scholarly journals Neural Excursions from Low-Dimensional Manifold Structure Explain Intersubject Variation in Human Motor Learning

2021 ◽  
Author(s):  
Corson N Areshenkoff ◽  
Daniel J Gale ◽  
Joe Y Nashed ◽  
Dominic Standage ◽  
John Randall Flanagan ◽  
...  
Keyword(s):  
Motor Learning ◽  
Large Scale ◽  
Brain Activity ◽  
Dimensional Subspace ◽  
Learning Performance ◽  
Dimensional Manifold ◽  
Learning Abilities ◽  
Manifold Structure ◽  
Electrophysiological Studies ◽  
Low Dimensional

Humans vary greatly in their motor learning abilities, yet little is known about the neural mechanisms that underlie this variability. Recent neuroimaging and electrophysiological studies demonstrate that large-scale neural dynamics inhabit a low-dimensional subspace or manifold, and that learning is constrained by this intrinsic manifold architecture. Here we asked, using functional MRI, whether subject-level differences in neural excursion from manifold structure can explain differences in learning across participants. We had subjects perform a sensorimotor adaptation task in the MRI scanner on two consecutive days, allowing us to assess their learning performance across days, as well as continuously measure brain activity. We find that the overall neural excursion from manifold activity in both cognitive and sensorimotor brain networks is associated with differences in subjects' patterns of learning and relearning across days. These findings suggest that off-manifold activity provides an index of the relative engagement of different neural systems during learning, and that intersubject differences in patterns of learning and relearning across days are related to reconfiguration processes in cognitive and sensorimotor networks during learning.

scholarly journals Low-Dimensional Dynamics of Brain Activity Associated with Manual Acupuncture in Healthy Subjects

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7432
Author(s):  
Xinmeng Guo ◽  
Jiang Wang
Keyword(s):  
Latent Variables ◽  
Large Scale ◽  
Scientific Explanation ◽  
Brain Activity ◽  
Dimensional Subspace ◽  
Oscillatory Activity ◽  
Manual Acupuncture ◽  
Brain Response ◽  
Acupuncture Stimulation ◽  
Low Dimensional

Acupuncture is one of the oldest traditional medical treatments in Asian countries. However, the scientific explanation regarding the therapeutic effect of acupuncture is still unknown. The much-discussed hypothesis it that acupuncture’s effects are mediated via autonomic neural networks; nevertheless, dynamic brain activity involved in the acupuncture response has still not been elicited. In this work, we hypothesized that there exists a lower-dimensional subspace of dynamic brain activity across subjects, underpinning the brain’s response to manual acupuncture stimulation. To this end, we employed a variational auto-encoder to probe the latent variables from multichannel EEG signals associated with acupuncture stimulation at the ST36 acupoint. The experimental results demonstrate that manual acupuncture stimuli can reduce the dimensionality of brain activity, which results from the enhancement of oscillatory activity in the delta and alpha frequency bands induced by acupuncture. Moreover, it was found that large-scale brain activity could be constrained within a low-dimensional neural subspace, which is spanned by the “acupuncture mode”. In each neural subspace, the steady dynamics of the brain in response to acupuncture stimuli converge to topologically similar elliptic-shaped attractors across different subjects. The attractor morphology is closely related to the frequency of the acupuncture stimulation. These results shed light on probing the large-scale brain response to manual acupuncture stimuli.

scholarly journals Six dimensions describe action understanding: the ACT-FASTaxonomy

2019 ◽  
Author(s):  
Mark Allen Thornton ◽  
Diana Tamir
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Dimensional Structure ◽  
Representational Space ◽  
Socially Relevant ◽  
Low Dimensional ◽  
Motor Actions ◽  
The Mind ◽  
Psychological Dimensions ◽  
Optimal Set

