifolds’ structural properties. Next, we will describe how such methods can, in fact, open the ‘black box’ of distributed neuronal circuits in a range of experimental neural datasets. In particular, our method overcomes the limitations of traditional dimensionality reduction techniques, as it operates directly on the high-dimensional representations, rather than relying on low-dimensionality assumptions for visualization. Furthermore, this method allows for simultaneous multi-level analysis, by measuring geometric properties in neural population data, and estimating the amount of task information embedded in the same population. These geometric frameworks are general and can be used across different brain areas and task modalities, as demonstrated in the work of ours and others, ranging from the visual cortex to parietal cortex to hippocampus, and from calcium imaging to electrophysiology to fMRI datasets. Finally, we will discuss our recent efforts to fully extend this multi-level description of neural populations, by (1) investigating how single neuron properties shape the representation geometry in early sensory areas, and by (2) understanding how task-efficient neural manifolds emerge in biologically-constrained neural networks. By extending our mathematical toolkit for analyzing representations underlying complex neuronal networks, we hope to contribute to the long-term challenge of understanding the neuronal basis of tasks and behaviors.
Controlling the present while planning the future: How the brain learns and produces fast motor sequences
Motor sequencing is one of the fundamental components of human motor skill. In this talk I will show evidence that the fast and smooth production of motor sequences relies on the ability to plan upcoming movements while simultaneously controlling the ongoing movement. I will argue that this ability relies heavily on planning-related areas in premotor and parietal cortex.
From Computation to Large-scale Neural Circuitry in Human Belief Updating
Many decisions under uncertainty entail dynamic belief updating: multiple pieces of evidence informing about the state of the environment are accumulated across time to infer the environmental state, and choose a corresponding action. Traditionally, this process has been conceptualized as a linear and perfect (i.e., without loss) integration of sensory information along purely feedforward sensory-motor pathways. Yet, natural environments can undergo hidden changes in their state, which requires a non-linear accumulation of decision evidence that strikes a tradeoff between stability and flexibility in response to change. How this adaptive computation is implemented in the brain has remained unknown. In this talk, I will present an approach that my laboratory has developed to identify evidence accumulation signatures in human behavior and neural population activity (measured with magnetoencephalography, MEG), across a large number of cortical areas. Applying this approach to data recorded during visual evidence accumulation tasks with change-points, we find that behavior and neural activity in frontal and parietal regions involved in motor planning exhibit hallmarks signatures of adaptive evidence accumulation. The same signatures of adaptive behavior and neural activity emerge naturally from simulations of a biophysically detailed model of a recurrent cortical microcircuit. The MEG data further show that decision dynamics in parietal and frontal cortex are mirrored by a selective modulation of the state of early visual cortex. This state modulation is (i) specifically expressed in the alpha frequency-band, (ii) consistent with feedback of evolving belief states from frontal cortex, (iii) dependent on the environmental volatility, and (iv) amplified by pupil-linked arousal responses during evidence accumulation. Together, our findings link normative decision computations to recurrent cortical circuit dynamics and highlight the adaptive nature of decision-related long-range feedback processing in the brain.
Visualising time in the human brain
We all have a sense of time. Yet it is a particularly intangible sensation. So how is our “sense” of time represented in the brain? Functional neuroimaging studies have consistently identified a network of regions, including Supplementary Motor Area and basal ganglia, that are activated when participants make judgements about the duration of currently unfolding events. In parallel, left parietal cortex and cerebellum are activated when participants predict when future events are likely to occur. These structures are activated by temporal processing even when task goals are purely perceptual. So why should the perception of time be represented in regions of the brain that have more traditionally been implicated in motor function? One possibility is that we learn about time through action. In other words, action could provide the functional scaffolding for learning about time in childhood, explaining why it has come to be represented in motor circuits of the adult brain.
Cognitive experience alters cortical involvement in navigation decisions
The neural correlates of decision-making have been investigated extensively, and recent work aims to identify under what conditions cortex is actually necessary for making accurate decisions. We discovered that mice with distinct cognitive experiences, beyond sensory and motor learning, use different cortical areas and neural activity patterns to solve the same task, revealing past learning as a critical determinant of whether cortex is necessary for decision tasks. We used optogenetics and calcium imaging to study the necessity and neural activity of multiple cortical areas in mice with different training histories. Posterior parietal cortex and retrosplenial cortex were mostly dispensable for accurate performance of a simple navigation-based visual discrimination task. In contrast, these areas were essential for the same simple task when mice were previously trained on complex tasks with delay periods or association switches. Multi-area calcium imaging showed that, in mice with complex-task experience, single-neuron activity had higher selectivity and neuron-neuron correlations were weaker, leading to codes with higher task information. Therefore, past experience is a key factor in determining whether cortical areas have a causal role in decision tasks.
