scholarly journals Interpreting wide-band neural activity using convolutional neural networks

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Markus Frey ◽  
Sander Tanni ◽  
Catherine Perrodin ◽  
Alice O'Leary ◽  
Matthias Nau ◽  
...  
Keyword(s):  
Neural Activity ◽  
Calcium Imaging ◽  
Wide Band ◽  
Brain Regions ◽  
Imaging Data ◽  
User Input ◽  
Neural Recordings ◽  
Learning Framework ◽  
Behavioral Variables ◽  
Spatial Behaviors

Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the representation and often depends on manual operations. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning framework able to decode sensory and behavioral variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviors, brain regions, and recording techniques. Once trained, it can be analyzed to determine elements of the neural code that are informative about a given variable. We validated this approach using electrophysiological and calcium-imaging data from rodent auditory cortex and hippocampus as well as human electrocorticography (ECoG) data. We show successful decoding of finger movement, auditory stimuli, and spatial behaviors – including a novel representation of head direction - from raw neural activity.

scholarly journals Interpreting Wide-Band Neural Activity Using Convolutional Neural Networks

2019 ◽  
Author(s):  
Markus Frey ◽  
Sander Tanni ◽  
Catherine Perrodin ◽  
Alice O’Leary ◽  
Matthias Nau ◽  
...  
Keyword(s):  
Calcium Imaging ◽  
Wide Band ◽  
Brain Regions ◽  
Head Direction ◽  
Rapid Progress ◽  
Neural Data ◽  
User Input ◽  
Neural Recordings ◽  
Learning Framework ◽  
Using Data

AbstractRapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data often depends on manual operations and requires considerable knowledge about the nature of the representation. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning-framework able to decode sensory and behavioural variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviours, brain regions, and recording techniques. Once trained, it can be analysed to determine elements of the neural code that are informative about a given variable. We validated this approach using data from rodent auditory cortex and hippocampus, identifying a novel representation of head direction encoded by putative CA1 interneurons.

scholarly journals Model-based decoupling of evoked and spontaneous neural activity in calcium imaging data

2020 ◽  
Vol 16 (11) ◽  
pp. e1008330
Author(s):  
Marcus A. Triplett ◽  
Zac Pujic ◽  
Biao Sun ◽  
Lilach Avitan ◽  
Geoffrey J. Goodhill
Keyword(s):  
Spontaneous Activity ◽  
Neural Activity ◽  
Calcium Imaging ◽  
Temporal Dynamics ◽  
Population Level ◽  
Imaging Data ◽  
Evoked Activity ◽  
Zebrafish Optic Tectum ◽  
Low Dimensional ◽  
Mouse Visual Cortex

The pattern of neural activity evoked by a stimulus can be substantially affected by ongoing spontaneous activity. Separating these two types of activity is particularly important for calcium imaging data given the slow temporal dynamics of calcium indicators. Here we present a statistical model that decouples stimulus-driven activity from low dimensional spontaneous activity in this case. The model identifies hidden factors giving rise to spontaneous activity while jointly estimating stimulus tuning properties that account for the confounding effects that these factors introduce. By applying our model to data from zebrafish optic tectum and mouse visual cortex, we obtain quantitative measurements of the extent that neurons in each case are driven by evoked activity, spontaneous activity, and their interaction. By not averaging away potentially important information encoded in spontaneous activity, this broadly applicable model brings new insight into population-level neural activity within single trials.

scholarly journals Community-based benchmarking improves spike rate inference from two-photon calcium imaging data

2017 ◽  
Author(s):  
Philipp Berens ◽  
Jeremy Freeman ◽  
Thomas Deneux ◽  
Nicolay Chenkov ◽  
Thomas McColgan ◽  
...  
Keyword(s):  
Neural Activity ◽  
Calcium Imaging ◽  
Indirect Measurement ◽  
Generative Models ◽  
Imaging Data ◽  
Crowd Sourcing ◽  
Two Photon ◽  
Wide Range ◽  
Improved Performance ◽  
Highly Correlated

