Corpus ID: 1245894

The Imaging Computational Microscope

  title={The Imaging Computational Microscope},
  author={Edward Paxon Frady and William B. Kristan},
  journal={arXiv: Neurons and Cognition},
The Imaging Computational Microscope (ICM) is a suite of computational tools for automated analysis of functional imaging data that runs under the cross-platform MATLAB environment (The Mathworks, Inc.). ICM uses a semi-supervised framework, in which at every stage of analysis computers handle the routine work, which is then refined by user intervention. The main functionality of ICM is built upon automated extraction of component signals from imaging data, segmentation and clustering of… Expand
Scalable Semisupervised Functional Neurocartography Reveals Canonical Neurons in Behavioral Networks
This study illustrates the basic framework to map neural activity in high dimensions with large-scale recordings and how to extract the rich information necessary to perform analyses in light of intersubject variability. Expand
Direct Imaging of Hippocampal Epileptiform Calcium Motifs Following Kainic Acid Administration in Freely Behaving Mice
The results confirm the presence of pathological calcium activity preceding convulsive motor seizures and support calcium as a candidate signaling molecule in a pathway connecting seizures to subsequent cellular damage. Expand


Automated Analysis of Cellular Signals from Large-Scale Calcium Imaging Data
An automated sorting procedure is described that combines independent component analysis and image segmentation for extracting cells' locations and their dynamics with minimal human supervision and found microzones of Purkinje cells that were stable across behavioral states and in which synchronous Ca(2+) spiking rose significantly during locomotion. Expand
Whole-brain functional imaging at cellular resolution using light-sheet microscopy
Light-sheet microscopy is used to record activity from the entire volume of the brain of the larval zebrafish in vivo at 0.8 Hz, capturing more than 80% of all neurons at single-cell resolution, demonstrating how this technique can be used to reveal functionally defined circuits across the brain. Expand
Observed Brain Dynamics
This volume addresses the need for a textbook in this interdisciplinary area of neuroscience and is written for a broad spectrum of readers ranging from physical scientists, mathematicians, and statisticians wishing to educate themselves about neuroscience, to biologists who would like to learn time series analysis methods in particular and refresh their mathematical and statistical knowledge in general. Expand
Mapping brain activity at scale with cluster computing
A library of analytical tools called Thunder built on the open-source Apache Spark platform for large-scale distributed computing that implements a variety of univariate and multivariate analyses with a modular, extendable structure well-suited to interactive exploration and analysis development. Expand
EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis
We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEGExpand
Validation of independent component analysis for rapid spike sorting of optical recording data.
The accuracy of ICA for extracting the neural activity of many individual neurons from noisy, mixed, and redundant optical recording data sets is validated and should enable the use of this powerful large-scale imaging approach for studies of invertebrate and suitable vertebrate neuronal networks. Expand
Parametric Image Alignment Using Enhanced Correlation Coefficient Maximization
This work proposes the use of a modified version of the correlation coefficient as a performance criterion for the image alignment problem and proposes an efficient approximation that leads to a closed form solution which is of low computational complexity. Expand
Applicability of independent component analysis on high-density microelectrode array recordings.
The demixing performance of ICA is assessed using simulated data sets and it is found that the performance strongly depends on neuronal density and spike amplitude, and postprocessing techniques can be used to overcome the most severe limitations of the technique. Expand
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
It is suggested that information maximization provides a unifying framework for problems in "blind" signal processing and dependencies of information transfer on time delays are derived. Expand
Optically monitoring voltage in neurons by photo-induced electron transfer through molecular wires
Fluorescence sensors that detect voltage changes in neurons by modulation of photo-induced electron transfer from an electron donor through a synthetic molecular wire to a fluorophore and enable single-trial detection of synaptic and action potentials in cultured hippocampal neurons and intact leech ganglia are presented. Expand