Biology subjects

Paninski, L.

Publications and source records attributed to Paninski, L..

58 records · Page 4Linked to original sources

bioRxiv2017

A Statistical Model of Shared Variability in the Songbird Auditory System

Vocal communication evokes robust responses in primary auditory cortex (A1) of songbirds, and single neurons from superficial and deep regions of A1 have been shown to respond selectively to songs over complex, synthetic sounds. However, little is known about how this song selectivity arises and manifests itself on the level of networks of neurons in songbird A1. Here, we examined the network-level coding of song and synthetic sounds in A1 by simultaneously recording the responses of multiple neurons in unanesthetized zebra finches. We developed a latent factor model of the joint simultaneous activity of these neural populations, and found that the shared variability in the activity has a surprisingly simple structure; it is dominated by an unobserved latent source with one degree-of-freedom. This simple model captures the structure of the correlated activity in these populations in both spontaneous and stimulus-driven conditions, and given both song and synthetic stimuli. The inferred latent variability is strongly suppressed under stimulation, consistent with similar observations in a range of mammalian cortical regions.

neuroscience↗

bioRxiv2016

Multi-scale approaches for high-speed imaging and analysis of large neural populations

Progress in modern neuroscience critically depends on our ability to observe the activity of large neuronal populations with cellular spatial and high temporal resolution. However, two bottlenecks constrain efforts towards fast imaging of large populations. First, the resulting large video data is challenging to analyze. Second, there is an explicit tradeoff between imaging speed, signal-to-noise, and field of view: with current recording technology we cannot image very large neuronal populations with simultaneously high spatial and temporal resolution.\n\nHere we describe multi-scale approaches for alleviating both of these bottlenecks. First, we show that spatial and temporal decimation techniques provide order-of-magnitude speedups in spatiotemporally demixing calcium video data into estimates of single-cell neural activity. Second, once the shapes of individual neurons have been identified (e.g., after an initial phase of conventional imaging with standard temporal and spatial resolution), we find that the spatial/temporal resolution tradeoff shifts dramatically: after demixing we can accurately recover neural activity from data that has been spatially decimated by an order of magnitude. This offers a cheap method for compressing this large video data, and also implies that it is possible to either speed up imaging significantly, or to \"zoom out\" by a corresponding factor to image order-of-magnitude larger neuronal populations with minimal loss in accuracy or temporal resolution.\n\nAuthor SummaryThe voxel rate of imaging systems ultimately sets the limit on the speed of data acquisition. These limits often mean that only a small fraction of the activity of large neuronal populations can be observed at high spatio-temporal resolution. For imaging of very large populations with single cell resolution, temporal resolution is typically sacrificed. Here we propose a multi-scale approach to achieve single cell precision using fast imaging at reduced spatial resolution. In the first phase the spatial location and shape of each neuron is obtained at standard spatial resolution; in the second phase imaging is performed at much lower spatial resolution. We show that we can apply a demixing algorithm to accurately recover each neurons activity from the low-resolution data by exploiting the high-resolution cellular maps estimated in the first imaging phase. Thus by decreasing the spatial resolution in the second phase, we can compress the video data significantly, and potentially acquire images over an order-of-magnitude larger area, or image at significantly higher temporal resolution, with minimal loss in accuracy of the recovered neuronal activity. We evaluate this approach on real data from light-sheet and 2-photon calcium imaging.

neuroscience↗

bioRxiv2016

Electrical Stimulus Artifact Cancellation and Neural Spike Detection on Large Multi-Electrode Arrays

Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible.\n\nAuthor SummarySimultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these recordings is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is largely stymied by electrical stimulation artifacts across the array, which are typically larger than the signals of interest. We develop a novel computational framework to estimate and subtract away this contaminating artifact, enabling the large-scale analysis of responses of possibly hundreds of cells to tailored stimulation. Importantly, we suggest that this technology may also be helpful for the development of future high-resolution neural prosthetic devices (e.g., retinal prostheses).

neuroscience↗

bioRxiv2016

Scalable variational inference for super resolution microscopy

Super-resolution microscopy methods (e.g. STORM or PALM imaging) have become essential tools in biology, opening up a variety of new questions that were previously inaccessible with standard light microscopy methods. In this paper we develop new Bayesian image processing methods that extend the reach of super-resolution microscopy even further. Our method couples variational inference techniques with a data summarization based on Laplace approximation to ensure computational scalability. Our formulation makes it straightforward to incorporate prior information about the underlying sample to further improve accuracy. The proposed method obtains dramatic resolution improvements over previous methods while retaining computational tractability.

bioinformatics↗