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike trains from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

scholarly journals Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data

2019 ◽  
Author(s):  
Shreya Saxena ◽  
Ian Kinsella ◽  
Simon Musall ◽  
Sharon H. Kim ◽  
Jozsef Meszaros ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Calcium Imaging ◽  
Large Scale ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Brain Regions ◽  
Video Data ◽  
Imaging Data ◽  
Dorsal Cortex ◽  
Experimental Conditions

Widefield calcium imaging enables recording of large-scale neural activity across the mouse dorsal cortex. In order to examine the relationship of these neural signals to the resulting behavior, it is critical to demix the recordings into meaningful spatial and temporal components that can be mapped onto well-defined brain regions. However, no current tools satisfactorily extract the activity of the different brain regions in individual mice in a data-driven manner, while taking into account mouse-specific and preparation-specific differences. Here, we introduce Localized semi-Nonnegative Matrix Factorization (LocaNMF), a method that efficiently decomposes widefield video data and allows us to directly compare activity across multiple mice by outputting mouse-specific localized functional regions that are significantly more interpretable than more traditional decomposition techniques. Moreover, it provides a natural subspace to directly compare correlation maps and neural dynamics across different behaviors, mice, and experimental conditions, and enables identification of task- and movement-related brain regions.

scholarly journals CaPTure: Calcium PeakToolbox for analysis of in vitro calcium imaging data

2021 ◽  
Author(s):  
Madhavi Tippani ◽  
Elizabeth A. Pattie ◽  
Brittany A. Davis ◽  
Claudia V. Nguyen ◽  
Yanhong Wang ◽  
...  
Keyword(s):  
High Throughput ◽  
Calcium Imaging ◽  
High Throughput Screening ◽  
Cultured Cells ◽  
Rapid Assessment ◽  
Imaging Data ◽  
User Input ◽  
Large Populations ◽  
Parameter Modification

ABSTRACTBackgroundCalcium imaging is a powerful technique for recording cellular activity across large populations of neurons. However, analysis methods capable of single-cell resolution in cultured neurons, especially for cultures derived from human induced pluripotent stem cells (hiPSCs), are lacking. Existing methods lack scalability to accommodate high-throughput comparisons between multiple lines, across developmental timepoints, or across pharmacological manipulations.ResultsWe developed a scalable, automated Ca2+ imaging analysis pipeline called CaPTure (https://github.com/LieberInstitute/CaPTure). This method detects neurons, classifies and quantifies spontaneous activity, quantifies synchrony metrics, and generates cell- and network-specific metrics that facilitate phenotypic discovery. The method is compatible with parallel processing on computing clusters without requiring significant user input or parameter modification.ConclusionCaPTure allows for rapid assessment of neuronal activity in cultured cells at cellular resolution, rendering it amenable to high-throughput screening and phenotypic discovery. The platform can be applied to both human- and rodent-derived neurons and is compatible with many imaging systems.

scholarly journals PatchWarp: Corrections of non-uniform image distortions in two-photon calcium imaging data by patchwork affine transformations

2021 ◽  
Author(s):  
Ryoma Hattori ◽  
Takaki Komiyama
Keyword(s):  
Neural Activity ◽  
Calcium Imaging ◽  
Motion Correction ◽  
Extended Period ◽  
Affine Transformations ◽  
Imaging Data ◽  
Full Field ◽  
Two Photon ◽  
Automated Method ◽  
Wide Range

