382 research outputs found

    The cognitive neuroscience of visual working memory

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    Visual working memory allows us to temporarily maintain and manipulate visual information in order to solve a task. The study of the brain mechanisms underlying this function began more than half a century ago, with Scoville and Milner’s (1957) seminal discoveries with amnesic patients. This timely collection of papers brings together diverse perspectives on the cognitive neuroscience of visual working memory from multiple fields that have traditionally been fairly disjointed: human neuroimaging, electrophysiological, behavioural and animal lesion studies, investigating both the developing and the adult brain

    Amodal processing in human prefrontal cortex

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    Information enters the cortex via modality-specific sensory regions, whereas actions are produced by modality-specific motor regions. Intervening central stages of information processing map sensation to behavior. Humans perform this central processing in a flexible, abstract manner such that sensory information in any modality can lead to response via any motor system. Cognitive theories account for such flexible behavior by positing amodal central information processing (e. g., "central executive," Baddeley and Hitch, 1974; "supervisory attentional system," Norman and Shallice, 1986; "response selection bottleneck," Pashler, 1994). However, the extent to which brain regions embodying central mechanisms of information processing are amodal remains unclear. Here we apply multivariate pattern analysis to functional magnetic resonance imaging (fMRI) data to compare response selection, a cognitive process widely believed to recruit an amodal central resource across sensory and motor modalities. We show that most frontal and parietal cortical areas known to activate across a wide variety of tasks code modality, casting doubt on the notion that these regions embody a central processor devoid of modality representation. Importantly, regions of anterior insula and dorsolateral prefrontal cortex consistently failed to code modality across four experiments. However, these areas code at least one other task dimension, process (instantiated as response selection vs response execution), ensuring that failure to find coding of modality is not driven by insensitivity of multivariate pattern analysis in these regions. We conclude that abstract encoding of information modality is primarily a property of subregions of the prefrontal cortex

    A hierarchical system for a distributed representation of the peripersonal space of a humanoid robot

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    Reaching a target object in an unknown and unstructured environment is easily performed by human beings. However, designing a humanoid robot that executes the same task requires the implementation of complex abilities, such as identifying the target in the visual field, estimating its spatial location, and precisely driving the motors of the arm to reach it. While research usually tackles the development of such abilities singularly, in this work we integrate a number of computational models into a unified framework, and demonstrate in a humanoid torso the feasibility of an integrated working representation of its peripersonal space. To achieve this goal, we propose a cognitive architecture that connects several models inspired by neural circuits of the visual, frontal and posterior parietal cortices of the brain. The outcome of the integration process is a system that allows the robot to create its internal model and its representation of the surrounding space by interacting with the environment directly, through a mutual adaptation of perception and action. The robot is eventually capable of executing a set of tasks, such as recognizing, gazing and reaching target objects, which can work separately or cooperate for supporting more structured and effective behaviors

    A hierarchical system for a distributed representation of the peripersonal space of a humanoid robot

    Get PDF
    Reaching a target object in an unknown and unstructured environment is easily performed by human beings. However, designing a humanoid robot that executes the same task requires the implementation of complex abilities, such as identifying the target in the visual field, estimating its spatial location, and precisely driving the motors of the arm to reach it. While research usually tackles the development of such abilities singularly, in this work we integrate a number of computational models into a unified framework, and demonstrate in a humanoid torso the feasibility of an integrated working representation of its peripersonal space. To achieve this goal, we propose a cognitive architecture that connects several models inspired by neural circuits of the visual, frontal and posterior parietal cortices of the brain. The outcome of the integration process is a system that allows the robot to create its internal model and its representation of the surrounding space by interacting with the environment directly, through a mutual adaptation of perception and action. The robot is eventually capable of executing a set of tasks, such as recognizing, gazing and reaching target objects, which can work separately or cooperate for supporting more structured and effective behaviors

    Single-trial correlates of decision-making in dorsal premotor and primary motor cortices

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    Everyday, as we move through the world we seamlessly make hundreds of per- ceptual decisions often without fully appreciating the underlying fast and precise computations going on in our brains. A fundamental question in the decision-making eld is to understand how and where in the brain information is combined to form decisions that lead to appropriate responses.(...

    Learning and adaptation in brain machine interfaces

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    Balancing subject learning and decoder adaptation is central to increasing brain machine interface (BMI) performance. We addressed these complementary aspects in two studies: (1) a learning study, in which mice modulated “beta” band activity to control a 1D auditory cursor, and (2) an adaptive decoding study, in which a simple recurrent artificial neural network (RNN) decoded intended saccade targets of monkeys. In the learning study, three mice successfully increased beta band power following trial initiations, and specifically increased beta burst durations from 157 ms to 182 ms, likely contributing to performance. Though the task did not explicitly require specific movements, all three mice appeared to modulate beta activity via active motor control and had consistent vibrissal motor cortex multiunit activity and local field potential relationships with contralateral whisker pad electromyograms. The increased burst durations may therefore by a direct result of increased motor activity. These findings suggest that only a subset of beta rhythm phenomenology can be volitionally modulated (e.g. the tonic “hold” beta), therefore limiting the possible set of successful beta neuromodulation strategies. In the adaptive decoding study, RNNs decoded delay period activity in oculomotor and working memory regions while monkeys performed a delayed saccade task. Adaptive decoding sessions began with brain-controlled trials using pre-trained RNN models, in contrast to static decoding sessions in which 300-500 initial eye-controlled training trials were performed. Closed loop RNN decoding performance was lower than predicted by offline simulations. More consistent delay period activity and saccade paths across trials were associated with higher decoding performance. Despite the advantage of consistency, one monkey’s delay period activity patterns changed over the first week of adaptive decoding, and the other monkey’s saccades were more erratic during adaptive decoding than during static decoding sessions. It is possible that the altered session paradigm eliminating eye-controlled training trials led to either frustration or exploratory learning, causing the neural and behavioral changes. Considering neural control and decoder adaptation of BMIs in these studies, future work should improve the “two-learner” subject-decoder system by better modeling the interaction between underlying brain states (and possibly their modulation) and the neural signatures representing desired outcomes

    Neural Network Dynamics of Visual Processing in the Higher-Order Visual System

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    Vision is one of the most important human senses that facilitate rich interaction with the external environment. For example, optimal spatial localization and subsequent motor contact with a specific physical object amongst others requires a combination of visual attention, discrimination, and sensory-motor coordination. The mammalian brain has evolved to elegantly solve this problem of transforming visual input into an efficient motor output to interact with an object of interest. The frontal and parietal cortices are two higher-order (i.e. processes information beyond simple sensory transformations) brain areas that are intimately involved in assessing how an animal’s internal state or prior experiences should influence cognitive-behavioral output. It is well known that activity within each region and functional interactions between both regions are correlated with visual attention, decision-making, and memory performance. Therefore, it is not surprising that impairment in the fronto-parietal circuit is often observed in many psychiatric disorders. Network- and circuit-level fronto-parietal involvement in sensory-based behavior is well studied; however, comparatively less is known about how single neuron activity in each of these areas can give rise to such macroscopic activity. The goal of the studies in this dissertation is to address this gap in knowledge through simultaneous recordings of cellular and population activity during sensory processing and behavioral paradigms. Together, the combined narrative builds on several themes in neuroscience: variability of single cell function, population-level encoding of stimulus properties, and state and context-dependent neural dynamics.Doctor of Philosoph
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