Voltage imaging and optogenetics reveal behaviour-dependent changes in hippocampal dynamics

Mechanism of sensory perception unveiled by simultaneous measurement of membrane voltage and intracellular calcium

Measuring neuronal activity is important for understanding neuronal function. Ca2+ imaging by genetically encoded calcium indicators (GECIs) is a powerful way to measure neuronal activity. Although it revealed important aspects of neuronal function, measuring the neuronal membrane voltage is important to understand neuronal function as it triggers neuronal activation. Recent progress of genetically encoded voltage indicators (GEVIs) enabled us fast and precise measurements of neuronal membrane voltage. To clarify the relation of the membrane voltage and intracellular Ca2+, we analyzed neuronal activities of olfactory neuron AWA in Caenorhabditis elegans by GCaMP6f (GECI) and paQuasAr3 (GEVI) responding to odorants. We found that the membrane voltage encodes the stimuli change by the timing and the duration by the weak semi-stable depolarization. However, the change of the intracellular Ca2+ encodes the strength of the stimuli. Furthermore, ODR-3, a G-protein alpha subunit, was shown to be important for stabilizing the membrane voltage. These results suggest that the combination of calcium and voltage imaging provides a deeper understanding of the information in neural circuits.

Sub-threshold neuronal activity and the dynamical regime of cerebral cortex

Cortical neurons exhibit temporally irregular spiking patterns and heterogeneous firing rates. These features arise in model circuits operating in a 'fluctuation-driven regime', in which fluctuations in membrane potentials emerge from the network dynamics. However, it is still debated whether the cortex operates in such a regime. We evaluated the fluctuation-driven hypothesis by analyzing spiking and sub-threshold membrane potentials of neurons in the frontal cortex of mice performing a decision-making task. We showed that while standard fluctuation-driven models successfully account for spiking statistics, they fall short in capturing the heterogeneity in sub-threshold activity. This limitation is an inevitable outcome of bombarding single-compartment neurons with a large number of pre-synaptic inputs, thereby clamping the voltage of all neurons to more or less the same average voltage. To address this, we effectively incorporated dendritic morphology into the standard models. Inclusion of dendritic morphology in the neuronal models increased neuronal selectivity and reduced error trials, suggesting a functional role for dendrites during decision-making. Our work suggests that, during decision-making, cortical neurons in high-order cortical areas operate in a fluctuation-driven regime.

Linking brain activity across scales with simultaneous opto- and electrophysiology

The brain enables adaptive behavior via the dynamic coordination of diverse neuronal signals across spatial and temporal scales: from fast action potential patterns in microcircuits to slower patterns of distributed activity in brain-wide networks. Understanding principles of multiscale dynamics requires simultaneous monitoring of signals in multiple, distributed network nodes. Combining optical and electrical recordings of brain activity is promising for collecting data across multiple scales and can reveal aspects of coordinated dynamics invisible to standard, single-modality approaches. We review recent progress in combining opto- and electrophysiology, focusing on mouse studies that shed new light on the function of single neurons by embedding their activity in the context of brain-wide activity patterns. Optical and electrical readouts can be tailored to desired scales to tackle specific questions. For example, fast dynamics in single cells or local populations recorded with multi-electrode arrays can be related to simultaneously acquired optical signals that report activity in specified subpopulations of neurons, in non-neuronal cells, or in neuromodulatory pathways. Conversely, two-photon imaging can be used to densely monitor activity in local circuits while sampling electrical activity in distant brain areas at the same time. The refinement of combined approaches will continue to reveal previously inaccessible and under-appreciated aspects of coordinated brain activity.

Cl--dependent amplification of excitatory synaptic potentials at distal dendrites revealed by voltage imaging

The processing of synaptic signals in somatodendritic compartments determines neuronal computation. Although the amplification of excitatory signals by local voltage-dependent cation channels has been extensively studied, their spatiotemporal dynamics in elaborate dendritic branches remain obscure owing to technical limitations. Using fluorescent voltage imaging throughout dendritic arborizations in hippocampal pyramidal neurons, we demonstrate a unique chloride ion (Cl-)-dependent remote computation mechanism in the distal branches. Excitatory postsynaptic potentials triggered by local laser photolysis of caged glutamate spread along dendrites, with gradual amplification toward the distal end while attenuation toward the soma. Tour de force subcellular patch-clamp recordings from thin branches complemented by biophysical model simulations revealed that the asymmetric augmentation of excitation relies on tetrodotoxin-resistant sodium ion (Na+) channels and Cl- conductance accompanied by a more hyperpolarized dendritic resting potential. Together, this study reveals the cooperative voltage-dependent actions of cation and anion conductance for dendritic supralinear computation, which can locally decode the spatiotemporal context of synaptic inputs.

