scholarly journals Visual context modulates action perception in 10-month-old infants

2017 ◽  
Author(s):  
Cathleen Bache ◽  
Hannes Noack ◽  
Anne Springer ◽  
Waltraud Stadler ◽  
Franziska Kopp ◽  
...  
Keyword(s):  
Information Processing ◽  
Neural Activity ◽  
Alpha Activity ◽  
Visual Context ◽  
Action Perception ◽  
Early Action ◽  
Wide Range ◽  
Continuous Object ◽  
Situational Affordances ◽  
Tracking Behavior

AbstractResearch on early action perception has documented infants’ astounding abilities in tracking, predicting, and understanding other people’s actions. Common interpretations of previous findings tend to generalize across a wide range of action stimuli and contexts. In this study, ten-month-old infants repeatedly watched a video of a same-aged crawling baby that was transiently occluded. The video was presented in alternation with videos displaying visually either dissimilar movements (i.e., distorted human, continuous object, and distorted object movements) or similar movements (i.e., delayed or forwarded versions of the crawling video). Eye-tracking behavior and rhythmic neural activity, reflecting attention (posterior alpha), memory (frontal theta), and sensorimotor simulation (central alpha), were concurrently assessed. Results indicate that, when the very same movement was presented in a dissimilar context, it was tracked at more rear parts of the target and posterior alpha activity was elevated, suggesting higher demands on attention-controlled information processing. We conclude that early action perception is not immutable but shaped by the immediate visual context in which it appears, presumably reflecting infants’ ability to flexibly adjust stimulus processing to situational affordances.

Physical and Technological Limitations of Optical Information Processing and Computing

MRS Bulletin ◽  
1988 ◽  
Vol 13 (8) ◽  
pp. 36-41 ◽  
Author(s):  
Armand R. Tanguay
Keyword(s):  
Information Processing ◽  
Spread Spectrum ◽  
Beam Steering ◽  
Optical Processing ◽  
Robotic Vision ◽  
Wide Range ◽  
Simple Processing ◽  
Technological Barriers ◽  
Processing Architectures ◽  
Technological Limitations

Over the past four decades, the growth of information processing and computational capacity has been truly remarkable, paced to a large extent by equally remarkable progress in the integration and ultra-miniaturization of semiconductor devices. And yet it is becoming increasingly apparent that currently envisioned electronic processors and computers are rapidly approaching technological barriers that delimit processing speed, computational sophistication, and throughput per unit dissipated power. This realization has in turn led to intensive efforts to circumvent such bottlenecks through appropriate advances in processor architecture, multiprocessor distributed tasking, and software-defined algorithms.An alternative strategy that may yield significant computational enhancements for certain broad classes of problems involves the utilization of multidimensional optical components capable of modulating and/or redirecting information-carrying light wave-fronts. Such an optical processing or computing approach relies for its competitive advantage principally on massive parallelism in conjunction with relative ease of implementation of complex (weighted) interconnections among many (perhaps simple) processing elements. A wide range of computational problems exist that lend themselves quite naturally to optical processing architectures, including pattern recognition, earth resources data acquisition and analysis, texture discrimination, synthetic aperture radar (SAR) image formation, radar ambiguity function generation, spread spectrum identification and analysis, systolic array processing, phased array beam steering, and artificial (robotic) vision.

scholarly journals Omitted Variable Bias in GLMs of Neural Spiking Activity

2018 ◽  
Vol 30 (12) ◽  
pp. 3227-3258 ◽  
Author(s):  
Ian H. Stevenson
Keyword(s):  
Neural Activity ◽  
Single Neuron ◽  
Linear Models ◽  
Theory And Practice ◽  
Parameter Estimates ◽  
Omitted Variables ◽  
History Effects ◽  
Omitted Variable Bias ◽  
Wide Range ◽  
Variable Bias

Generalized linear models (GLMs) have a wide range of applications in systems neuroscience describing the encoding of stimulus and behavioral variables, as well as the dynamics of single neurons. However, in any given experiment, many variables that have an impact on neural activity are not observed or not modeled. Here we demonstrate, in both theory and practice, how these omitted variables can result in biased parameter estimates for the effects that are included. In three case studies, we estimate tuning functions for common experiments in motor cortex, hippocampus, and visual cortex. We find that including traditionally omitted variables changes estimates of the original parameters and that modulation originally attributed to one variable is reduced after new variables are included. In GLMs describing single-neuron dynamics, we then demonstrate how postspike history effects can also be biased by omitted variables. Here we find that omitted variable bias can lead to mistaken conclusions about the stability of single-neuron firing. Omitted variable bias can appear in any model with confounders—where omitted variables modulate neural activity and the effects of the omitted variables covary with the included effects. Understanding how and to what extent omitted variable bias affects parameter estimates is likely to be important for interpreting the parameters and predictions of many neural encoding models.

