Go with the FLOW: visualizing spatiotemporal dynamics in optical widefield calcium imaging

Widefield calcium imaging has recently emerged as a powerful experimental technique to record coordinated large-scale brain activity. These measurements present a unique opportunity to characterize spatiotemporally coherent structures that underlie neural activity across many regions of the brain. In this work, we leverage analytic techniques from fluid dynamics to develop a visualization framework that highlights features of flow across the cortex, mapping wavefronts that may be correlated with behavioural events. First, we transform the time series of widefield calcium images into time-varying vector fields using optic flow. Next, we extract concise diagrams summarizing the dynamics, which we refer to as FLOW (flow lines in optical widefield imaging) portraits. These FLOW portraits provide an intuitive map of dynamic calcium activity, including regions of initiation and termination, as well as the direction and extent of activity spread. To extract these structures, we use the finite-time Lyapunov exponent technique developed to analyse time-varying manifolds in unsteady fluids. Importantly, our approach captures coherent structures that are poorly represented by traditional modal decomposition techniques. We demonstrate the application of FLOW portraits on three simple synthetic datasets and two widefield calcium imaging datasets, including cortical waves in the developing mouse and spontaneous cortical activity in an adult mouse.     

Empirical explorations of mindfulness: Conceptual and methodological conundrums.

This commentary reflects on the articles in this Special Issue. The appearance of this group of articles underscores the important idea that a major target of mindfulness practice is on emotion. Transformation in trait affect is a key goal of all contemplative traditions. This commentary addresses several key methodological and conceptual issues in the empirical study of mindfulness. The many ways in which the term “mindfulness” is used in the articles in this Special Issue are noted, and they include its reference to states, traits, and independent variables that are manipulated in an experimental context. How the term “mindfulness” is conceptualized and operationalized is crucial, and for progress to be made it is essential that we qualify the use of this term by reference to how it is being operationalized in each context. Other methodological issues are considered, such as the duration of training and how it should be measured, and the nature of control and comparison groups in studies of mindfulness-based interventions. Finally, the commentary ends with a consideration of the targets within emotion processing that are likely to be impacted by mindfulness. This collection of articles underscores the substantial progress that has occurred in the empirical study of mindfulness and it is a harbinger of a very promising future in this area. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Bela Julesz (1928-2003).

Presents the obituary for Bela Julesz (1928-2003). Dr. Julesz is remembered for his contribution to the study of binocular vision, particularly his design of Julesz Random Dot Stereograms. In addition, he is noted for his research into the study of statistical properties of texture and the visual system's ability to apperceive variation in higher order statistics. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

There are no "specific correct" usages of concepts, only correct usages: Linguistic rules and the bounds of sense.

In this article we respond to Justin Sytsma's (see record 2008-01492-007) critique of our 2005 article "Analogy and Metaphor Running Amok: An Examination of the Use of Explanatory Devices in Neuroscience" (see record 2006-12348-004). We address each of Sytsma's major criticisms in turn. We conclude that, not only does Sytsma fail to convincingly demonstrate how our argument fails, he falls headlong into the very conceptual confusions we examine in our original article. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Minds, Machines and Turing

Turing's celebrated 1950 paper proposes a very generalmethodological criterion for modelling mental function: total functionalequivalence and indistinguishability. His criterion gives rise to ahierarchy of Turing Tests, from subtotal (toy) fragments of ourfunctions (t1), to total symbolic (pen-pal) function (T2 – the standardTuring Test), to total external sensorimotor (robotic) function (T3), tototal internal microfunction (T4), to total indistinguishability inevery empirically discernible respect (T5). This is areverse-engineering hierarchy of (decreasing) empiricalunderdetermination of the theory by the data. Level t1 is clearly toounderdetermined, T2 is vulnerable to a counterexample (Searle's ChineseRoom Argument), and T4 and T5 are arbitrarily overdetermined. Hence T3is the appropriate target level for cognitive science. When it isreached, however, there will still remain more unanswerable questionsthan when Physics reaches its Grand Unified Theory of Everything (GUTE),because of the mind/body problem and the other-minds problem, both ofwhich are inherent in this empirical domain, even though Turing hardlymentions them.     

