Applications of Information Theory to Analysis of Neural Data

Information theory is a practical and theoretical framework developed for the study of communication over noisy channels. Its probabilistic basis and capacity to relate statistical structure to function make it ideally suited for studying information flow in the nervous system. It has a number of useful properties: it is a general measure sensitive to any relationship, not only linear effects; it has meaningful units which in many cases allow direct comparison between different experiments; and it can be used to study how much information can be gained by observing neural responses in single trials, rather than in averages over multiple trials. A variety of information theoretic quantities are commonly used in neuroscience - (see entry "Definitions of Information-Theoretic Quantities"). In this entry we review some applications of information theory in neuroscience to study encoding of information in both single neurons and neuronal populations.Comment: 8 pages, 2 figure

Collaborative human–robot interaction interface: development for a spinal surgery robotic assistan

The growing introduction of robotics in non-industrial applications where the environment is unstructured and changing, has led to the need of development of safer and more intuitive, human-robot interfaces. In such environments, the use of collaborative robots has potential benefits, due to the combination of user experience, knowledge and flexibility with the robot's accuracy, stiffness and repeatability. Nevertheless, in order to guarantee a functional collaboration in these environments, the interaction between user and robot must be intuitive, natural, fast and easy to use. On one hand, commercial collaborative robots are less accurate and less stiff than the traditional industrial ones, on the other hand, the later have not intuitive interaction interfaces. There are tasks in which the stiffness of industrial robots and the intuitive interaction interfaces of collaborative commercial robots, are desirable. This is the case of some robotic assisted surgical procedures, such as robotic assisted spine surgery, with high accuracy demands and with the need of intuitive surgeon-robot interaction. This paper presents a hand guiding methodology for functional human-robot collaboration and the introduction of novel algorithms to enhance its behavior. Also its implementation on a robotic surgical assistant for spine procedures is presented. It is emphasized how a traditional industrial robot can be used as a collaborative one when the available commercial collaborative robots do not have the required accuracy and stiffness for the task

Volumetric two-photon imaging of neurons using stereoscopy (vTwINS)

Two-photon laser scanning microscopy of calcium dynamics using fluorescent indicators is a widely used imaging method for large scale recording of neural activity in vivo. Here we introduce volumetric Two-photon Imaging of Neurons using Stereoscopy (vTwINS), a volumetric calcium imaging method that employs an elongated, V-shaped point spread function to image a 3D brain volume. Single neurons project to spatially displaced “image pairs” in the resulting 2D image, and the separation distance between images is proportional to depth in the volume. To demix the fluorescence time series of individual neurons, we introduce a novel orthogonal matching pursuit algorithm that also infers source locations within the 3D volume. We illustrate vTwINS by imaging neural population activity in mouse primary visual cortex and hippocampus. Our results demonstrate that vTwINS provides an effective method for volumetric two-photon calcium imaging that increases the number of neurons recorded while maintaining a high frame-rate