Epilepsy insights revealed by intravital functional optical imaging

Despite an abundance of pharmacologic and surgical epilepsy treatments, there remain millions of patients suffering from poorly controlled seizures. One approach to closing this treatment gap may be found through a deeper mechanistic understanding of the network alterations that underly this aberrant activity. Functional optical imaging in vertebrate models provides powerful advantages to this end, enabling the spatiotemporal acquisition of individual neuron activity patterns across multiple seizures. This coupled with the advent of genetically encoded indicators, be them for specific ions, neurotransmitters or voltage, grants researchers unparalleled access to the intact nervous system. Here, we will review how in vivo functional optical imaging in various vertebrate seizure models has advanced our knowledge of seizure dynamics, principally seizure initiation, propagation and termination.

Imaging high-frequency voltage dynamics in multiple neuron classes of behaving mammals

Fluorescent genetically encoded voltage indicators report transmembrane potentials of targeted cell-types. However, voltage-imaging instrumentation has lacked the sensitivity to track spontaneous or evoked high-frequency voltage oscillations in neural populations. Here we describe two complementary TEMPO voltage-sensing technologies that capture neural oscillations up to ~100 Hz. Fiber-optic TEMPO achieves ~10-fold greater sensitivity than prior photometry systems, allows hour-long recordings, and monitors two neuron-classes per fiber-optic probe in freely moving mice. With it, we uncovered cross-frequency-coupled theta- and gamma-range oscillations and characterized excitatory-inhibitory neural dynamics during hippocampal ripples and visual cortical processing. The TEMPO mesoscope images voltage activity in two cell-classes across a ~8-mm-wide field-of-view in head-fixed animals. In awake mice, it revealed sensory-evoked excitatory-inhibitory neural interactions and traveling gamma and 3-7 Hz waves in the visual cortex, and previously unreported propagation directions for hippocampal theta and beta waves. These technologies have widespread applications probing diverse oscillations and neuron-type interactions in healthy and diseased brains.

Dynamic assemblies of parvalbumin interneurons in brain oscillations

Brain oscillations are crucial for perception, memory, and behavior. Parvalbumin-expressing (PV) interneurons are critical for these oscillations, but their population dynamics remain unclear. Using voltage imaging, we simultaneously recorded membrane potentials in up to 26 PV interneurons in vivo during hippocampal ripple oscillations in mice. We found that PV cells generate ripple-frequency rhythms by forming highly dynamic cell assemblies. These assemblies exhibit rapid and significant changes from cycle to cycle, varying greatly in both size and membership. Importantly, this variability is not just random spiking failures of individual neurons. Rather, the activities of other PV cells contain significant information about whether a PV cell spikes or not in a given cycle. This coordination persists without network oscillations, and it exists in subthreshold potentials even when the cells are not spiking. Dynamic assemblies of interneurons may provide a new mechanism to modulate postsynaptic dynamics and impact cognitive functions flexibly and rapidly.

Large field-of-view metabolic profiling of murine brain tissue following morphine incubation using label-free multiphoton microscopy

BACKGROUND

Although the effects on neural activation and glucose consumption caused by opiates such as morphine are known, the metabolic machinery underlying opioid use and misuse is not fully explored. Multiphoton microscopy (MPM) techniques have been developed for optical imaging at high spatial resolution. Despite the increased use of MPM for neural imaging, the use of intrinsic optical contrast has seen minimal use in neuroscience.

NEW METHOD

We present a label-free, multimodal microscopy technique for metabolic profiling of murine brain tissue following incubation with morphine sulfate (MSO4). We evaluate two- and three-photon excited autofluorescence, and second and third harmonic generation to determine meaningful intrinsic contrast mechanisms in brain tissue using simultaneous label-free, autofluorescence multi-harmonic (SLAM) microscopy.

RESULTS

Regional differences quantified in the cortex, caudate, and thalamus of the brain demonstrate region-specific changes to metabolic profiles measured from FAD intensity, along with brain-wide quantification. While the overall intensity of FAD signal significantly decreased after morphine incubation, this metabolic molecule accumulated near the nucleus accumbens.

COMPARISON WITH EXISTING METHODS

Histopathology requires tissue fixation and staining to determine cell type and morphology, lacking information about cellular metabolism. Tools such as fMRI or PET imaging have been widely used, but lack cellular resolution. SLAM microscopy obviates the need for tissue preparation, permitting immediate use and imaging of tissue with subcellular resolution in its native environment.

CONCLUSIONS

This study demonstrates the utility of SLAM microscopy for label-free investigations of neural metabolism, especially the intensity changes in FAD autofluorescence and structural morphology from third-harmonic generation.

Functional architecture of intracellular oscillations in hippocampal dendrites

Fast electrical signaling in dendrites is central to neural computations that support adaptive behaviors. Conventional techniques lack temporal and spatial resolution and the ability to track underlying membrane potential dynamics present across the complex three-dimensional dendritic arbor in vivo. Here, we perform fast two-photon imaging of dendritic and somatic membrane potential dynamics in single pyramidal cells in the CA1 region of the mouse hippocampus during awake behavior. We study the dynamics of subthreshold membrane potential and suprathreshold dendritic events throughout the dendritic arbor in vivo by combining voltage imaging with simultaneous local field potential recording, post hoc morphological reconstruction, and a spatial navigation task. We systematically quantify the modulation of local event rates by locomotion in distinct dendritic regions, report an advancing gradient of dendritic theta phase along the basal-tuft axis, and describe a predominant hyperpolarization of the dendritic arbor during sharp-wave ripples. Finally, we find that spatial tuning of dendritic representations dynamically reorganizes following place field formation. Our data reveal how the organization of electrical signaling in dendrites maps onto the anatomy of the dendritic tree across behavior, oscillatory network, and functional cell states.