The pervasive role of visuospatial coding
Historically, retinotopic organisation (the spatial mapping of the retina across the cortical surface) was considered the purview of early regions of visual cortex (V1-V4) only and that anterior, more cognitively involved regions abstracted this information away. The contemporary view is quite different. Here, with Advancing technologies and analysis methods, we see that retinotopic information is not simply thrown away by these regions but rather is maintained to the potential benefit of our broader cognition. This maintenance of visuospatial coding extends not only through visual cortex, but is present in parietal, frontal, medial and subcortical structures involved with coordinating-movements, mind-wandering and even memory. In this talk, I will outline some of the key empirical findings from my own work and the work of others that shaped this contemporary perspective.
NMC4 Short Talk: Neurocomputational mechanisms of causal inference during multisensory processing in the macaque brain
Natural perception relies inherently on inferring causal structure in the environment. However, the neural mechanisms and functional circuits that are essential for representing and updating the hidden causal structure during multisensory processing are unknown. To address this, monkeys were trained to infer the probability of a potential common source from visual and proprioceptive signals on the basis of their spatial disparity in a virtual reality system. The proprioceptive drift reported by monkeys demonstrated that they combined historical information and current multisensory signals to estimate the hidden common source and subsequently updated both the causal structure and sensory representation. Single-unit recordings in premotor and parietal cortices revealed that neural activity in premotor cortex represents the core computation of causal inference, characterizing the estimation and update of the likelihood of integrating multiple sensory inputs at a trial-by-trial level. In response to signals from premotor cortex, neural activity in parietal cortex also represents the causal structure and further dynamically updates the sensory representation to maintain consistency with the causal inference structure. Thus, our results indicate how premotor cortex integrates historical information and sensory inputs to infer hidden variables and selectively updates sensory representations in parietal cortex to support behavior. This dynamic loop of frontal-parietal interactions in the causal inference framework may provide the neural mechanism to answer long-standing questions regarding how neural circuits represent hidden structures for body-awareness and agency.
NMC4 Short Talk: Decoding finger movements from human posterior parietal cortex
Restoring hand function is a top priority for individuals with tetraplegia. This challenge motivates considerable research on brain-computer interfaces (BCIs), which bypass damaged neural pathways to control paralyzed or prosthetic limbs. Here, we demonstrate the BCI control of a prosthetic hand using intracortical recordings from the posterior parietal cortex (PPC). As part of an ongoing clinical trial, two participants with cervical spinal cord injury were each implanted with a 96-channel array in the left PPC. Across four sessions each, we recorded neural activity while they attempted to press individual fingers of the contralateral (right) hand. Single neurons modulated selectively for different finger movements. Offline, we accurately classified finger movements from neural firing rates using linear discriminant analysis (LDA) with cross-validation (accuracy = 90%; chance = 17%). Finally, the participants used the neural classifier online to control all five fingers of a BCI hand. Online control accuracy (86%; chance = 17%) exceeded previous state-of-the-art finger BCIs. Furthermore, offline, we could classify both flexion and extension of the right fingers, as well as flexion of all ten fingers. Our results indicate that neural recordings from PPC can be used to control prosthetic fingers, which may help contribute to a hand restoration strategy for people with tetraplegia.
Rule learning representation in the fronto-parietal network
We must constantly adapt the rules we use to guide our attention. To understand how the brain learns these rules, we designed a novel task that required monkeys to learn which color is the most rewarded at a given time (the current rule). However, just as in real life, the monkey was never explicitly told the rule. Instead, they had to learn it through trial and error by choosing a color, receiving feedback (amount of reward), and then updating their internal rule. After the monkeys reached a behavioral criterion, the rule changed. This change was not cued but could be inferred based on reward feedback. Behavioral modeling found monkeys used rewards to learn the rules. After the rule changed, animals adopted one of two strategies. If the change was small, reflected in a small reward prediction error, the animals continuously updated their rule. However, for large changes, monkeys ‘reset’ their belief about the rule and re-learned the rule from scratch. To understand the neural correlates of learning new rules, we recorded neurons simultaneously from the prefrontal and parietal cortex. We found that the strength of the rule representation increased with the certainty about the current rule, and that the certainty about the rule was represented both implicitly and explicitly in the population.
A role for cognitive maps in metaphors and analogy?
In human and non-human animals, conceptual knowledge is partially organized according to low-dimensional geometries that rely on brain structures and computations involved in spatial representations. Recently, two separate lines of research have investigated cognitive maps, that are associated with the hippocampal formation and are similar to world-centered representations of the environment, and image spaces, that are associated with the parietal cortex and are similar to self-centered spatial relationships. I will suggest that cognitive maps and image spaces may be two manifestations of a more general propensity of the mind to create low-dimensional internal models, and may play a role in analogical reasoning and metaphorical thinking. Finally, I will show some data suggesting that the metaphorical relationship between colors and emotions can be accounted for by the structural alignment of low-dimensional conceptual spaces.
