检索结果-内蒙古大学图书馆

Modulation of Dopamine for Adaptive Learning: a Neurocomputational Model

学校读者我要写书评

暂无评论

Computational Brain and Behavior 2021年第1期4卷 34-52页

作者： Inglis, Jeffrey B. Valentin, Vivian V. Ashby, F. Gregory Interdepartmental Graduate Program in Dynamical Neuroscience University of California Santa Barbara 93106 CA United States Department of Psychological & Brain Sciences University of California Santa Barbara 93106 CA United States

There have been many proposals that learning rates in the brain are adaptive, in the sense that they increase or decrease depending on environmental conditions. The majority of these models are abstract and make no attempt to describe the neural circuitry that implements the proposed computations. This article describes a biologically detailed computational model that overcomes this shortcoming. Specifically, we propose a neural circuit that implements adaptive learning rates by modulating the gain on the dopamine response to reward prediction errors, and we model activity within this circuit at the level of spiking neurons. The model generates a dopamine signal that depends on the size of the tonically active dopamine neuron population and the phasic spike rate. The model was tested successfully against results from two single-neuron recording studies and a fast-scan cyclic voltammetry study. We conclude by discussing the general applicability of the model to dopamine-mediated tasks that transcend the experimental phenomena it was initially designed to address. © 2020, Society for Mathematical Psychology.

关键词： Adaptive learning rate Computational cognitive neuroscience Dopamine Ventral subiculum

Simulated prosthetic vision identifies checkerboard as an effective raster pattern for retinal implants

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Kasowski, Justin M. Beyeler, Michael Interdepartmental Graduate Program in Dynamical Neuroscience University of California Santa BarbaraCA93106 United States Department of Computer Science University of California Santa BarbaraCA93106 United States Department of Psychological & Brain Sciences University of California Santa BarbaraCA93106 United States

Objective: This study systematically evaluates the impact of raster patterns-specific spatial arrangements of sequential electrode activation-on performance and perceived difficulty in simulated prosthetic vision (SPV). By addressing a critical gap in the literature, we aimed to identify patterns that optimize functional vision in retinal implants. Approach: Sighted participants completed letter recognition and motion discrimination tasks under four raster patterns (horizontal, vertical, checkerboard, and random) using an immersive SPV system. The simulations employed psychophysically validated models of electrode activation, phosphene appearance, and temporal dynamics, ensuring realistic representation of prosthetic vision. Performance accuracy and self-reported difficulty were analyzed to assess the effects of raster patterning. Main Results: The checkerboard pattern consistently outperformed other raster patterns, yielding significantly higher accuracy and lower difficulty ratings across both tasks. The horizontal and vertical patterns introduced biases aligned with apparent motion artifacts, while the checkerboard minimized such effects. Random patterns resulted in the lowest performance, underscoring the importance of structured activation. Significance: These findings provide the first systematic evaluation of raster patterns, suggesting that the checkerboard configuration may enhance usability and perceptual clarity in retinal implants. While based on simulated environments, this study establishes a foundation for optimizing spatial and temporal electrode activation strategies in next-generation devices. © 2025, CC BY.

关键词： Prosthetics

On Koopman mode decomposition and tensor component analysis

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Redman, William T. Interdepartmental Graduate Program in Dynamical Neuroscience University of California Santa Barbara CA93106 United States

Koopman mode decomposition and tensor component analysis (also known as CANDECOMP/PARAFAC or canonical polyadic decomposition) are two popular approaches of decomposing high dimensional data sets into modes that capture the most relevant features and/or dynamics. Despite their similar goal, the two methods are largely used by different scientific communities and are formulated in distinct mathematical languages. We examine the two together and show that, under certain conditions on the data, the theoretical decomposition given by tensor component analysis is the same as that given by Koopman mode decomposition. This provides a "bridge" with which the two communities should be able to more effectively communicate. Our work provides new possibilities for algorithmic approaches to Koopman mode decomposition and tensor component analysis, and offers a principled way in which to compare the two methods. Additionally, it builds upon a growing body of work showing that dynamical systems theory, and Koopman operator theory in particular, can be useful for problems that have historically made use of optimization theory. © 2021, CC BY.

关键词： Clustering algorithms

Renormalization group as a Koopman operator

学校读者我要写书评

暂无评论

Physical Review E 2020年第6期101卷 060104(R)-060104(R)页

作者： William T. Redman Interdepartmental Graduate Program in Dynamical Neuroscience University of California Santa Barbara California 93106 USA

Koopman operator theory is shown to be directly related to the renormalization group. This observation allows us, with no assumption of translational invariance, to compute the critical exponents η and δ, as well as ratios of critical exponents, of classical spin systems from single observables alone. This broadens the types of problems that the renormalization group framework can be applied to and establish universality classes of. In addition, this connection may allow for a new, data-driven way in which to find the renormalization group fixed point(s), and their relevant and irrelevant directions.

关键词： dynamical systems Chaos & nonlinear dynamics Critical exponents Renormalization group

Algorithmic (Semi-)Conjugacy via Koopman Operator Theory

学校读者我要写书评

暂无评论

Algorithmic (Semi-)Conjugacy via Koopman Operator Theory

IEEE Conference on Decision and Control

作者： William T. Redman Maria Fonoberova Ryan Mohr Ioannis G. Kevrekidis Igor Mezić Interdepartmental Graduate Program in Dynamical Neuroscience University of California Santa Barbara AIMdyn Inc. Santa Barbara Departments of Chemical and Biomolecular Engineering and of Mathematics and Statistics The Johns Hopkins University Departments of Mechanical Engineering and of Mathematics University of California Santa Barbara

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Iterative algorithms are of utmost importance in decision and control. With an ever growing number of algorithms being developed, distributed, and proprietarized, there is a similarly growing need for methods that can provide classification and comparison. By viewing iterative algorithms as discrete-time dynamical systems, we leverage Koopman operator theory to identify (semi-)conjugacies between algorithms using their spectral properties. This provides a general framework with which to classify and compare algorithms.

