Posterior parietal cortex

A prevailing theory in goal-directed decision-making is that the brain accumulates evidence over time to make perceptual decisions. However, even after two decades of studies on evidence accumulation, it is still unclear how neuronal populations cooperate to dynamically represent decision variables. This is because of a lack of tools for gaining experimental control over variables of interest to test their relationship to behavior. To address this gap, we develop a state-space control framework to causally interact with decision-related latent variables. To expose decision variables to target

Many behaviors demand a balance between exploitation, which requires reliability, and exploration, which involves flexibility. In cortical circuits, much work has emphasized how attractor dynamics generate reliable behavior through the convergence of a network to specialized activity patterns for specific decisions and actions1. Other work has emphasized how cortical activity exhibits variability across trials of a behavior, which could in principle support flexibility in actions2. Here, through multi-area calcium imaging and recurrent neural network models, we propose that cortical activity f

Previous research suggests that electrical stimulation over posterior parietal cortex (PPC) can modulate visual cognition that requires selective attention, yet the specific neural processes affected remain unclear. We hypothesized that tDCS over the PPC influenced the distribution of visuospatial attention by mediating the way selective attention wrap visuocortical processing. We tested this hypothesis using unilateral transcranial direct-current stimulation (tDCS) to the PPC of male and female human volunteers before we recorded electroencephalography (EEG) and collected psychophysical data.

…studies, mainly on the motor cortex. Our goal was to assess if visuospatial coding affects the semantic comprehension of space-related words differently for peripersonal and extrapersonal space, and the causal role of the posterior parietal cortex (PPC).Methods: Utilizing Unity3D and Oculus Rift S, a virtual reality setup displayed 3D words at peripersonal (60 cm) and extrapersonal (120 cm) space. Participant

…in mouse somatosensory (S1) cortex and posterior parietal cortex (PPC) while mice were learning a multisensory texture discrimination task. Mice were trained to discriminate two textures, each preceded by a distinct au

…neural regions, such as the posterior parietal cortex (PPC). The PPC, intricately linked with the hippocampus and essential to memory functions, particularly spatial memory, is vital for decoding early symptoms and the pr

Perceptual memories store our experiences; they are the basis for our understanding the external world and guiding our decisions. Despite fast-paced research in this field, behavioral and cognitive constructs are often custom built around the investigators’ preferred task, and general principles across tasks are still missing. To address these issues, we performed psychophysical experiments aiming to build a computational model comprised of interconnected functional units, each performing a specific task-independent operation. The interaction between units and the readout of the system can be

…primary whisker somatosensory cortex (wS1) whereas visual signals arrive in the retinotopically organized primary visual cortex (V1). The posterior parietal cortex (PPC) in-between is a candidate cortical region for merging these two representations. However, how converging visual and tactile inputs of nearby objects are processed in these cortical areas remains unclear. To address t

Knowledge of the timing of events is critical for efficient behaviour. Previous work examining the formation of expectations following temporally predictable stimulus sequences has revealed that humans are able to extract regularities, which can improve performance. To determine the underlying neural circuits that drive temporal attention, we established an equivalent behavioural paradigm for mice. We randomize trials with either a long or short delay between a cue and target stimulus, where one delay type occurs with a higher frequency. This allowed for the formation of expectations based on

The central tendency bias, or contraction bias, is a phenomenon where the judgment of the magnitude of items held in working memory (WM) is biased towards the average of past observations. It is assumed to be an optimal strategy by the brain, and commonly thought of an expression of the brain’s ability to learn the statistical structure of sensory inputs. On the other hand, recency biases such as serial dependence are also commonly observed, and are thought to reflect the content of WM. Recent results from an auditory delayed comparison task in rats, suggest that both biases may be more relate

Generic neural networks optimized for task performance are often successful in predicting neural activity in animals. However, neural mechanisms dictate not only the task performance but also how a particular task is solved. Can we deduce mechanisms from cognitive strategies? To find out, we asked humans and monkeys to perform a challenging task in which they steered to a remembered goal location by integrating self-motion in a virtual environment lacking position cues. Although this task requires only mentally tracking one’s position relative to the goal, subjects physically tracked this late

…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 diffe

Recent studies in flies and rodents have shown that animals switch between a small number of ‘behavioral states’ during decision-making. However, the majority of studies infer these state switches only from behavioral measurements such as choices or movements. To provide greater insight into the internal processes that guide these switches, we developed a method for identifying state changes from behavioral measurements and neural responses. We applied this method to an evidence accumulation task studied in rats. Our method models trial-to-trial state switches with a hidden Markov model (HMM)

Decisions are often guided by detecting subtle signals in a dynamic sensory environment. Although the brain must track such decision-relevant signals, how they are represented and transformed by neural activity across the sensorimotor axis remains poorly understood. Here, we recorded neural activity with Neuropixels probes across dozens of brain regions while mice performed a visual change-detection task. We trained mice to detect a sustained increase in temporal-frequency (TF) of a drifting grating stimulus, whose speed fluctuates stochastically around the mean of 1Hz. The task requires mice

…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 pr