Prefrontal neural dynamics for behavioral decisions and attentional control

ABSTRACTComplex neural dynamics in the prefrontal cortex contribute to context-dependent decisions and attentional competition. To analyze these dynamics, we apply demixed principal component analysis to activity of a primate prefrontal cell sample recorded in a cued target detection task. The results track dynamics of cue and object coding, feeding into movements along a target present-absent decision axis in a low-dimensional subspace of population activity. For a single stimulus, object and cue coding are seen mainly in the contralateral hemisphere. Later, a developing decision code in both hemispheres may reflect interhemispheric communication. With a target in one hemifield and a competing distractor in the other, each hemisphere initially encodes the contralateral object, but finally, decision coding is dominated by the task-relevant target. Tracking complex neural events in a low-dimensional activity subspace illuminates information flow towards task-appropriate behavior, unravelling mechanisms of prefrontal computation.

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Multiscale low-dimensional motor cortical state dynamics predict naturalistic reach-and-grasp behavior

Nature Communications ◽

10.1038/s41467-020-20197-x ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Hamidreza Abbaspourazad ◽

Mahdi Choudhury ◽

Yan T. Wong ◽

Bijan Pesaran ◽

Maryam M. Shanechi

Keyword(s):

Neural Control ◽

Multiple Scales ◽

Network Activity ◽

Neural Dynamics ◽

Population Activity ◽

Reach And Grasp ◽

Spatiotemporal Scales ◽

Motor Cortical ◽

Low Dimensional ◽

Low Dimensional Dynamics

AbstractMotor function depends on neural dynamics spanning multiple spatiotemporal scales of population activity, from spiking of neurons to larger-scale local field potentials (LFP). How multiple scales of low-dimensional population dynamics are related in control of movements remains unknown. Multiscale neural dynamics are especially important to study in naturalistic reach-and-grasp movements, which are relatively under-explored. We learn novel multiscale dynamical models for spike-LFP network activity in monkeys performing naturalistic reach-and-grasps. We show low-dimensional dynamics of spiking and LFP activity exhibited several principal modes, each with a unique decay-frequency characteristic. One principal mode dominantly predicted movements. Despite distinct principal modes existing at the two scales, this predictive mode was multiscale and shared between scales, and was shared across sessions and monkeys, yet did not simply replicate behavioral modes. Further, this multiscale mode’s decay-frequency explained behavior. We propose that multiscale, low-dimensional motor cortical state dynamics reflect the neural control of naturalistic reach-and-grasp behaviors.

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Object Tracking Using Probabilistic Principal Component Analysis Based on Particle Filtering Framework

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.341-342.790 ◽

2011 ◽

Vol 341-342 ◽

pp. 790-797 ◽

Cited By ~ 2

Author(s):

Zhi Yan Xiang ◽

Tie Yong Cao ◽

Peng Zhang ◽

Tao Zhu ◽

Jing Feng Pan

Keyword(s):

Principal Component Analysis ◽

Object Tracking ◽

Particle Filtering ◽

Principal Component ◽

Component Analysis ◽

Dimensional Subspace ◽

Target Object ◽

Scale Invariant ◽

Low Dimensional ◽

Probabilistic Principal Component Analysis

In this paper, an object tracking approach is introduced for color video sequences. The approach presents the integration of color distributions and probabilistic principal component analysis (PPCA) into particle filtering framework. Color distributions are robust to partial occlusion, are rotation and scale invariant and are calculated efficiently. Principal Component Analysis (PCA) is used to update the eigenbasis and the mean, which can reflect the appearance changes of the tracked object. And a low dimensional subspace representation of PPCA efficiently adapts to these changes of appearance of the target object. At the same time, a forgetting factor is incorporated into the updating process, which can be used to economize on processing time and enhance the efficiency of object tracking. Computer simulation experiments demonstrate the effectiveness and the robustness of the proposed tracking algorithm when the target object undergoes pose and scale changes, defilade and complex background.

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SUBSPACE-BASED FACE RECOGNITION: OUTLIER DETECTION AND A NEW DISTANCE CRITERION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001405004174 ◽

2005 ◽

Vol 19 (04) ◽

pp. 479-493 ◽

Cited By ~ 1

Author(s):

PEI CHEN ◽

DAVID SUTER

Keyword(s):

Face Recognition ◽

Outlier Detection ◽

Principal Component ◽

Dimensional Subspace ◽

Recognition Algorithm ◽

New Techniques ◽

Face Images ◽

The Face ◽

Low Dimensional ◽

New Strategies

Illumination effects, including shadows and varying lighting, make the problem of face recognition challenging. Experimental and theoretical results show that the face images under different illumination conditions approximately lie in a low-dimensional subspace, hence principal component analysis (PCA) or low-dimensional subspace techniques have been used. Following this spirit, we propose new techniques for the face recognition problem, including an outlier detection strategy (mainly for those points not following the Lambertian reflectance model), and a new error criterion for the recognition algorithm. Experiments using the Yale-B face database show the effectiveness of the new strategies.

