FIOLA: an accelerated pipeline for fluorescence imaging online analysis.

Optical microscopy methods such as calcium and voltage imaging enable fast activity readout of large neuronal populations using light. However, the lack of corresponding advances in online algorithms has slowed progress in retrieving information about neural activity during or shortly after an experiment. This gap not only prevents the execution of real-time closed-loop experiments, but also hampers fast experiment-analysis-theory turnover for high-throughput imaging modalities.

VolPy: Automated and scalable analysis pipelines for voltage imaging datasets.

Voltage imaging enables monitoring neural activity at sub-millisecond and sub-cellular scale, unlocking the study of subthreshold activity, synchrony, and network dynamics with unprecedented spatio-temporal resolution. However, high data rates (>800MB/s) and low signal-to-noise ratios create bottlenecks for analyzing such datasets. Here we present VolPy, an automated and scalable pipeline to pre-process voltage imaging datasets.

Online analysis of microendoscopic 1-photon calcium imaging data streams.

In vivo calcium imaging through microendoscopic lenses enables imaging of neuronal populations deep within the brains of freely moving animals. Previously, a constrained matrix factorization approach (CNMF-E) has been suggested to extract single-neuronal activity from microendoscopic data. However, this approach relies on offline batch processing of the entire video data and is demanding both in terms of computing and memory requirements.

Auditory activity is diverse and widespread throughout the central brain of Drosophila.

Sensory pathways are typically studied by starting at receptor neurons and following postsynaptic neurons into the brain. However, this leads to a bias in analyses of activity toward the earliest layers of processing. Here, we present new methods for volumetric neural imaging with precise across-brain registration to characterize auditory activity throughout the entire central brain of Drosophila and make comparisons across trials, individuals and sexes.

Differential Emergence and Stability of Sensory and Temporal Representations in Context-Specific Hippocampal Sequences.

Hippocampal spiking sequences encode external stimuli and spatiotemporal intervals, linking sequential experiences in memory, but the dynamics controlling the emergence and stability of such diverse representations remain unclear. Using two-photon calcium imaging in CA1 while mice performed an olfactory working-memory task, we recorded stimulus-specific sequences of "odor-cells" encoding olfactory stimuli followed by "time-cells" encoding time points in the ensuing delay. Odor-cells were reliably activated and retained stable fields during changes in trial structure and across days.

Excitatory and Inhibitory Subnetworks Are Equally Selective during Decision-Making and Emerge Simultaneously during Learning.

Inhibitory neurons, which play a critical role in decision-making models, are often simplified as a single pool of non-selective neurons lacking connection specificity. This assumption is supported by observations in the primary visual cortex: inhibitory neurons are broadly tuned in vivo and show non-specific connectivity in slice. The selectivity of excitatory and inhibitory neurons within decision circuits and, hence, the validity of decision-making models are unknown.

Reinforcement Learning Recruits Somata and Apical Dendrites across Layers of Primary Sensory Cortex.

The mammalian brain can form associations between behaviorally relevant stimuli in an animal's environment. While such learning is thought to primarily involve high-order association cortex, even primary sensory areas receive long-range connections carrying information that could contribute to high-level representations. Here, we imaged layer 1 apical dendrites in the barrel cortex of mice performing a whisker-based operant behavior.

Analysis pipelines for calcium imaging data.

Calcium imaging is a popular tool among neuroscientists because of its capability to monitor in vivo large neural populations across weeks with single neuron and single spike resolution. Before any downstream analysis, the data needs to be pre-processed to extract the location and activity of the neurons and processes in the observed field of view. The ever increasing size of calcium imaging datasets necessitates scalable analysis pipelines that are reproducible and fully automated.

Publisher Correction: A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters.

In the version of this article originally published, the bottom of Figure 4f,g was partially truncated in the PDF. The error has been corrected in the PDF version of this article.

A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters.

We developed a new way to engineer complex proteins toward multidimensional specifications using a simple, yet scalable, directed evolution strategy. By robotically picking mammalian cells that were identified, under a microscope, as expressing proteins that simultaneously exhibit several specific properties, we can screen hundreds of thousands of proteins in a library in just a few hours, evaluating each along multiple performance axes. To demonstrate the power of this approach, we created a genetically encoded fluorescent voltage indicator, simultaneously optimizing its brightness and membrane localization using our microscopy-guided cell-picking strategy.

Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data.

In vivo calcium imaging through microendoscopic lenses enables imaging of previously inaccessible neuronal populations deep within the brains of freely moving animals. However, it is computationally challenging to extract single-neuronal activity from microendoscopic data, because of the very large background fluctuations and high spatial overlaps intrinsic to this recording modality. Here, we describe a new constrained matrix factorization approach to accurately separate the background and then demix and denoise the neuronal signals of interest.

NoRMCorre: An online algorithm for piecewise rigid motion correction of calcium imaging data.

Background: Motion correction is a challenging pre-processing problem that arises early in the analysis pipeline of calcium imaging data sequences. The motion artifacts in two-photon microscopy recordings can be non-rigid, arising from the finite time of raster scanning and non-uniform deformations of the brain medium.