A Cortical Based Model of Perceptual Completion in the Roto-Translation Space

We present a mathematical model of perceptual completion and formation of subjective surfaces, which is at the same time inspired by the architecture of the visual cortex, and is the lifting in the 3-dimensional rototranslation group of the phenomenological variational models based on elastica functional. The initial image is lifted by the simple cells to a surface in the rototranslation group and the completion process is modeled via a diffusion driven motion by curvature. The convergence of the motion to a minimal surface is proved. Results are presented both for modal and amodal completion in classic Kanizsa images. The work was supported by University of Bologna: founds for selected research topics. Giovanna Citti is full professor of Mathematical Analysis at University of Bologna, and she is coordinator, together with A.Sarti, of the local interdipartimental group of “Neuromathematics and Visual Cognition”. Her principal research interests are existence and regularity of solution of nonlinear subelliptic equations represented as sum of squares of vector fields, whose associated geometry is subriemannian. Besides she is interested in applications of instruments of real analysis in Lie Groups and subriemannian geometry to visual perception, and to the study of the functionality of the visual cortex. Alessandro Sarti received the Ph.D. degree in bioengineering from the University of Bologna in 1996. From 1997 to 2000 he was appointed with a Postdoc position at the Mathematics Department of the University of California, Berkeley, and the Mathematics Department of the Lawrence Berkeley National Laboratory in Berkeley. Since 2001 he got a permanent position at the University of Bologna. He is associate to CREA, Ecole Polytechnique, Paris, France. With Giovanna Citti, he is the scientific responsible of the interdipartimental group of “Neuromathematics and Visual Cognition.” In the last years he gave lectures at the University of Yale, University of California at Los Angeles, University of California at Berkeley, Freie Universitat Berlin, Ecole Normale Superieure Cachan, Ecole Polytechnique, Scuola Normale Superiore di Pisa.     

复杂网络动力学与智能控制

智能如何产生, 其动力学行为如何演化、如何控制? 针对这些问题, 本文从复杂网络和动力学系统的角度简要综述智能控制的相关研究: 讨论复杂网络、动力学系统、神经科学和智能控制交叉研究的内涵和挑战问题; 概述牵制控制、混杂控制、自适应控制及复杂网络可控性等研究进展。并探讨复杂网络动力学与智能控制在脑科学与机器行为学中的相关应用及研究方向。     

Modeling incubation and restructuring for creative problem solving in robots

Creativity represents the pinnacle of higher-level cognition, but exactly how it is achieved remains poorly understood, especially when simultaneously facing the opposing challenge of intractable complexity. The aims of the current study were (a) to examine how the brain may achieve the dual goals of creativity and complexity reduction, and (b) to begin developing higher-level cognition and creativity in robots. We address these aims by (a) modeling an example of insight problem solving and comparing it to empirical data, and (b) testing the model on a robot platform. Unlike other models, we propose a single mechanism for both creative problem solving and complexity reduction. Focusing on creativity, the computational mechanism leads to insightful problem solving by restructuring an internal belief representation based on evidence collected during an incubation period. Because insightful problem solving has been examined closely in nonhuman primates, providing detailed quantitative datasets lacking in humans, we tested the model by comparing simulations to insightful problem solving by rhesus monkeys. Results show that the proposed model accounts for both the discontinuous three stage problem-solving patterns and the spontaneous generalization to novel cases observed with the monkeys. To test the model in a physical environment, we implemented it in a vision-equipped robot, and the model solved the same insight problem from camera percepts. Our model shows how the creative brain may address the dual challenges of complex environments–finding unprecedented opportunity hidden within potentially intractable complexity–and suggests that both challenges may be met by a single underlying computational mechanism.     

表象式直接知识表示

通过对人的相对低层次认知行为的模仿可以改善计算机感知外部世界的能力。知觉作为首要的认知行为,它所需要的知识不便于采用传统的符号化知识表示方法来表示。该文以视知觉研究为基础,通过模拟分布于视皮层中的特异性功能柱型结构,将图形模式分解为简单特征的组合,直接表示在神经网络上,众多功能单元的同时响应就构成某一实体的视觉表象。最后还讨论了这种直接知识表示方法在知识与概念化问题、知识的来源问题、学习问题等几个最核心的人工智能问题下的意义。     

Automatic Generation of Cognitive Theories using Genetic Programming

Cognitive neuroscience is the branch of neuroscience that studies the neural mechanisms underpinning cognition and develops theories explaining them. Within cognitive neuroscience, computational neuroscience focuses on modeling behavior, using theories expressed as computer programs. Up to now, computational theories have been formulated by neuroscientists. In this paper, we present a new approach to theory development in neuroscience: the automatic generation and testing of cognitive theories using genetic programming (GP). Our approach evolves from experimental data cognitive theories that explain “the mental program” that subjects use to solve a specific task. As an example, we have focused on a typical neuroscience experiment, the delayed-match-to-sample (DMTS) task. The main goal of our approach is to develop a tool that neuroscientists can use to develop better cognitive theories.