Mapping the spatiotemporal dynamics of calcium signaling in cellular neural networks using optical flow

An optical flow gradient algorithm was applied to spontaneously forming net- works of neurons and glia in culture imaged by fluorescence optical microscopy in order to map functional calcium signaling with single pixel resolution. Optical flow estimates the direction and speed of motion of objects in an image between subsequent frames in a recorded digital sequence of images (i.e. a movie). Computed vector field outputs by the algorithm were able to track the spatiotemporal dynamics of calcium signaling pat- terns. We begin by briefly reviewing the mathematics of the optical flow algorithm, and then describe how to solve for the displacement vectors and how to measure their reliability. We then compare computed flow vectors with manually estimated vectors for the progression of a calcium signal recorded from representative astrocyte cultures. Finally, we applied the algorithm to preparations of primary astrocytes and hippocampal neurons and to the rMC-1 Muller glial cell line in order to illustrate the capability of the algorithm for capturing different types of spatiotemporal calcium activity. We discuss the imaging requirements, parameter selection and threshold selection for reliable measurements, and offer perspectives on uses of the vector data.Comment: 23 pages, 5 figures. Peer reviewed accepted version in press in Annals of Biomedical Engineerin

Optomechanical and Crystallization Phenomena Visualized with 4D Electron Microscopy: Interfacial Carbon Nanotubes on Silicon Nitride

With ultrafast electron microscopy (UEM), we report observation of the nanoscopic crystallization of amorphous silicon nitride, and the ultrashort optomechanical motion of the crystalline silicon nitride at the interface of an adhering carbon nanotube network. The in situ static crystallization of the silicon nitride occurs only in the presence of an adhering nanotube network, thus indicating their mediating role in reaching temperatures close to 1000 °C when exposed to a train of laser pulses. Under such condition, 4D visualization of the optomechanical motion of the specimen was followed by quantifying the change in diffraction contrast of crystalline silicon nitride, to which the nanotube network is bonded. The direction of the motion was established from a tilt series correlating the change in displacement with both the tilt angle and the response time. Correlation of nanoscopic motion with the picosecond atomic-scale dynamics suggests that electronic processes initiated in the nanotubes are responsible for the initial ultrafast optomechanical motion. The time scales accessible to UEM are 12 orders of magnitude shorter than those traditionally used to study the optomechanical motion of carbon nanotube networks, thus allowing for distinctions between the different electronic and thermal mechanisms to be made

Ultrabroadband dispersive radiation by spatiotemporal oscillation of multimode waves

Despite the abundance and importance of three-dimensional systems, relatively little progress has been made on spatiotemporal nonlinear optical waves compared to time-only or space-only systems. Here we study radiation emitted by three-dimensionally evolving nonlinear optical waves in multimode fiber. Spatiotemporal oscillations of solitons in the fiber generate multimode dispersive wave sidebands over an ultrabroadband spectral range. This work suggests routes to multipurpose sources of coherent electromagnetic waves, with unprecedented wavelength coverage.Comment: 13 pages, 3 figures, Supplementary Movie files for preprint available at: https://www.youtube.com/watch?v=h3meO8G6ZzA and https://www.youtube.com/watch?v=k42llO-c1r

Efficient spiking neural network model of pattern motion selectivity in visual cortex

Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance approach to simulating large-scale neural network models in real-time is to leverage the parallel processing capability of graphics processing units (GPUs). Based on this approach, we present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction- selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and Heeger. Pattern-direction-selective (PDS) cells in MT are constructed by pooling over MT CDS cells with a wide range of preferred directions. Responses of our model neurons are comparable to electrophysiological results for grating and plaid stimuli as well as speed tuning. The behavioral response of the network in a motion discrimination task is in agreement with psychophysical data. Moreover, our implementation outperforms a previous implementation of the motion energy model by orders of magnitude in terms of computational speed and memory usage. The full network, which comprises 153,216 neurons and approximately 40 million synapses, processes 20 frames per second of a 40∈×∈40 input video in real-time using a single off-the-shelf GPU. To promote the use of this algorithm among neuroscientists and computer vision researchers, the source code for the simulator, the network, and analysis scripts are publicly available. © 2014 Springer Science+Business Media New York