New Method: We introduce an algorithm for fast Non-Rigid Motion Correction (NoRMCorre) based on template matching.

Cerebellar granule cells acquire a widespread predictive feedback signal during motor learning.

Cerebellar granule cells, which constitute half the brain's neurons, supply Purkinje cells with contextual information necessary for motor learning, but how they encode this information is unknown. Here we show, using two-photon microscopy to track neural activity over multiple days of cerebellum-dependent eyeblink conditioning in mice, that granule cell populations acquire a dense representation of the anticipatory eyelid movement. Initially, granule cells responded to neutral visual and somatosensory stimuli as well as periorbital airpuffs used for training.

Population-Level Representation of a Temporal Sequence Underlying Song Production in the Zebra Finch.

The zebra finch brain features a set of clearly defined and hierarchically arranged motor nuclei that are selectively responsible for producing singing behavior. One of these regions, a critical forebrain structure called HVC, contains premotor neurons that are active at precise time points during song production. However, the neural representation of this behavior at a population level remains elusive.

Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data.

We present a modular approach for analyzing calcium imaging recordings of large neuronal ensembles. Our goal is to simultaneously identify the locations of the neurons, demix spatially overlapping components, and denoise and deconvolve the spiking activity from the slow dynamics of the calcium indicator. Our approach relies on a constrained nonnegative matrix factorization that expresses the spatiotemporal fluorescence activity as the product of a spatial matrix that encodes the spatial footprint of each neuron in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time.

Simultaneous Multi-plane Imaging of Neural Circuits.

Recording the activity of large populations of neurons is an important step toward understanding the emergent function of neural circuits. Here we present a simple holographic method to simultaneously perform two-photon calcium imaging of neuronal populations across multiple areas and layers of mouse cortex in vivo. We use prior knowledge of neuronal locations, activity sparsity, and a constrained nonnegative matrix factorization algorithm to extract signals from neurons imaged simultaneously and located in different focal planes or fields of view.

Primacy of Flexor Locomotor Pattern Revealed by Ancestral Reversion of Motor Neuron Identity.

Spinal circuits can generate locomotor output in the absence of sensory or descending input, but the principles of locomotor circuit organization remain unclear. We sought insight into these principles by considering the elaboration of locomotor circuits across evolution. The identity of limb-innervating motor neurons was reverted to a state resembling that of motor neurons that direct undulatory swimming in primitive aquatic vertebrates, permitting assessment of the role of motor neuron identity in determining locomotor pattern.

Age-related homeostatic midchannel proteolysis of neuronal L-type voltage-gated Ca²⁺ channels.

Neural circuitry and brain activity depend critically on proper function of voltage-gated calcium channels (VGCCs), whose activity must be tightly controlled. We show that the main body of the pore-forming α1 subunit of neuronal L-type VGCCs, Cav1.2, is proteolytically cleaved, resulting in Cav1.

Spatiotemporal receptive fields of barrel cortex revealed by reverse correlation of synaptic input.

Of all of the sensory areas, barrel cortex is among the best understood in terms of circuitry, yet least understood in terms of sensory function. We combined intracellular recording in rats with a multi-directional, multi-whisker stimulator system to estimate receptive fields by reverse correlation of stimuli to synaptic inputs. Spatiotemporal receptive fields were identified orders of magnitude faster than by conventional spike-based approaches, even for neurons with little spiking activity.

The power of connectivity: identity preserving transformations on visual streams in the spike domain.

We investigate neural architectures for identity preserving transformations (IPTs) on visual stimuli in the spike domain. The stimuli are encoded with a population of spiking neurons; the resulting spikes are processed and finally decoded. A number of IPTs are demonstrated including faithful stimulus recovery, as well as simple transformations on the original visual stimulus such as translations, rotations and zoomings.

Fast spatiotemporal smoothing of calcium measurements in dendritic trees.

We discuss methods for fast spatiotemporal smoothing of calcium signals in dendritic trees, given single-trial, spatially localized imaging data obtained via multi-photon microscopy. By analyzing the dynamics of calcium binding to probe molecules and the effects of the imaging procedure, we show that calcium concentration can be estimated up to an affine transformation, i.e.

Video time encoding machines.

We investigate architectures for time encoding and time decoding of visual stimuli such as natural and synthetic video streams (movies, animation). The architecture for time encoding is akin to models of the early visual system. It consists of a bank of filters in cascade with single-input multi-output neural circuits.

Encoding natural scenes with neural circuits with random thresholds.

We present a general framework for the reconstruction of natural video scenes encoded with a population of spiking neural circuits with random thresholds. The natural scenes are modeled as space-time functions that belong to a space of trigonometric polynomials. The visual encoding system consists of a bank of filters, modeling the visual receptive fields, in cascade with a population of neural circuits, modeling encoding in the early visual system.

Consistent recovery of sensory stimuli encoded with MIMO neural circuits.

We consider the problem of reconstructing finite energy stimuli encoded with a population of spiking leaky integrate-and-fire neurons. The reconstructed signal satisfies a consistency condition: when passed through the same neuron, it triggers the same spike train as the original stimulus. The recovered stimulus has to also minimize a quadratic smoothness optimality criterion.