Imaging different cell populations in the mouse olfactory bulb using the genetically encoded voltage indicator ArcLight

Genetically encoded voltage indicators (GEVIs) are protein-based optical sensors that allow for measurements from genetically defined populations of neurons. Although in vivo imaging in the mammalian brain with early generation GEVIs was difficult due to poor membrane expression and low signal-to-noise ratio, newer and more sensitive GEVIs have begun to make them useful for answering fundamental questions in neuroscience. We discuss principles of imaging using GEVIs and genetically encoded calcium indicators, both useful tools for in vivo imaging of neuronal activity, and review some of the recent mechanistic advances that have led to GEVI improvements. We provide an overview of the mouse olfactory bulb (OB) and discuss recent studies using the GEVI ArcLight to study different cell types within the bulb using both widefield and two-photon microscopy. Specific emphasis is placed on using GEVIs to begin to study the principles of concentration coding in the OB, how to interpret the optical signals from population measurements in the in vivo brain, and future developments that will push the field forward.

Multiphoton imaging of hippocampal neural circuits: techniques and biological insights into region-, cell-type-, and pathway-specific functions

Significance

The function of the hippocampus in behavior and cognition has long been studied primarily through electrophysiological recordings from freely moving rodents. However, the application of optical recording methods, particularly multiphoton fluorescence microscopy, in the last decade or two has dramatically advanced our understanding of hippocampal function. This article provides a comprehensive overview of techniques and biological findings obtained from multiphoton imaging of hippocampal neural circuits.

Aim

This review aims to summarize and discuss the recent technical advances in multiphoton imaging of hippocampal neural circuits and the accumulated biological knowledge gained through this technology.

Approach

First, we provide a brief overview of various techniques of multiphoton imaging of the hippocampus and discuss its advantages, drawbacks, and associated key innovations and practices. Then, we review a large body of findings obtained through multiphoton imaging by region (CA1 and dentate gyrus), cell type (pyramidal neurons, inhibitory interneurons, and glial cells), and cellular compartment (dendrite and axon).

Results

Multiphoton imaging of the hippocampus is primarily performed under head-fixed conditions and can reveal detailed mechanisms of circuit operation owing to its high spatial resolution and specificity. As the hippocampus lies deep below the cortex, its imaging requires elaborate methods. These include imaging cannula implantation, microendoscopy, and the use of long-wavelength light sources. Although many studies have focused on the dorsal CA1 pyramidal cells, studies of other local and inter-areal circuitry elements have also helped provide a more comprehensive picture of the information processing performed by the hippocampal circuits. Imaging of circuit function in mouse models of Alzheimer's disease and other brain disorders such as autism spectrum disorder has also contributed greatly to our understanding of their pathophysiology.

Conclusions

Multiphoton imaging has revealed much regarding region-, cell-type-, and pathway-specific mechanisms in hippocampal function and dysfunction in health and disease. Future technological advances will allow further illustration of the operating principle of the hippocampal circuits via the large-scale, high-resolution, multimodal, and minimally invasive imaging.

Open Data In Neurophysiology: Advancements, Solutions & Challenges

Across the life sciences, an ongoing effort over the last 50 years has made data and methods more reproducible and transparent. This openness has led to transformative insights and vastly accelerated scientific progress1,2. For example, structural biology3 and genomics4,5 have undertaken systematic collection and publication of protein sequences and structures over the past half-century, and these data have led to scientific breakthroughs that were unthinkable when data collection first began (e.g.6). We believe that neuroscience is poised to follow the same path, and that principles of open data and open science will transform our understanding of the nervous system in ways that are impossible to predict at the moment. To this end, new social structures along with active and open scientific communities are essential7 to facilitate and expand the still limited adoption of open science practices in our field8. Unified by shared values of openness, we set out to organize a symposium for Open Data in Neuroscience (ODIN) to strengthen our community and facilitate transformative neuroscience research at large. In this report, we share what we learned during this first ODIN event. We also lay out plans for how to grow this movement, document emerging conversations, and propose a path toward a better and more transparent science of tomorrow.

Optical constraints on two-photon voltage imaging

Significance

Genetically encoded voltage indicators (GEVIs) are a valuable tool for studying neural circuits in vivo, but the relative merits and limitations of one-photon (1P) versus two-photon (2P) voltage imaging are not well characterized.

Aim

We consider the optical and biophysical constraints particular to 1P and 2P voltage imaging and compare the imaging properties of commonly used GEVIs under 1P and 2P excitation.