scholarly journals Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics

2017 ◽  
Vol 24 (3) ◽  
pp. 277-293 ◽  
Author(s):  
Selen Atasoy ◽  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Joel Pearson
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Building Blocks ◽  
Mental States ◽  
Brain Dynamics ◽  
Space And Time ◽  
Wide Range ◽  
The Brain

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at “rest.” Here, we introduce the concept of harmonic brain modes—fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; that is, connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal, and network-level changes in the brain across different mental states ( wakefulness, sleep, anesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal, and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

scholarly journals Functional but not obligatory link between microsaccades and neural modulation by covert spatial attention

2021 ◽  
Author(s):  
Baiwei Liu ◽  
Anna C Nobre ◽  
Freek van Ede
Keyword(s):  
Spatial Attention ◽  
Neural Activity ◽  
Alpha Activity ◽  
Spatial Modulation ◽  
Fixational Eye Movements ◽  
Neural Modulation ◽  
Gaze Behaviour ◽  
Eeg Alpha ◽  
Eeg Alpha Activity ◽  
Spatial Modulations

Covert spatial attention is associated with spatially specific modulation of neural activity as well as with directional biases in fixational eye-movements known as microsaccades. Recently, this link has been suggested to be obligatory, such that modulation of neural activity by covert spatial attention occurs only when paired with microsaccades toward the attended location. Here we revisited this link between microsaccades and neural modulation by covert spatial attention in humans. We investigated spatial modulation of 8-12 Hz EEG alpha activity and microsaccades in a context with no incentive for overt gaze behaviour: when attention is directed internally within the spatial layout of visual working memory. In line with a common attentional origin, we show that spatial modulations of alpha activity and microsaccades co-vary: alpha lateralisation is stronger in trials with microsaccades toward compared to away from the memorised location of the to-be-attended item and occurs earlier in trials with earlier microsaccades toward this item. Critically, however, trials without attention-driven microsaccades nevertheless showed clear spatial modulation of alpha activity - comparable to the neural modulation observed in trials with attention-driven microsaccades. Thus, directional biases in microsaccades are correlated with neural signatures of covert spatial attention, but they are not a prerequisite for neural modulation by covert spatial attention to be manifest.

scholarly journals Cell-free genetic devices confer autonomic and adaptive properties to hydrogels

2019 ◽  
Author(s):  
Colette J. Whitfield ◽  
Alice M. Banks ◽  
Gema Dura ◽  
John Love ◽  
Jonathan E. Fieldsend ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Synthesis ◽  
Information Processing ◽  
Synthetic Biology ◽  
Smart Materials ◽  
Living Organism ◽  
Chemical Diversity ◽  
Biological Information ◽  
Self Healing ◽  
Wide Range

AbstractSmart materials are able to alter one or more of their properties in response to defined stimuli. Our ability to design and create such materials, however, does not match the diversity and specificity of responses seen within the biological domain. We propose that relocation of molecular phenomena from living cells into hydrogels can be used to confer smart functionality to materials. We establish that cell-free protein synthesis can be conducted in agarose hydrogels, that gene expression occurs throughout the material and that co-expression of genes is possible. We demonstrate that gene expression can be controlled transcriptionally (using in gel gene interactions) and translationally in response to small molecule and nucleic acid triggers. We use this system to design and build a genetic device that can alter the structural property of its chassis material in response to exogenous stimuli. Importantly, we establish that a wide range of hydrogels are appropriate chassis for cell-free synthetic biology, meaning a designer may alter both the genetic and hydrogel components according to the requirements of a given application. We probe the relationship between the physical structure of the gel and in gel protein synthesis and reveal that the material itself may act as a macromolecular crowder enhancing protein synthesis. Given the extensive range of genetically encoded information processing networks in the living kingdom and the structural and chemical diversity of hydrogels, this work establishes a model by which cell-free synthetic biology can be used to create autonomic and adaptive materials.Significance statementSmart materials have the ability to change one or more of their properties (e.g. structure, shape or function) in response to specific triggers. They have applications ranging from light-sensitive sunglasses and drug delivery systems to shape-memory alloys and self-healing coatings. The ability to programme such materials, however, is basic compared to the ability of a living organism to observe, understand and respond to its environment. Here we demonstrate the relocation of biological information processing systems from cells to materials. We achieved this by operating small, programmable genetic devices outside the confines of a living cell and inside hydrogel matrices. These results establish a method for developing materials functionally enhanced with molecular machinery from biological systems.

scholarly journals The Cognitive Lens: a primer on conceptual tools for analysing information processing in developmental and regenerative morphogenesis

