Python for Large-Scale Electrophysiology

Electrophysiology is increasingly moving towards highly parallel recording techniques which generate large data sets. We record extracellularly in vivo in cat and rat visual cortex with 54-channel silicon polytrodes, under time-locked visual stimulation, from localized neuronal populations within a cortical column. To help deal with the complexity of generating and analysing these data, we used the Python programming language to develop three software projects: one for temporally precise visual stimulus generation (“dimstim”); one for electrophysiological waveform visualization and spike sorting (“spyke”); and one for spike train and stimulus analysis (“neuropy”). All three are open source and available for download (http://swindale.ecc.ubc.ca/code). The requirements and solutions for these projects differed greatly, yet we found Python to be well suited for all three. Here we present our software as a showcase of the extensive capabilities of Python in neuroscience

Hardware-accelerated interactive data visualization for neuroscience in Python.

Large datasets are becoming more and more common in science, particularly in neuroscience where experimental techniques are rapidly evolving. Obtaining interpretable results from raw data can sometimes be done automatically; however, there are numerous situations where there is a need, at all processing stages, to visualize the data in an interactive way. This enables the scientist to gain intuition, discover unexpected patterns, and find guidance about subsequent analysis steps. Existing visualization tools mostly focus on static publication-quality figures and do not support interactive visualization of large datasets. While working on Python software for visualization of neurophysiological data, we developed techniques to leverage the computational power of modern graphics cards for high-performance interactive data visualization. We were able to achieve very high performance despite the interpreted and dynamic nature of Python, by using state-of-the-art, fast libraries such as NumPy, PyOpenGL, and PyTables. We present applications of these methods to visualization of neurophysiological data. We believe our tools will be useful in a broad range of domains, in neuroscience and beyond, where there is an increasing need for scalable and fast interactive visualization

BrainGlobe Atlas API: a common interface for neuroanatomical atlases

Summary: Neuroscientists routinely perform experiments aimed at recording or manipulating neural activity, uncovering physiological processes underlying brain function or elucidating aspects of brain anatomy. Understanding how the brain generates behaviour ultimately depends on merging the results of these experiments into a unified picture of brain anatomy and function. Brain atlases are crucial in this endeavour: by outlining the organization of brain regions they provide a reference upon which our understanding of brain function can be anchored. More recently, digital high-resolution 3d atlases have been produced for several model organisms providing an invaluable resource for the research community. Effective use of these atlases depends on the availability of an application programming interface (API) that enables researchers to develop software to access and query atlas data. However, while some atlases come with an API, these are generally specific for individual atlases, and this hinders the development and adoption of open-source neuroanatomy software. The BrainGlobe atlas API (BG-Atlas API) overcomes this problem by providing a common interface for programmers to download and process data across a variety of model organisms. By adopting the BG-Atlas API, software can then be developed agnostic to the atlas, increasing adoption and interoperability of packages in neuroscience and enabling direct integration of different experimental modalities and even comparisons across model organisms

Herramienta de modelado disfuncional tridimensional basado en estudios de neuroimagen

El modelado disfuncional basado en estudios de neuroimagen mejora la comprensión de los cambios estructurales provocados ante la presencia de lesiones cerebrales. Actualmente, existen numerosas herramientas para el análisis y procesado de estudios de neuroimagen. Algunas de ellas, como el 3D Slicer, BrainVoyager y el FreeSurfer permiten la creación y navegación sobre modelos tridimensionales cerebrales sin alteraciones estructurales. Sin embargo, no se han detectado herramientas que permitan modelar tridimensionalmente lesiones a partir de estudios de neuroimagen, concretamente de estudios de resonancia magnética. El objetivo de este trabajo es el diseño de una metodología que permite la creación de este tipo de modelos y su visualización y navegación