Approach

We measure the brightness and voltage sensitivity of voltage indicators from commonly used classes under 1P and 2P illumination. We also measure the decrease in fluorescence as a function of depth in the mouse brain. We develop a simple model of the number of measurable cells as a function of reporter properties, imaging parameters, and desired signal-to-noise ratio (SNR). We then discuss how the performance of voltage imaging would be affected by sensor improvements and by recently introduced advanced imaging modalities.

Results

Compared with 1P excitation, 2P excitation requires ∼ 10 4 -fold more illumination power per cell to produce similar photon count rates. For voltage imaging with JEDI-2P in the mouse cortex with a target SNR of 10 (spike height to baseline shot noise), a measurement bandwidth of 1 kHz, a thermally limited laser power of 200 mW, and an imaging depth of > 300    μ m , 2P voltage imaging using an 80-MHz source can record from no more than ∼ 12 neurons simultaneously.

Conclusions

Due to the stringent photon-count requirements of voltage imaging and the modest voltage sensitivity of existing reporters, 2P voltage imaging in vivo faces a stringent tradeoff between shot noise and tissue photodamage. 2P imaging of hundreds of neurons with high SNR at a depth of > 300    μ m will require either major improvements in 2P GEVIs or qualitatively new approaches to imaging.

Scanless two-photon voltage imaging

Two-photon voltage imaging has long been heralded as a transformative approach capable of answering many long-standing questions in modern neuroscience. However, exploiting its full potential requires the development of novel imaging approaches well suited to the photophysical properties of genetically encoded voltage indicators. We demonstrate that parallel excitation approaches developed for scanless two-photon photostimulation enable high-SNR two-photon voltage imaging. We use whole-cell patch-clamp electrophysiology to perform a thorough characterization of scanless two-photon voltage imaging using three parallel illumination approaches and lasers with different repetition rates and wavelengths. We demonstrate voltage recordings of high-frequency spike trains and sub-threshold depolarizations from neurons expressing the soma-targeted genetically encoded voltage indicator JEDI-2P-Kv. Using a low repetition-rate laser, we perform multi-cell recordings from up to fifteen targets simultaneously. We co-express JEDI-2P-Kv and the channelrhodopsin ChroME-ST and capitalize on their overlapping two-photon absorption spectra to simultaneously evoke and image action potentials using a single laser source. We also demonstrate in vivo scanless two-photon imaging of multiple cells simultaneously up to 250 µm deep in the barrel cortex of head-fixed, anaesthetised mice.

All-optical voltage imaging-guided postsynaptic single-cell transcriptome profiling with Voltage-Seq

Neuronal pathways recruit large postsynaptic populations and maintain connections via distinct postsynaptic response types (PRTs). Until recently, PRTs were accessible as a selection criterion for single-cell RNA sequencing only through probing by low-throughput whole-cell electrophysiology. To overcome these limitations and target neurons on the basis of specific PRTs for soma collection and subsequent single-cell RNA sequencing, we developed Voltage-Seq using the genetically encoded voltage indicator Voltron in acute brain slices from mice. We also created an onsite analysis tool, VoltView, to guide soma collection of specific PRTs using a classifier based on a previously acquired database of connectomes from multiple animals. Here we present our procedure for preparing the optical path, the imaging setup and detailing the imaging and analysis steps, as well as a complete procedure for sequencing library preparation. This enables researchers to conduct our high-throughput all-optical synaptic assay and to obtain single-cell transcriptomic data from selected postsynaptic neurons. This also allows researchers to resolve the connectivity ratio of a specific pathway and explore the diversity of PRTs within that connectome. Furthermore, combining high throughput with quick analysis gives unique access to find specific connections within a large postsynaptic connectome. Voltage-Seq also allows the investigation of correlations between connectivity and gene expression changes in a postsynaptic cell-type-specific manner for both excitatory and inhibitory connections. The Voltage-Seq workflow can be completed in ~6 weeks, including 4-5 weeks for viral expression of the Voltron sensor. The technique requires knowledge of basic laboratory techniques, micromanipulator handling skills and experience in molecular biology and bioinformatics.

Large-scale deep tissue voltage imaging with targeted-illumination confocal microscopy

Voltage imaging with cellular specificity has been made possible by advances in genetically encoded voltage indicators. However, the kilohertz rates required for voltage imaging lead to weak signals. Moreover, out-of-focus fluorescence and tissue scattering produce background that both undermines the signal-to-noise ratio and induces crosstalk between cells, making reliable in vivo imaging in densely labeled tissue highly challenging. We describe a microscope that combines the distinct advantages of targeted illumination and confocal gating while also maximizing signal detection efficiency. The resulting benefits in signal-to-noise ratio and crosstalk reduction are quantified experimentally and theoretically. Our microscope provides a versatile solution for enabling high-fidelity in vivo voltage imaging at large scales and penetration depths, which we demonstrate across a wide range of imaging conditions and different genetically encoded voltage indicator classes.