2019 ◽  
Vol 374 (1774) ◽  
pp. 20180369 ◽  
Author(s):  
Santosh Manicka ◽  
Michael Levin
Keyword(s):  
Information Processing ◽  
Evolutionary Biology ◽  
Biological Information ◽  
Neural Cells ◽  
Cognitive Architectures ◽  
Least Action ◽  
Mechanistic Investigation ◽  
Wide Range ◽  
Level Information ◽  
Scale Integration

Brains exhibit plasticity, multi-scale integration of information, computation and memory, having evolved by specialization of non-neural cells that already possessed many of the same molecular components and functions. The emerging field of basal cognition provides many examples of decision-making throughout a wide range of non-neural systems. How can biological information processing across scales of size and complexity be quantitatively characterized and exploited in biomedical settings? We use pattern regulation as a context in which to introduce the Cognitive Lens—a strategy using well-established concepts from cognitive and computer science to complement mechanistic investigation in biology. To facilitate the assimilation and application of these approaches across biology, we review tools from various quantitative disciplines, including dynamical systems, information theory and least-action principles. We propose that these tools can be extended beyond neural settings to predict and control systems-level outcomes, and to understand biological patterning as a form of primitive cognition. We hypothesize that a cognitive-level information-processing view of the functions of living systems can complement reductive perspectives, improving efficient top-down control of organism-level outcomes. Exploration of the deep parallels across diverse quantitative paradigms will drive integrative advances in evolutionary biology, regenerative medicine, synthetic bioengineering, cognitive neuroscience and artificial intelligence. This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.

scholarly journals Ballroom Dancing Promotes Neural Activity in the Sensorimotor System: A Resting-State fMRI Study

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Yingzhi Lu ◽  
Qi Zhao ◽  
Yingying Wang ◽  
Chenglin Zhou
Keyword(s):  
Functional Connectivity ◽  
Neural Activity ◽  
Resting State ◽  
Inferior Frontal Gyrus ◽  
Middle Temporal Gyrus ◽  
Sensorimotor System ◽  
Precentral Gyrus ◽  
Action Perception ◽  
Ballroom Dancing ◽  
Left Middle

Objective. This study aims at investigating differences in the spontaneous brain activity and functional connectivity in the sensorimotor system between ballroom dancers and nondancers, to further support the functional alteration in people with expertise. Materials and Methods. Twenty-three ballroom dancers and twenty-one matched novices with no dance experience were recruited in this study. Amplitude of low-frequency fluctuation (ALFF) and seed-based functional connectivity, as methods for assessing resting-state functional magnetic resonance imaging (rs-fMRI) data, were used to reveal the resting-state brain function in these participants. Results. Compared to the novices, ballroom dancers showed increased ALFF in the left middle temporal gyrus, bilateral precentral gyrus, bilateral inferior frontal gyrus, left postcentral gyrus, left inferior temporal gyrus, right middle occipital gyrus, right superior temporal gyrus, and left middle frontal gyrus. The ballroom dancers also demonstrated lower ALFF in the left lingual gyrus and altered functional connectivity between the inferior frontal gyrus and temporal, parietal regions. Conclusions. Our results indicated that ballroom dancers showed elevated neural activity in sensorimotor regions relative to novices and functional alterations in frontal-temporal and frontal-parietal connectivity, which may reflect specific training experience related to ballroom dancing, including high-capacity action perception, attentional control, and movement adjustment.

scholarly journals Intrinsic Adaptation in Autonomous Recurrent Neural Networks

2012 ◽  
Vol 24 (2) ◽  
pp. 523-540 ◽  
Author(s):  
Dimitrije Marković ◽  
Claudius Gros
Keyword(s):  
Neural Networks ◽  
Information Processing ◽  
Neural Activity ◽  
Recurrent Neural Networks ◽  
Activity Patterns ◽  
Self Organized ◽  
Dynamical Regimes ◽  
Autonomous State ◽  
Adaptation Processes ◽  
External Stimuli

A massively recurrent neural network responds on one side to input stimuli and is autonomously active, on the other side, in the absence of sensory inputs. Stimuli and information processing depend crucially on the qualia of the autonomous-state dynamics of the ongoing neural activity. This default neural activity may be dynamically structured in time and space, showing regular, synchronized, bursting, or chaotic activity patterns. We study the influence of nonsynaptic plasticity on the default dynamical state of recurrent neural networks. The nonsynaptic adaption considered acts on intrinsic neural parameters, such as the threshold and the gain, and is driven by the optimization of the information entropy. We observe, in the presence of the intrinsic adaptation processes, three distinct and globally attracting dynamical regimes: a regular synchronized, an overall chaotic, and an intermittent bursting regime. The intermittent bursting regime is characterized by intervals of regular flows, which are quite insensitive to external stimuli, interceded by chaotic bursts that respond sensitively to input signals. We discuss these findings in the context of self-organized information processing and critical brain dynamics.

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