Humans engage in a wide variety of different actions and activities. These range from simple motor actions like reaching for an object, to complex activities like governing a nation. Navigating everyday life requires people to make sense of this diversity of actions. We suggest that the mind simplifies this complex domain by attending primarily to the most essential features of actions. Using a parsimonious set of action dimensions, the mind can organize action knowledge in a low-dimensional representational space. In nine studies, we derive and validate such an action taxonomy. Studies 1-3 use large-scale text analyses to generate and test potential action dimensions. Study 4 validates interpretable labels for these dimensions. Studies 5-7 demonstrate that these dimensions can explain human judgments about actions. We perform model selection on data from Studies 5-7 to arrive at the optimal set of six psychological dimensions, together forming the Abstraction, Creation, Tradition, Food, Animacy, Spiritualism Taxonomy (ACT-FAST). Study 8 demonstrates that ACT-FAST can predict socially relevant qualities of actions, including how, when, where, why, and by whom they are performed. Finally, Study 9 shows that ACT-FAST can explain action-related patterns of brain activity using naturalistic fMRI. Together, these studies reveal the dimensional structure the mind applies to organize action concepts.

scholarly journals Building Recognition on Subregion’s Multiscale Gist Feature Extraction and Corresponding Columns Information Based Dimensionality Reduction

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Bin Li ◽  
Wei Pang ◽  
Yuhao Liu ◽  
Xiangchun Yu ◽  
Anan Du ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Dimensional Subspace ◽  
Dimensional Manifold ◽  
Feature Maps ◽  
High Dimensions ◽  
Recognition Method ◽  
Second Stage ◽  
Building Recognition ◽  
Reduction Methods ◽  
Low Dimensional

In this paper, we proposed a new building recognition method named subregion’s multiscale gist feature (SM-gist) extraction and corresponding columns information based dimensionality reduction (CCI-DR). Our proposed building recognition method is presented as a two-stage model: in the first stage, a building image is divided into 4 × 5 subregions, and gist vectors are extracted from these regions individually. Then, we combine these gist vectors into a matrix with relatively high dimensions. In the second stage, we proposed CCI-DR to project the high dimensional manifold matrix to low dimensional subspace. Compared with the previous building recognition method the advantages of our proposed method are that (1) gist features extracted by SM-gist have the ability to adapt to nonuniform illumination and that (2) CCI-DR can address the limitation of traditional dimensionality reduction methods, which convert gist matrices into vectors and thus mix the corresponding gist vectors from different feature maps. Our building recognition method is evaluated on the Sheffield buildings database, and experiments show that our method can achieve satisfactory performance.

Coherency Identification in Large-Scale Power System Using Measured Data

2013 ◽  
Vol 448-453 ◽  
pp. 2428-2433
Author(s):  
Xiao Dong Li ◽  
Ji Nan Zhang ◽  
Peng Li ◽  
Hong Jie Jia ◽  
Tao Jiang
Keyword(s):  
Power System ◽  
Large Scale ◽  
Oscillation Mode ◽  
Projection Pursuit ◽  
Dimensional Subspace ◽  
Measured Data ◽  
Model Parameters ◽  
Low Dimensional ◽  
Real Time Identification ◽  
The Impact

This paper presents a new method to identify coherent generator groups in power system based on projection pursuit. Projection pursuit algorithm is introduced to model wide-area measured time series and analyses high-dimensional data in low-dimensional subspace. It could seek and extract key projection vectors reflecting generator coherent features and identify the coherency of generators according to projection directions of generators. The presented technique could realize real-time identification of coherent generators, in which grouping is based on measured data avoiding the impact of model parameters. It proves that the composition of principal components has corresponding relationship with system oscillation mode. Finally, China Southern Power Grid is used as testing system to verify the feasibility and effectiveness of the method.

scholarly journals Online Subspace Tracking of Sensors Data for Damage Propagation Modeling and Predictive Analytics

2019 ◽  
Author(s):  
Farhan Khan
Keyword(s):  
Remaining Useful Life ◽  
Propagation Model ◽  
Dimensional Manifold ◽  
Subspace Tracking ◽  
Static Behavior ◽  
Damage Propagation ◽  
Propagation Modeling ◽  
Manifold Structure ◽  
Low Dimensional ◽  
Low Dimensional Manifold

We analyze damage propagation modeling of turbo-engines in a data-driven approach. We investigate subspace tracking assuming a low dimensional manifold structure and a static behavior during the healthy state of the machines. Our damage propagation model is based on the deviation of the data from the static behavior and uses the notion of health index as a measure of the condition. Hence, we incorporate condition-based maintenance and estimate the remaining useful life based on the current and previous health indexes. This paper proposes an algorithm that adapts well to the dynamics of the data and underlying system, and reduces the computational complexity by utilizing the low dimensional manifold structure of the data. A significant performance improvement is demonstrated over existing methods by using the proposed algorithm on CMAPSS Turbo-engine datasets.