An NIR-IIb emissive transmembrane voltage nano-indicator for the optical monitoring of electrophysiological activities in vivo

In vivo transmembrane-voltage detection reflected the electrophysiological activities of the biological system, which is crucial for the diagnosis of neuronal disease. Traditional implanted electrodes can only monitor limited regions and induce relatively large tissue damage. Despite emerging monitoring methods based on optical imaging have access to signal recording in a larger area, the recording wavelength of less than 1000 nm seriously weakens the detection depth and resolution in vivo. Herein, a Förster resonance energy transfer (FRET)-based nano-indicator, NaYbF4:Er@NaYF4@Cy7.5@DPPC (Cy7.5-ErNP) with emission in the near-infrared IIb biological window (NIR-IIb, 1500-1700 nm) is developed for transmembrane-voltage detection. Cy7.5 dye is found to be voltage-sensitive and is employed as the energy donor for the energy transfer to the lanthanide nanoparticle, NaYbF4:Er@NaYF4 (ErNP), which works as the acceptor to achieve electrophysiological signal responsive NIR-IIb luminescence. Benefiting from the high penetration and low scattering of NIR-IIb luminescence, the Cy7.5-ErNP enables both the visualization of action potential in vitro and monitoring of Mesial Temporal lobe epilepsy (mTLE) disease in vivo. This work presents a concept for leveraging the lanthanide luminescent nanoprobes to visualize electrophysiological activity in vivo, which facilitates the development of an optical nano-indicator for the diagnosis of neurological disorders.

Dendritic excitations govern back-propagation via a spike-rate accelerometer

Dendrites on neurons support nonlinear electrical excitations, but the computational significance of these events is not well understood. We developed molecular, optical, and analytical tools to map sub-millisecond voltage dynamics throughout the dendritic trees of CA1 pyramidal neurons under diverse optogenetic and synaptic stimulus patterns, in acute brain slices. We observed history-dependent spike back-propagation in distal dendrites, driven by locally generated Na+ spikes (dSpikes). Dendritic depolarization created a transient window for dSpike propagation, opened by A-type K V channel inactivation, and closed by slow N a V inactivation. Collisions of dSpikes with synaptic inputs triggered calcium channel and N-methyl-D-aspartate receptor (NMDAR)-dependent plateau potentials, with accompanying complex spikes at the soma. This hierarchical ion channel network acts as a spike-rate accelerometer, providing an intuitive picture of how dendritic excitations shape associative plasticity rules.

Characterization of two near-infrared genetically encoded voltage indicators

Significance

Efforts starting more than 20 years ago led to increasingly well performing genetically encoded voltage indicators (GEVIs) for optical imaging at wavelengths < 600    nm . Although optical imaging in the > 600    nm wavelength range has many advantages over shorter wavelength approaches for mesoscopic in vivo monitoring of neuronal activity in the mammalian brain, the availability and evaluation of well performing near-infrared GEVIs are still limited.

Aim

Here, we characterized two recent near-infrared GEVIs, Archon1 and nirButterfly, to support interested tool users in selecting a suitable near-infrared GEVI for their specific research question requirements.

Approach

We characterized side-by-side the brightness, sensitivity, and kinetics of both near-infrared GEVIs in a setting focused on population imaging.

Results

We found that nirButterfly shows seven-fold higher brightness than Archon1 under the same conditions and faster kinetics than Archon1 for population imaging without cellular resolution. But Archon1 showed larger signals than nirButterfly.

Conclusions

Neither GEVI characterized here surpasses in all three key parameters (brightness, kinetics, and sensitivity), so there is no unequivocal preference for one of the two. Our side-by-side characterization presented here provides new information for future in vitro and ex vivo experimental designs.

Intracellular neural control of an active feeding structure in Aplysia using a carbon fiber electrode array

BACKGROUND

To study neural control of behavior, intracellular recording and stimulation of many neurons in freely moving animals would be ideal. However, current technologies limit the number of neurons that can be monitored and manipulated. A new technology has become available for intracellular recording and stimulation which we demonstrate in the tractable nervous system of Aplysia.

NEW METHOD

Carbon fiber electrode arrays (whose tips are coated with platinum-iridium) were used with an in vitro feeding preparation to intracellularly record from and to control the activity of multiple neurons during feeding movements.

RESULTS

In an in vitro feeding preparation, the carbon fiber electrode arrays recorded action potentials and subthreshold synaptic potentials during feeding movements. Depolarizing or hyperpolarizing currents activated or inhibited identified neurons (respectively), manipulating the movements of the feeding apparatus.

COMPARISON WITH EXISTING METHOD(S)

Standard glass microelectrodes that are commonly used for intracellular recording are stiff, liable to break in response to movement, and require many micromanipulators to be precisely positioned. In contrast, carbon fiber arrays are less sensitive to movement, but are capable of multiple channels of intracellular recording and stimulation.

CONCLUSIONS

Carbon fiber arrays are a novel technology for intracellular recording that can be used in moving preparations. They can record both action potentials and synaptic activity in multiple neurons and can be used to stimulate multiple neurons in complex patterns.