关键词： Heuristic algorithms Classification algorithms Iterative methods dynamical systems

Algorithmic (Semi-)Conjugacy via Koopman Operator Theory

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Redman, William T. Fonoberova, Maria Mohr, Ryan Kevrekidis, Ioannis G. Mezić, Igor The Interdepartmental Graduate Program in Dynamical Neuroscience University of California Santa Barbara United States AIMdyn Inc. Santa Barbara United States The Department of Chemical and Biomolecular Engineering and of Mathematics and Statistics The Johns Hopkins University United States The Department of Mechanical Engineering and of Mathematics University of California Santa Barbara United States

关键词： dynamical systems

Control of ecological outcomes through deliberate parameter changes in a model of the gut microbiome

学校读者我要写书评

暂无评论

Physical Review E 2020年第5期101卷 052402-052402页

作者： Zipeng Wang Eric W. Jones Joshua M. Mueller Jean M. Carlson Department of Physics University of California Santa Barbara Santa Barbara California 93106 USA Interdepartmental Graduate Program in Dynamical Neuroscience University of California Santa Barbara Santa Barbara California 93106 USA

The generalized Lotka-Volterra (gLV) equations are a mathematical proxy for ecological dynamics. We focus on a gLV model of the gut microbiome, in which the evolution of the gut microbial state is determined in part by pairwise interspecies interaction parameters that encode environmentally mediated resource competition between microbes. We develop an in silico method that controls the steady-state outcome of the system by adjusting these interaction parameters. This approach is confined to a bistable region of the gLV model. In this method, a dimensionality reduction technique called steady-state reduction (SSR) is first used to generate a two-dimensional (2D) gLV model that approximates the high-dimensional dynamics on the 2D subspace spanned by the two steady states. Then a bifurcation analysis of the 2D model analytically determines parameter modifications that drive an initial condition to a target steady state. This parameter modification of the reduced 2D model guides parameter modifications of the original high-dimensional model, resulting in a change of steady-state outcome in the high-dimensional model. This control method, called SSR-guided parameter change (SPARC), bypasses the computational challenge of directly determining parameter modifications in the original high-dimensional system. SPARC could guide the development of indirect bacteriotherapies, which seek to change microbial compositions by deliberately modifying gut environmental variables such as gut acidity or macronutrient availability.

关键词： Complex systems Ecological population dynamics Bacteria dynamical systems High dimensional systems Nonequilibrium systems Coarse graining Phase space dynamics

A Systematic Review of Extended Reality (XR) for Understanding and Augmenting Vision Loss

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Kasowski, Justin Johnson, Byron A. Neydavood, Ryan Akkaraju, Anvitha Beyeler, Michael Graduate Program in Dynamical Neuroscience University of California Santa BarbaraCA93106 United States Department of Psychological & Brain Sciences University of California Santa BarbaraCA93106 United States Department of Computer Science University of California Santa BarbaraCA93106 United States

Purpose: Over the past decade, extended reality (XR) has emerged as an assistive technology not only to augment residual vision of people losing their sight but also to study the rudimentary vision restored to blind people by a visual neuroprosthesis. To make the best use of these emerging technologies, it is valuable and timely to understand the state of this research and identify any shortcomings that are present. Methods: Here we present a systematic literature review of 227 publications from 106 different venues assessing the potential of XR technology to further visual accessibility. In contrast to other reviews, we sample studies from multiple scientific disciplines, focus on augmentation of a person's residual vision, and require studies to feature a quantitative evaluation with appropriate end users. Results: We summarize prominent findings from different XR research areas, show how the landscape has changed over the last decade, and identify scientific gaps in the literature. Specifically, we highlight the need for real-world validation, the broadening of end-user participation, and a more nuanced understanding of the suitability and usability of different XR-based accessibility aids. Translational Relevance: By broadening end-user participation to early stages of the design process and shifting the focus from behavioral performance to qualitative assessments of usability, future research has the potential to develop XR technologies that may not only allow for studying vision loss, but also enable novel visual accessibility aids with the potential to impact the lives of millions of people living with vision loss. © 2021, CC BY.

关键词： Augmented reality

Universality of Winning Tickets: A Renormalization Group Perspective

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Redman, William T. Chen, Tianlong Wang, Zhangyang Dogra, Akshunna S. Interdepartmental Graduate Program in Dynamical Neuroscience University of California Santa Barbara United States Department of Electrical and Computer Engineering University of Texas at Austin United States Department of Mathematics Imperial College London United Kingdom EPSRC CDT in Mathematics of Random Systems: Analysis Modelling and Simulation United Kingdom

Foundational work on the Lottery Ticket Hypothesis has suggested an exciting corollary: winning tickets found in the context of one task can be transferred to similar tasks, possibly even across different architectures. This has generated broad interest, but methods to study this universality are lacking. We make use of renormalization group theory, a powerful tool from theoretical physics, to address this need. We find that iterative magnitude pruning, the principal algorithm used for discovering winning tickets, is a renormalization group scheme, and can be viewed as inducing a flow in parameter space. We demonstrate that ResNet-50 models with transferable winning tickets have flows with common properties, as would be expected from the theory. Similar observations are made for BERT models, with evidence that their flows are near fixed points. Additionally, we leverage our framework to study winning tickets transferred across ResNet architectures, observing that smaller models have flows with more uniform properties than larger models, complicating transfer between them. © 2021, CC BY.

关键词： Statistical mechanics