scholarly journals Minimum Eigenvector Collaborative Representation Discriminant Projection for Feature Extraction

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4778
Author(s):  
Haoshuang Hu ◽  
Da-Zheng Feng
Keyword(s):  
Feature Extraction ◽  
Training Sample ◽  
Dimensional Subspace ◽  
Collaborative Representation ◽  
High Dimensional ◽  
Dimensional Manifold ◽  
Zero Eigenvalue ◽  
Sample Covariance ◽  
Low Dimensional ◽  
Discriminant Projection

High-dimensional signals, such as image signals and audio signals, usually have a sparse or low-dimensional manifold structure, which can be projected into a low-dimensional subspace to improve the efficiency and effectiveness of data processing. In this paper, we propose a linear dimensionality reduction method—minimum eigenvector collaborative representation discriminant projection—to address high-dimensional feature extraction problems. On the one hand, unlike the existing collaborative representation method, we use the eigenvector corresponding to the smallest non-zero eigenvalue of the sample covariance matrix to reduce the error of collaborative representation. On the other hand, we maintain the collaborative representation relationship of samples in the projection subspace to enhance the discriminability of the extracted features. Also, the between-class scatter of the reconstructed samples is used to improve the robustness of the projection space. The experimental results on the COIL-20 image object database, ORL, and FERET face databases, as well as Isolet database demonstrate the effectiveness of the proposed method, especially in low dimensions and small training sample size.

scholarly journals Finding Approximate POMDP solutions Through Belief Compression

2005 ◽  
Vol 23 ◽  
pp. 1-40 ◽  
Author(s):  
N. Roy ◽  
G. Gordon ◽  
S. Thrun
Keyword(s):  
Optimal Policy ◽  
Large Scale ◽  
Value Function ◽  
Dimensional Space ◽  
Good Control ◽  
Dimensional Subspace ◽  
Mobile Robot Navigation ◽  
High Dimensional ◽  
Markov Decision ◽  
Low Dimensional

Standard value function approaches to finding policies for Partially Observable Markov Decision Processes (POMDPs) are generally considered to be intractable for large models. The intractability of these algorithms is to a large extent a consequence of computing an exact, optimal policy over the entire belief space. However, in real-world POMDP problems, computing the optimal policy for the full belief space is often unnecessary for good control even for problems with complicated policy classes. The beliefs experienced by the controller often lie near a structured, low-dimensional subspace embedded in the high-dimensional belief space. Finding a good approximation to the optimal value function for only this subspace can be much easier than computing the full value function. We introduce a new method for solving large-scale POMDPs by reducing the dimensionality of the belief space. We use Exponential family Principal Components Analysis (Collins, Dasgupta & Schapire, 2002) to represent sparse, high-dimensional belief spaces using small sets of learned features of the belief state. We then plan only in terms of the low-dimensional belief features. By planning in this low-dimensional space, we can find policies for POMDP models that are orders of magnitude larger than models that can be handled by conventional techniques. We demonstrate the use of this algorithm on a synthetic problem and on mobile robot navigation tasks.

scholarly journals Learning physics-based models from data: perspectives from inverse problems and model reduction

Acta Numerica ◽  
2021 ◽  
Vol 30 ◽  
pp. 445-554
Author(s):  
Omar Ghattas ◽  
Karen Willcox
Keyword(s):  
Inverse Problems ◽  
Model Reduction ◽  
Large Scale ◽  
Dimensional Subspace ◽  
Dimensional Solution ◽  
Scale Models ◽  
Recent Developments ◽  
Engineered Systems ◽  
Low Dimensional ◽  
Solution Manifold