In vivo imaging of the neuronal response to spinal cord injury: a narrative review

Deciphering the neuronal response to injury in the spinal cord is essential for exploring treatment strategies for spinal cord injury (SCI). However, this subject has been neglected in part because appropriate tools are lacking. Emerging in vivo imaging and labeling methods offer great potential for observing dynamic neural processes in the central nervous system in conditions of health and disease. This review first discusses in vivo imaging of the mouse spinal cord with a focus on the latest imaging techniques, and then analyzes the dynamic biological response of spinal cord sensory and motor neurons to SCI. We then summarize and compare the techniques behind these studies and clarify the advantages of in vivo imaging compared with traditional neuroscience examinations. Finally, we identify the challenges and possible solutions for spinal cord neuron imaging.

Hippocampome.org 2.0 is a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits

Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Previously, Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression (Wheeler et al., 2015). Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.

Hippocampome.org v2.0: a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits

Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression. Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.

Bio-friendly long-term subcellular dynamic recording by self-supervised image enhancement microscopy

Fluorescence microscopy has become an indispensable tool for revealing the dynamic regulation of cells and organelles. However, stochastic noise inherently restricts optical interrogation quality and exacerbates observation fidelity when balancing the joint demands of high frame rate, long-term recording and low phototoxicity. Here we propose DeepSeMi, a self-supervised-learning-based denoising framework capable of increasing signal-to-noise ratio by over 12 dB across various conditions. With the introduction of newly designed eccentric blind-spot convolution filters, DeepSeMi effectively denoises images with no loss of spatiotemporal resolution. In combination with confocal microscopy, DeepSeMi allows for recording organelle interactions in four colors at high frame rates across tens of thousands of frames, monitoring migrasomes and retractosomes over a half day, and imaging ultra-phototoxicity-sensitive Dictyostelium cells over thousands of frames. Through comprehensive validations across various samples and instruments, we prove DeepSeMi to be a versatile and biocompatible tool for breaking the shot-noise limit.

Compact Electrochromic Optical Recording of Bioelectric Potentials

Electrochromic optical recording (ECORE) is a label-free method that utilizes electrochromism to optically detect electrical signals in biological cells with a high signal-to-noise ratio and is suitable for long-term recording. However, ECORE usually requires a large and intricate optical setup, making it relatively difficult to transport and to study specimens on a large scale. Here, we present a Compact ECORE (CECORE) apparatus that drastically reduces the spatial footprint and complexity of the ECORE setup whilst maintaining high sensitivity. An autobalancing differential photodetector automates common-mode noise rejection, removing the need for manually adjustable optics, and a compact laser module conserves space compared to a typical laser mount. The result is a simple, easy-to-use, and relatively low cost system that achieves a sensitivity of 16.7 μV (within a factor of 5 of the shot noise limit), and reliably detects action potentials from Human-induced pluripotent stem cell (HiPSC) derived cardiomyocytes. This setup can be further improved to within 1.5 dB of the shot noise limit by filtering out power-line interference.

Bright and sensitive red voltage indicators for imaging action potentials in brain slices and pancreatic islets

Genetically encoded voltage indicators (GEVIs) allow the direct visualization of cellular membrane potential at the millisecond time scale. Among these, red-emitting GEVIs have been reported to support multichannel recordings and manipulation of cellular activities with reduced autofluorescence background. However, the limited sensitivity and dimness of existing red GEVIs have restricted their applications in neuroscience. Here, we report a pair of red-shifted opsin-based GEVIs, Cepheid1b and Cepheid1s, with improved dynamic range, brightness, and photostability. The improved dynamic range is achieved by a rational design to raise the electrochromic Förster resonance energy transfer efficiency, and the higher brightness and photostability are approached with separately engineered red fluorescent proteins. With Cepheid1 indicators, we recorded complex firings and subthreshold activities of neurons on acute brain slices and observed heterogeneity in the voltage‑calcium coupling on pancreatic islets. Overall, Cepheid1 indicators provide a strong tool to investigate excitable cells in various sophisticated biological systems.

Current Status and Future Strategies for Advancing Functional Circuit Mapping In Vivo

The human brain represents one of the most complex biological systems, containing billions of neurons interconnected through trillions of synapses. Inherent to the brain is a biochemical complexity involving ions, signaling molecules, and peptides that regulate neuronal activity and allow for short- and long-term adaptations. Large-scale and noninvasive imaging techniques, such as fMRI and EEG, have highlighted brain regions involved in specific functions and visualized connections between different brain areas. A major shortcoming, however, is the need for more information on specific cell types and neurotransmitters involved, as well as poor spatial and temporal resolution. Recent technologies have been advanced for neuronal circuit mapping and implemented in behaving model organisms to address this. Here, we highlight strategies for targeting specific neuronal subtypes, identifying, and releasing signaling molecules, controlling gene expression, and monitoring neuronal circuits in real-time in vivo Combined, these approaches allow us to establish direct causal links from genes and molecules to the systems level and ultimately to cognitive processes.