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Frequency-separated principal component analysis of cortical population activity

Journal of Neurophysiology ◽

10.1152/jn.00167.2020 ◽

2020 ◽

Vol 124 (3) ◽

pp. 668-681

Author(s):

Jean-Philippe Thivierge

Keyword(s):

Principal Component Analysis ◽

Visual Cortex ◽

Sensory Input ◽

Low Frequency ◽

Principal Component ◽

Component Analysis ◽

Population Activity ◽

Standard Principal Component Analysis ◽

Specific Frequency ◽

Low Dimensional

A method termed frequency-separated principal component analysis (FS-PCA) is introduced for analyzing populations of simultaneously recorded neurons. This framework extends standard principal component analysis by extracting components of activity delimited to specific frequency bands. FS-PCA revealed that circuits of the primary visual cortex generate a broad range of components dominated by low-frequency activity. Furthermore, low-dimensional fluctuations in population activity modulated the response of individual neurons to sensory input.

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Elucidating the neural mechanisms of Learning-to-Learn

10.1101/2021.09.02.455707 ◽

2021 ◽

Cited By ~ 1

Author(s):

Vishwa Goudar ◽

Barbara Peysakhovich ◽

David J. Freedman ◽

Elizabeth A. Buffalo ◽

Xiao-Jing Wang

Keyword(s):

Vector Field ◽

Dimensional Subspace ◽

Recurrent Network ◽

Learning To Learn ◽

Weight Changes ◽

Population Activity ◽

Connection Weight ◽

Behavior Learning ◽

Population Trajectory ◽

Low Dimensional

AbstractLearning-to-learn, a progressive acceleration of learning while solving a series of similar problems, represents a core process of knowledge acquisition that draws attention in both neuroscience and artificial intelligence. To investigate its underlying brain mechanism, we trained a recurrent neural network model on arbitrary sensorimotor mappings. The network displayed an exponential speedup in learning. The neural substrate of a schema emerges within a low-dimensional subspace of population activity. Its reuse in new problems facilitates learning by limiting connection weight changes. Since the population trajectory of a recurrent network produces behavior, learning is determined by the vector field changes. We propose a novel analysis of weight-driven vector field changes, which showed that novel stimuli in new problems can distort the schema representation. Weight changes eliminate such distortions and improve the invariance of the reused representations in future learning. The accumulation of such weight changes across problems underlies the learning-to-learn dynamics.

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Numerical Valuation of American Basket Options via Partial Differential Complementarity Problems

Mathematics ◽

10.3390/math9131498 ◽

2021 ◽

Vol 9 (13) ◽

pp. 1498

Author(s):

Karel J. in’t Hout ◽

Jacob Snoeijer

Keyword(s):

Principal Component Analysis ◽

Numerical Experiments ◽

Complementarity Problems ◽

Principal Component ◽

Partial Differential ◽

Basket Options ◽

Basket Option ◽

Option Values ◽

Convergence Behaviour ◽

Low Dimensional

We study the principal component analysis based approach introduced by Reisinger and Wittum (2007) and the comonotonic approach considered by Hanbali and Linders (2019) for the approximation of American basket option values via multidimensional partial differential complementarity problems (PDCPs). Both approximation approaches require the solution of just a limited number of low-dimensional PDCPs. It is demonstrated by ample numerical experiments that they define approximations that lie close to each other. Next, an efficient discretisation of the pertinent PDCPs is presented that leads to a favourable convergence behaviour.

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Optimizing the structure and movement of a robotic bat with biological kinematic synergies

The International Journal of Robotics Research ◽

10.1177/0278364918804654 ◽

2018 ◽

Vol 37 (10) ◽

pp. 1233-1252 ◽

Cited By ~ 2

Author(s):

Jonathan Hoff ◽

Alireza Ramezani ◽

Soon-Jo Chung ◽

Seth Hutchinson

Keyword(s):

Principal Component Analysis ◽

Topological Structure ◽

Degrees Of Freedom ◽

Dimensional Space ◽

Principal Component ◽

Biologically Inspired ◽

Wing Kinematics ◽

Wing Motion ◽

Kinematic Synergies ◽

Low Dimensional

In this article, we present methods to optimize the design and flight characteristics of a biologically inspired bat-like robot. In previous, work we have designed the topological structure for the wing kinematics of this robot; here we present methods to optimize the geometry of this structure, and to compute actuator trajectories such that its wingbeat pattern closely matches biological counterparts. Our approach is motivated by recent studies on biological bat flight that have shown that the salient aspects of wing motion can be accurately represented in a low-dimensional space. Although bats have over 40 degrees of freedom (DoFs), our robot possesses several biologically meaningful morphing specializations. We use principal component analysis (PCA) to characterize the two most dominant modes of biological bat flight kinematics, and we optimize our robot’s parametric kinematics to mimic these. The method yields a robot that is reduced from five degrees of actuation (DoAs) to just three, and that actively folds its wings within a wingbeat period. As a result of mimicking synergies, the robot produces an average net lift improvesment of 89% over the same robot when its wings cannot fold.