This article addresses the inference of physics models from data, from the perspectives of inverse problems and model reduction. These fields develop formulations that integrate data into physics-based models while exploiting the fact that many mathematical models of natural and engineered systems exhibit an intrinsically low-dimensional solution manifold. In inverse problems, we seek to infer uncertain components of the inputs from observations of the outputs, while in model reduction we seek low-dimensional models that explicitly capture the salient features of the input–output map through approximation in a low-dimensional subspace. In both cases, the result is a predictive model that reflects data-driven learning yet deeply embeds the underlying physics, and thus can be used for design, control and decision-making, often with quantified uncertainties. We highlight recent developments in scalable and efficient algorithms for inverse problems and model reduction governed by large-scale models in the form of partial differential equations. Several illustrative applications to large-scale complex problems across different domains of science and engineering are provided.

scholarly journals The dynamic basis of cognition: an integrative core under the control of the ascending neuromodulatory system

2018 ◽  
Author(s):  
J.M. Shine ◽  
M. Breakspear ◽  
P.T. Bell ◽  
K. Ehgoetz Martens ◽  
R. Shine ◽  
...  
Keyword(s):  
Human Brain ◽  
Neural Activity ◽  
Large Scale ◽  
System Level ◽  
Dimensional Manifold ◽  
Brain Organization ◽  
Human Connectome Project ◽  
Functional Brain Organization ◽  
Low Dimensional ◽  
Dynamic Execution

AbstractThe human brain integrates diverse cognitive processes into a coherent whole, shifting fluidly as a function of changing environmental demands. Despite recent progress, the neurobiological mechanisms responsible for this dynamic system-level integration remain poorly understood. Here, we used multi-task fMRI data from the Human Connectome Project to examine the spatiotemporal architecture of cognition in the human brain. By investigating the spatial, dynamic and molecular signatures of system-wide neural activity across a range of cognitive tasks, we show that large-scale neuronal activity converges onto a low dimensional manifold that facilitates the dynamic execution of diverse task states. Flow within this attractor space is associated with dissociable cognitive functions, and with unique patterns of network-level topology and information processing complexity. The axes of the low-dimensional neurocognitive architecture align with regional differences in the density of neuromodulatory receptors, which in turn relate to distinct signatures of network controllability estimated from the structural connectome. These results advance our understanding of functional brain organization by emphasizing the interface between low dimensional neural activity, network topology, neuromodulatory systems and cognitive function.One Sentence SummaryA diverse set of neuromodulators facilitates the formation of a dynamic, low-dimensional integrative core in the brain that is recruited by diverse cognitive demands

scholarly journals Low Dimensional Activity in Spiking Neuronal Networks

2017 ◽  
Author(s):  
Emil Wärnberg ◽  
Arvind Kumar
Keyword(s):  
Neural Activity ◽  
Degrees Of Freedom ◽  
Neuronal Networks ◽  
Brain Activity ◽  
A Priori ◽  
Network Activity ◽  
Connectivity Pattern ◽  
Dimensional Manifold ◽  
Low Dimensional

AbstractSeveral recent studies have shown that neural activity in vivo tends to be constrained to a low-dimensional manifold. Such activity does not arise in simulated neural networks with homogeneous connectivity and it has been suggested that it is indicative of some other connectivity pattern in neuronal networks. Surprisingly, the structure of the intrinsic manifold of the network activity puts constraints on learning. For instance, animals find it difficult to perform tasks that may require a change in the intrinsic manifold. Here, we demonstrate that the Neural Engineering Framework (NEF) can be adapted to design a biologically plausible spiking neuronal network that exhibit low dimensional activity. Consistent with experimental observations, the resulting synaptic weight distribution is heavy-tailed (log-normal). In our model, a change in the intrinsic manifold of the network activity requires rewiring of the whole network, which may be either not possible or a very slow process. This observation provides an explanation of why learning is easier when it does not require the neural activity to leave its intrinsic manifold.Significance statementA network in the brain consists of thousands of neurons. A priori, we expect that the network will have as many degrees of freedom as its number of neurons. Surprisingly, experimental evidence suggests that local brain activity is confined to a space spanned by 10 variables. Here, we describe an approach to construct spiking neuronal networks that exhibit low-dimensional activity and address the question: how the intrinsic dimensionality of the network activity restricts the learning as suggested by recent experiments? Specifically, we show that tasks that requires animals to change the network activity outside the intrinsic space would entail large changes in the neuronal connectivity, and therefore, animals are either slow or not able to acquire such tasks.

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