Diminishing neuronal acidification by channelrhodopsins with low proton conduction

Many channelrhodopsins are permeable to protons. We found that in neurons, activation of a high-current channelrhodopsin, CheRiff, led to significant acidification, with faster acidification in the dendrites than in the soma. Experiments with patterned optogenetic stimulation in monolayers of HEK cells established that the acidification was due to proton transport through the opsin, rather than through other voltage-dependent channels. We identified and characterized two opsins which showed large photocurrents, but small proton permeability, PsCatCh2.0 and ChR2-3M. PsCatCh2.0 showed excellent response kinetics and was also spectrally compatible with simultaneous voltage imaging with QuasAr6a. Stimulation-evoked acidification is a possible source of disruptions to cell health in scientific and prospective therapeutic applications of optogenetics. Channelrhodopsins with low proton permeability are a promising strategy for avoiding these problems.

Statistically unbiased prediction enables accurate denoising of voltage imaging data

Here we report SUPPORT (statistically unbiased prediction utilizing spatiotemporal information in imaging data), a self-supervised learning method for removing Poisson-Gaussian noise in voltage imaging data. SUPPORT is based on the insight that a pixel value in voltage imaging data is highly dependent on its spatiotemporal neighboring pixels, even when its temporally adjacent frames alone do not provide useful information for statistical prediction. Such dependency is captured and used by a convolutional neural network with a spatiotemporal blind spot to accurately denoise voltage imaging data in which the existence of the action potential in a time frame cannot be inferred by the information in other frames. Through simulations and experiments, we show that SUPPORT enables precise denoising of voltage imaging data and other types of microscopy image while preserving the underlying dynamics within the scene.

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. Reliable extraction of neural activity from fluorescence imaging frames at speeds compatible with indicator dynamics and imaging modalities poses a challenge. We therefore developed FIOLA, a framework for fluorescence imaging online analysis that extracts neuronal activity from calcium and voltage imaging movies at speeds one order of magnitude faster than state-of-the-art methods. FIOLA exploits algorithms optimized for parallel processing on GPUs and CPUs. We demonstrate reliable and scalable performance of FIOLA on both simulated and real calcium and voltage imaging datasets. Finally, we present an online experimental scenario to provide guidance in setting FIOLA parameters and to highlight the trade-offs of our approach.

High-speed multiplane confocal microscopy for voltage imaging in densely labeled neuronal populations

Genetically encoded voltage indicators (GEVIs) hold immense potential for monitoring neuronal population activity. To date, best-in-class GEVIs rely on one-photon excitation. However, GEVI imaging of dense neuronal populations remains difficult because out-of-focus background fluorescence produces low contrast and excess noise when paired with conventional one-photon widefield imaging methods. To address this challenge, we developed an imaging system capable of efficient, high-contrast GEVI imaging at near-kHz rates and demonstrate it for in vivo and ex vivo imaging applications in the mouse neocortex. Our approach uses simultaneous multiplane imaging to monitor activity within contiguous tissue volumes with no penalty in speed or requirement for high excitation power. This approach, multi-Z imaging with confocal detection (MuZIC), permits high signal-to-noise ratio voltage imaging in densely labeled neuronal populations and is compatible with imaging through micro-optics. Moreover, it minimizes artifacts associated with concurrent imaging and optogenetic photostimulation for all-optical electrophysiology.

Theta and gamma rhythmic coding through two spike output modes in the hippocampus during spatial navigation

Hippocampal CA1 neurons generate single spikes and stereotyped bursts of spikes. However, it is unclear how individual neurons dynamically switch between these output modes and whether these two spiking outputs relay distinct information. We performed extracellular recordings in spatially navigating rats and cellular voltage imaging and optogenetics in awake mice. We found that spike bursts are preferentially linked to cellular and network theta rhythms (3-12 Hz) and encode an animal's position via theta phase precession, particularly as animals are entering a place field. In contrast, single spikes exhibit additional coupling to gamma rhythms (30-100 Hz), particularly as animals leave a place field. Biophysical modeling suggests that intracellular properties alone are sufficient to explain the observed input frequency-dependent spike coding. Thus, hippocampal neurons regulate the generation of bursts and single spikes according to frequency-specific network and intracellular dynamics, suggesting that these spiking modes perform distinct computations to support spatial behavior.

Voltage Imaging with Engineered Proton-Pumping Rhodopsins: Insights from the Proton Transfer Pathway

Voltage imaging using genetically encoded voltage indicators (GEVIs) has taken the field of neuroscience by storm in the past decade. Its ability to create subcellular and network level readouts of electrical dynamics depends critically on the kinetics of the response to voltage of the indicator used. Engineered microbial rhodopsins form a GEVI subclass known for their high voltage sensitivity and fast response kinetics. Here we review the essential aspects of microbial rhodopsin photocycles that are critical to understanding the mechanisms of voltage sensitivity in these proteins and link them to insights from efforts to create faster, brighter and more sensitive microbial rhodopsin-based GEVIs.