Two-photon microscopy has been widely used to record the activity of populations of individual neurons at high spatial resolution in behaving animals. The ability to perform imaging for an extended period of time allows the investigation of activity changes associated with behavioral states and learning. However, imaging often accompanies shifts of the imaging field, including rapid (~100ms) translation and slow, spatially non-uniform distortion. To combat this issue and obtain a stable time series of the target structures, motion correction algorithms are commonly applied. However, typical motion correction algorithms are limited to full field translation of images and are unable to correct non-uniform distortions. Here, we developed a novel algorithm, PatchWarp, to robustly correct slow image distortion for calcium imaging data. PatchWarp is a two-step algorithm with rigid and non-rigid image registrations. To correct non-uniform image distortions, it splits the imaging field and estimates the best affine transformation matrix for each of the subfields. The distortion-corrected subfields are stitched together like a patchwork to reconstruct the distortion-corrected imaging field. We show that PatchWarp robustly corrects image distortions of calcium imaging data collected from various cortical areas through glass window or GRIN lens with a higher accuracy than existing non-rigid algorithms. Furthermore, it provides a fully automated method of registering images from different imaging sessions for longitudinal neural activity analyses. PatchWarp improves the quality of neural activity analyses and would be useful as a general approach to correct image distortions in a wide range of disciplines.

scholarly journals Neural basis of functional ultrasound signals

2021 ◽  
Author(s):  
Anwar O. Nunez-Elizalde ◽  
Michael Krumin ◽  
Charu Bai Reddy ◽  
Gabriel Montaldo ◽  
Alan Urban ◽  
...  
Keyword(s):  
Firing Rate ◽  
Neural Activity ◽  
Brain Regions ◽  
Neuronal Firing ◽  
Linear Filter ◽  
Neural Firing ◽  
Hemodynamic Response Function ◽  
Neural Basis ◽  
Neural Recordings ◽  
Highly Correlated

SummaryFunctional ultrasound imaging (fUSI) is a popular method for studying brain function, but it remains unclear to what degree its signals reflect neural activity on a trial-by-trial basis. Here, we answer this question with simultaneous fUSI and neural recordings with Neuropixels probes in awake mice. fUSI signals strongly correlated with the slow (<0.3 Hz) fluctuations in firing rate measured in the same location and were closely predicted by convolving the firing rate with a 2.9 s wide linear filter. This filter matched the hemodynamic response function of awake mouse and was invariant across mice, stimulus conditions, and brain regions. fUSI signals matched neural firing also spatially: recordings with two probes revealed that firing rates were as highly correlated across hemispheres as fUSI signals. We conclude that fUSI signals bear a simple linear relationship to neuronal firing and accurately reflect neural activity both in time and in space.

scholarly journals Model-based decoupling of evoked and spontaneous neural activity in calcium imaging data

2019 ◽  
Author(s):  
Marcus A. Triplett ◽  
Zac Pujic ◽  
Biao Sun ◽  
Lilach Avitan ◽  
Geoffrey J. Goodhill
Keyword(s):  
Spontaneous Activity ◽  
Neural Activity ◽  
Calcium Imaging ◽  
Temporal Dynamics ◽  
Population Level ◽  
Imaging Data ◽  
Evoked Activity ◽  
Zebrafish Optic Tectum ◽  
Low Dimensional ◽  
Mouse Visual Cortex

AbstractThe pattern of neural activity evoked by a stimulus can be substantially affected by ongoing spontaneous activity. Separating these two types of activity is particularly important for calcium imaging data given the slow temporal dynamics of calcium indicators. Here we present a statistical model that decouples stimulus-driven activity from low dimensional spontaneous activity in this case. The model identifies hidden factors giving rise to spontaneous activity while jointly estimating stimulus tuning properties that account for the confounding effects that these factors introduce. By applying our model to data from zebrafish optic tectum and mouse visual cortex, we obtain quantitative measurements of the extent that neurons in each case are driven by evoked activity, spontaneous activity, and their interaction. This broadly applicable model brings new insight into population-level neural activity in single trials without averaging away potentially important information encoded in spontaneous activity.

Deconvolution of Sustained Neural Activity From Large-Scale Calcium Imaging Data

2020 ◽  
Vol 39 (4) ◽  
pp. 1094-1103
Author(s):  
Younes Farouj ◽  
Fikret Isik Karahanoglu ◽  
Dimitri Van De Ville
Keyword(s):  
Neural Activity ◽  
Calcium Imaging ◽  
Large Scale ◽  
Imaging Data
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