Rhythms for Cognition: Communication through Coherence

Neuron, Год журнала: 2015, Номер 88(1), С. 220 - 235

Опубликована: Окт. 1, 2015

Язык: Английский

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A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls

Frontiers in Systems Neuroscience, Год журнала: 2016, Номер 9

Опубликована: Янв. 8, 2016

Oscillatory neuronal activity may provide a mechanism for dynamic network coordination. Rhythmic interactions can be quantified using multiple metrics, each with their own advantages and disadvantages. This tutorial will review summarize current analysis methods used in the field of invasive non-invasive electrophysiology to study connections between populations. First, we metrics functional connectivity, including coherence, phase synchronization, phase-slope index, Granger causality, specific aim an intuition how these work, as well quantitative definition. Next, highlight number interpretational caveats common pitfalls that arise when performing connectivity analysis, reference problem, signal noise ratio volume conduction input trial sample size bias problem. These illustrated by presenting set MATLAB-scripts, which executed reader simulate potential problems. We discuss issues addressed methods.

Язык: Английский

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Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex

Proceedings of the National Academy of Sciences, Год журнала: 2014, Номер 111(40), С. 14332 - 14341

Опубликована: Сен. 9, 2014

Significance One of the main unresolved questions in cognitive neuroscience is how low-level and high-level areas visual cortex interact with each other during perception cognition. We investigated whether cortical oscillations can be used to distinguish feedforward from feedback processing. studied propagation α- γ-oscillations through layers between different areas. induced microstimulation influenced them using a pharmacological approach. The results these experiments demonstrate that propagate direction, whereas α-oscillations direction. conclude high- low-frequency provide markers processing, respectively.

Язык: Английский

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Conscious Processing and the Global Neuronal Workspace Hypothesis

Neuron, Год журнала: 2020, Номер 105(5), С. 776 - 798

Опубликована: Март 1, 2020

Язык: Английский

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Granger Causality Analysis in Neuroscience and Neuroimaging

Journal of Neuroscience, Год журнала: 2015, Номер 35(8), С. 3293 - 3297

Опубликована: Фев. 25, 2015

### Introduction A key challenge in neuroscience and, particular, neuroimaging, is to move beyond identification of regional activations toward the characterization functional circuits underpinning perception, cognition, behavior, and consciousness. Granger causality (G-causality) analysis

Язык: Английский

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Active interoceptive inference and the emotional brain

Philosophical Transactions of the Royal Society B Biological Sciences, Год журнала: 2016, Номер 371(1708), С. 20160007 - 20160007

Опубликована: Окт. 11, 2016

We review a recent shift in conceptions of interoception and its relationship to hierarchical inference the brain. The notion interoceptive means that bodily states are regulated by autonomic reflexes enslaved descending predictions from deep generative models our internal external milieu. This re-conceptualization illuminates several issues cognitive clinical neuroscience with implications for experiences selfhood emotion. first contextualize terms active (Bayesian) brain, highlighting enactivist (embodied) aspects. then consider key role uncertainty or precision how this might translate into neuromodulation. next examine understanding functional anatomy emotional surveying observations on agranular cortex. Finally, we turn theoretical issues, namely, shaping sense embodied self feelings. will draw links between physiological homoeostasis allostasis, early cybernetic ideas predictive control processing. explanatory scope ranges explanations autism depression, through consciousness. offer brief survey these exciting developments. article is part themed issue ‘Interoception beyond homeostasis: affect, cognition mental health’.

Язык: Английский

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The Predictive Coding Account of Psychosis

Biological Psychiatry, Год журнала: 2018, Номер 84(9), С. 634 - 643

Опубликована: Май 25, 2018

Fueled by developments in computational neuroscience, there has been increasing interest the underlying neurocomputational mechanisms of psychosis. One successful approach involves predictive coding and Bayesian inference. Here, inferences regarding current state world are made combining prior beliefs with incoming sensory signals. Mismatches between signals constitute prediction errors that drive new learning. Psychosis suggested to result from a decreased precision encoding relative data, thereby garnering maladaptive inferences. we review evidence for aberrant discuss challenges this canonical account For example, hallucinations delusions may relate distinct alterations coding, despite their common co-occurrence. More broadly, some studies implicate weakened psychosis, others find stronger priors. These might be answered more nuanced view coding. Different priors specified different modalities integration, deficits each modality need not uniform. Furthermore, hierarchical organization critical. Altered processes at lower levels hierarchy linearly related higher (and vice versa). Finally, theories do highlight active inference—the process through which effects our actions on sensations anticipated minimized. It is possible conflicting findings reconciled considering these complexities, portending framework psychosis equipped deal its many manifestations.

Язык: Английский

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Alpha-Beta and Gamma Rhythms Subserve Feedback and Feedforward Influences among Human Visual Cortical Areas

Neuron, Год журнала: 2016, Номер 89(2), С. 384 - 397

Опубликована: Янв. 1, 2016

Язык: Английский

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Working Memory 2.0

Neuron, Год журнала: 2018, Номер 100(2), С. 463 - 475

Опубликована: Окт. 1, 2018

Язык: Английский

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Toward an Integration of Deep Learning and Neuroscience

Frontiers in Computational Neuroscience, Год журнала: 2016, Номер 10

Опубликована: Сен. 14, 2016

Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial networks tend to eschew precisely designed or circuits in favor brute force optimization a cost function, often using simple relatively uniform initial architectures. Two recent developments have emerged within learning that create an opportunity connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion various forms short- long-term memory storage. Second, functions training procedures become more complex varied across layers over time. Here we think about brain terms ideas. We hypothesize (1) optimizes functions, (2) diverse differ locations development, (3) operates pre-structured architecture matched computational problems posed by behavior. Such heterogeneously optimized system, enabled series interacting serves make data-efficient targeted needs organism. suggest directions which neuroscience could seek refine test hypotheses.

Язык: Английский

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