Neural representation of pose and movement in parietal cortex and beyond
Jonathan Whitlock is an associate professor of neuroscience at the Kavli Institute for Systems Neuroscience in Trondheim, Norway. His group combines high-density single-unit recordings with silicone probes and sub-millimeter 3D tracking to study the cortical representation of pose and movement in freely behaving rats. The lecture will introduce his group’s work on neural tuning to pose and movement parietal and motor areas, and will include more recent findings from primary visual, auditory and somatosensory areas
Emergent scientists discuss Alzheimer's disease
This seminar is part of our “Emergent Scientists” series, an initiative that provides a platform for scientists at the critical PhD/postdoc transition period to share their work with a broad audience and network. Summary: These talks cover Alzheimer’s disease (AD) research in both mice and humans. Christiana will discuss in particular the translational aspects of applying mouse work to humans and the importance of timing in disease pathology and intervention (e.g. timing between AD biomarkers vs. symptom onset, timing of therapy, etc.). Siddharth will discuss a rare variant of Alzheimer’s disease called “Logopenic Progressive Aphasia”, which presents with temporo-parietal atrophy yet relative sparing of hippocampal circuitry. Siddharth will discuss how, despite the unusual anatomical basis underlying this AD variant, degeneration of the angular gyrus in the left inferior parietal lobule contributes to memory deficits similar to those of typical amnesic Alzheimer’s disease. Christiana’s abstract: Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder that causes severe deterioration of memory, cognition, behavior, and the ability to perform daily activities. The disease is characterized by the accumulation of two proteins in fibrillar form; Amyloid-β forms fibrils that accumulate as extracellular plaques while tau fibrils form intracellular tangles. Here we aim to translate findings from a commonly used AD mouse model to AD patients. Here we initiate and chronically inhibit neuropathology in lateral entorhinal cortex (LEC) layer two neurons in an AD mouse model. This is achieved by over-expressing P301L tau virally and chronically activating hM4Di DREADDs intracranially using the ligand dechloroclozapine. Biomarkers in cerebrospinal fluid (CSF) is measured longitudinally in the model using microdialysis, and we use this same system to intracranially administer drugs aimed at halting AD-related neuropathology. The models are additionally tested in a novel contextual memory task. Preliminary findings indicate that viral injections of P301L tau into LEC layer two reveal direct projections between this region and the outer molecular layer of dentate gyrus and the rest of hippocampus. Additionally, phosphorylated tau co-localize with ‘starter cells’ and appear to spread from the injection site. Preliminary microdialysis results suggest that the concentrations of CSF amyloid-β and tau proteins mirror changes observed along the disease cascade in patients. The disease-modifying drugs appear to halt neuropathological development in this preclincial model. These findings will lead to a novel platform for translational AD research, linking the extensive research done in rodents to clinical applications. Siddharth’s abstract: A distributed brain network supports our ability to remember past events. The parietal cortex is a critical member of this network, yet, its exact contributions to episodic remembering remain unclear. Neurodegenerative syndromes affecting the posterior neocortex offer a unique opportunity to understand the importance and role of parietal regions to episodic memory. In this talk, I introduce and explore the rare neurodegenerative syndrome of Logopenic Progressive Aphasia (LPA), an aphasic variant of Alzheimer’s disease presenting with early, left-lateralized temporo-parietal atrophy, amidst relatively spared hippocampal integrity. I then discuss two key studies from my recent Ph.D. work showcasing pervasive episodic and autobiographical memory dysfunction in LPA, to a level comparable to typical, amnesic Alzheimer’s disease. Using multimodal neuroimaging, I demonstrate how degeneration of the angular gyrus in the left inferior parietal lobule, and its structural connections to the hippocampus, contribute to amnesic profiles in this syndrome. I finally evaluate these findings in the context of memory profiles in other posterior cortical neurodegenerative syndromes as well as recent theoretical models underscoring the importance of the parietal cortex in the integration and representation of episodic contextual information.
Orthogonal line attractors in the monkey frontoparietal cortex and RNNs support hierarchical decisions
COSYNE 2025
The posterior parietal cortex mediates serial dependence during visuospatial attention
COSYNE 2025
Electrical stimulation over the parietal cortex induces spatial bias by mediating the influence of visuospatial attention on the temporal dynamics of visuocortical processing
FENS Forum 2024
Posterior parietal cortex oscillatory activity shapes persistent spatial memory impairments induced by soluble amyloid-β oligomers
FENS Forum 2024
Touching what you see: Multisensory location coding in mouse posterior parietal cortex
FENS Forum 2024
Modeling Effects of Interrupting Parietal Cortex Neural Activity on Working Memory Limit
Neuromatch 5
parietal cortex coverage
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