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Small-variance asymptotics for non-parametric online robot learning

The International Journal of Robotics Research ◽

10.1177/0278364918816374 ◽

2018 ◽

Vol 38 (1) ◽

pp. 3-22 ◽

Cited By ~ 5

Author(s):

Ajay Kumar Tanwani ◽

Sylvain Calinon

Keyword(s):

Mixture Models ◽

Dirichlet Process ◽

Large Scale ◽

Principal Component ◽

Small Variance ◽

Remote Manipulation ◽

State Duration ◽

Duration Information ◽

Low Dimensional ◽

Non Parametric

Small-variance asymptotics is emerging as a useful technique for inference in large-scale Bayesian non-parametric mixture models. This paper analyzes the online learning of robot manipulation tasks with Bayesian non-parametric mixture models under small-variance asymptotics. The analysis yields a scalable online sequence clustering (SOSC) algorithm that is non-parametric in the number of clusters and the subspace dimension of each cluster. SOSC groups the new datapoint in low-dimensional subspaces by online inference in a non-parametric mixture of probabilistic principal component analyzers (MPPCA) based on a Dirichlet process, and captures the state transition and state duration information online in a hidden semi-Markov model (HSMM) based on a hierarchical Dirichlet process. A task-parameterized formulation of our approach autonomously adapts the model to changing environmental situations during manipulation. We apply the algorithm in a teleoperation setting to recognize the intention of the operator and remotely adjust the movement of the robot using the learned model. The generative model is used to synthesize both time-independent and time-dependent behaviors by relying on the principles of shared and autonomous control. Experiments with the Baxter robot yield parsimonious clusters that adapt online with new demonstrations and assist the operator in performing remote manipulation tasks.

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A Geometric Framework for Understanding Dynamic Information Integration in Context-Dependent Computation

10.1101/2021.02.09.430498 ◽

2021 ◽

Author(s):

Xiaohan Zhang ◽

Shenquan Liu ◽

Zhe Sage Chen

Keyword(s):

Decision Making ◽

Prefrontal Cortex ◽

Information Integration ◽

Principal Component ◽

Population Coding ◽

Geometric Framework ◽

Context Dependent ◽

Activity State ◽

Low Dimensional ◽

Context Cues

AbstractPrefrontal cortex plays a prominent role in performing flexible cognitive functions and working memory, yet the underlying computational principle remains poorly understood. Here we trained a rate-based recurrent neural network (RNN) to explore how the context rules are encoded, maintained across seconds-long mnemonic delay, and subsequently used in a context-dependent decision-making task. The trained networks emerged key experimentally observed features in the prefrontal cortex (PFC) of rodent and monkey experiments, such as mixed-selectivity, sparse representations, neuronal sequential activity and rotation dynamics. To uncover the high-dimensional neural dynamical system, we further proposed a geometric framework to quantify and visualize population coding and sensory integration in a temporally-defined manner. We employed dynamic epoch-wise principal component analysis (PCA) to define multiple task-specific subspaces and task-related axes, and computed the angles between task-related axes and these subspaces. In low-dimensional neural representations, the trained RNN first encoded the context cues in a cue-specific subspace, and then maintained the cue information with a stable low-activity state persisting during the delay epoch, and further formed line attractors for sensor integration through low-dimensional neural trajectories to guide decision making. We demonstrated via intensive computer simulations that the geometric manifolds encoding the context information were robust to varying degrees of weight perturbation in both space and time. Overall, our analysis framework provides clear geometric interpretations and quantification of information coding, maintenance and integration, yielding new insight into the computational mechanisms of context-dependent computation.

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Approximating deformation fields for the analysis of continuous heterogeneity of biological macromolecules by 3D Zernike polynomials

IUCrJ ◽

10.1107/s2052252521008903 ◽

2021 ◽

Vol 8 (6) ◽

Author(s):

David Herreros ◽

Roy R. Lederman ◽

James Krieger ◽

Amaya Jiménez-Moreno ◽

Marta Martínez ◽

...

Keyword(s):

Electron Microscopy ◽

Structural Characteristics ◽

Normal Mode Analysis ◽

Principal Component ◽

Current Data ◽

Mode Analysis ◽

Biological Macromolecules ◽

Mathematical Basis ◽

Common Framework ◽

Low Dimensional

Structural biology has evolved greatly due to the advances introduced in fields like electron microscopy. This image-capturing technique, combined with improved algorithms and current data processing software, allows the recovery of different conformational states of a macromolecule, opening new possibilities for the study of its flexibility and dynamic events. However, the ensemble analysis of these different conformations, and in particular their placement into a common variable space in which the differences and similarities can be easily recognized, is not an easy matter. To simplify the analysis of continuous heterogeneity data, this work proposes a new automatic algorithm that relies on a mathematical basis defined over the sphere to estimate the deformation fields describing conformational transitions among different structures. Thanks to the approximation of these deformation fields, it is possible to describe the forces acting on the molecules due to the presence of different motions. It is also possible to represent and compare several structures in a low-dimensional mapping, which summarizes the structural characteristics of different states. All these analyses are integrated into a common framework, providing the user with the ability to combine them seamlessly. In addition, this new approach is a significant step forward compared with principal component analysis and normal mode analysis of cryo-electron microscopy maps, avoiding the need to select components or modes and producing localized analysis.

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