A fundamental challenge in calcium imaging has been to infer spike rates of neurons from the measured noisy fluorescence traces. We systematically evaluate different spike inference algorithms on a large benchmark dataset (>100,000 spikes) recorded from varying neural tissue (V1 and retina) using different calcium indicators (OGB-1 and GCaMP6). In addition, we introduce a new algorithm based on supervised learning in flexible probabilistic models and find that it performs better than other published techniques. Importantly, it outperforms other algorithms even when applied to entirely new datasets for which no simultaneously recorded data is available. Future data acquired in new experimental conditions can be used to further improve the spike prediction accuracy and generalization performance of the model. Finally, we show that comparing algorithms on artificial data is not informative about performance on real data, suggesting that benchmarking different methods with real-world datasets may greatly facilitate future algorithmic developments in neuroscience.
Reconstruction of firing rate changes across neuronal populations by temporally deconvolved Ca2+ imaging
High-speed in vivo calcium imaging reveals neuronal network activity with near-millisecond precision
Standard errors and confidence intervals in within-subjects designs: generalizing Loftus and Masson (1994) and avoiding the biases of alternative accounts
A finite rate of innovation algorithm for fast and accurate spike detection from two-photon calcium imaging
High-fidelity optical reporting of neuronal electrical activity with an ultrafast fluorescent voltage sensor
An open source tool for automatic spatiotemporal assessment of calcium transients and local 'signal-close-to-noise' activity in calcium imaging data
Fast two-photon imaging of subcellular voltage dynamics in neuronal tissue with genetically encoded indicators
Accurate spike estimation from noisy calcium signals for ultrafast three-dimensional imaging of large neuronal populations in vivo
Calcium Activity Dynamics Correlate with Neuronal Phenotype at a Single Cell Level and in a Threshold-Dependent Manner
Dynamic Tracking Algorithm for Time-Varying Neuronal Network Connectivity using Wide-Field Optical Image Video Sequences.
Three-dimensional imaging and quantification of real-time cytosolic calcium oscillations in microglial cells cultured on electrospun matrices using laser scanning confocal microscopy
Scalable surrogate deconvolution for identification of partially-observable systems and brain modeling.
Improved spike inference accuracy by estimating the peak amplitude of unitary [Ca2+ ] transients in weakly GCaMP6f-expressing hippocampal pyramidal cells
nNOS-expressing interneurons control basal and behaviorally evoked arterial dilation in somatosensory cortex of mice.
Reduced activity of GAD67 expressing cells in the reticular thalamus enhance thalamic excitatory activity and varicella zoster virus associated pain
Cortical Observation by Synchronous Multifocal Optical Sampling Reveals Widespread Population Encoding of Actions.
High-Accuracy Detection of Neuronal Ensemble Activity in Two-Photon Functional Microscopy Using Smart Line Scanning.
MOD: A novel machine-learning optimal-filtering method for accurate and efficient detection of subthreshold synaptic events in vivo.
Brain developing: Influences & Outcomes
This feed focuses on influences that affect the developing brain including genetics, fetal development, prenatal care, and gene-environment interactions. Here is the latest research in this field.