High-speed low-light in vivo two-photon voltage imaging of large neuronal populations

Monitoring spiking activity across large neuronal populations at behaviorally relevant timescales is critical for understanding neural circuit function. Unlike calcium imaging, voltage imaging requires kilohertz sampling rates that reduce fluorescence detection to near shot-noise levels. High-photon flux excitation can overcome photon-limited shot noise, but photobleaching and photodamage restrict the number and duration of simultaneously imaged neurons. We investigated an alternative approach aimed at low two-photon flux, which is voltage imaging below the shot-noise limit. This framework involved developing positive-going voltage indicators with improved spike detection (SpikeyGi and SpikeyGi2); a two-photon microscope ('SMURF') for kilohertz frame rate imaging across a 0.4 mm × 0.4 mm field of view; and a self-supervised denoising algorithm (DeepVID) for inferring fluorescence from shot-noise-limited signals. Through these combined advances, we achieved simultaneous high-speed deep-tissue imaging of more than 100 densely labeled neurons over 1 hour in awake behaving mice. This demonstrates a scalable approach for voltage imaging across increasing neuronal populations.

Video-based pooled screening yields improved far-red genetically encoded voltage indicators

Video-based screening of pooled libraries is a powerful approach for directed evolution of biosensors because it enables selection along multiple dimensions simultaneously from large libraries. Here we develop a screening platform, Photopick, which achieves precise phenotype-activated photoselection over a large field of view (2.3 × 2.3 mm, containing >103 cells, per shot). We used the Photopick platform to evolve archaerhodopsin-derived genetically encoded voltage indicators (GEVIs) with improved signal-to-noise ratio (QuasAr6a) and kinetics (QuasAr6b). These GEVIs gave improved signals in cultured neurons and in live mouse brains. By combining targeted in vivo optogenetic stimulation with high-precision voltage imaging, we characterized inhibitory synaptic coupling between individual cortical NDNF (neuron-derived neurotrophic factor) interneurons, and excitatory electrical synapses between individual hippocampal parvalbumin neurons. The QuasAr6 GEVIs are powerful tools for all-optical electrophysiology and the Photopick approach could be adapted to evolve a broad range of biosensors.

Wide-field fluorescence lifetime imaging of neuron spiking and subthreshold activity in vivo

The development of voltage-sensitive fluorescent probes suggests fluorescence lifetime as a promising readout for electrical activity in biological systems. Existing approaches fail to achieve the speed and sensitivity required for voltage imaging in neuroscience applications. We demonstrated that wide-field electro-optic fluorescence lifetime imaging microscopy (EO-FLIM) allows lifetime imaging at kilohertz frame-acquisition rates, spatially resolving action potential propagation and subthreshold neural activity in live adult Drosophila. Lifetime resolutions of <5 picoseconds at 1 kilohertz were achieved for single-cell voltage recordings. Lifetime readout is limited by photon shot noise, and the method provides strong rejection of motion artifacts and technical noise sources. Recordings revealed local transmembrane depolarizations, two types of spikes with distinct fluorescence lifetimes, and phase locking of spikes to an external mechanical stimulus.

Current Practice in Using Voltage Imaging to Record Fast Neuronal Activity: Successful Examples from Invertebrate to Mammalian Studies

The optical imaging of neuronal activity with potentiometric probes has been credited with being able to address key questions in neuroscience via the simultaneous recording of many neurons. This technique, which was pioneered 50 years ago, has allowed researchers to study the dynamics of neural activity, from tiny subthreshold synaptic events in the axon and dendrites at the subcellular level to the fluctuation of field potentials and how they spread across large areas of the brain. Initially, synthetic voltage-sensitive dyes (VSDs) were applied directly to brain tissue via staining, but recent advances in transgenic methods now allow the expression of genetically encoded voltage indicators (GEVIs), specifically in selected neuron types. However, voltage imaging is technically difficult and limited by several methodological constraints that determine its applicability in a given type of experiment. The prevalence of this method is far from being comparable to patch clamp voltage recording or similar routine methods in neuroscience research. There are more than twice as many studies on VSDs as there are on GEVIs. As can be seen from the majority of the papers, most of them are either methodological ones or reviews. However, potentiometric imaging is able to address key questions in neuroscience by recording most or many neurons simultaneously, thus providing unique information that cannot be obtained via other methods. Different types of optical voltage indicators have their advantages and limitations, which we focus on in detail. Here, we summarize the experience of the scientific community in the application of voltage imaging and try to evaluate the contribution of this method to neuroscience research.

Identifying properties of pattern completion neurons in a computational model of the visual cortex

Neural ensembles are found throughout the brain and are believed to underlie diverse cognitive functions including memory and perception. Methods to activate ensembles precisely, reliably, and quickly are needed to further study the ensembles' role in cognitive processes. Previous work has found that ensembles in layer 2/3 of the visual cortex (V1) exhibited pattern completion properties: ensembles containing tens of neurons were activated by stimulation of just two neurons. However, methods that identify pattern completion neurons are underdeveloped. In this study, we optimized the selection of pattern completion neurons in simulated ensembles. We developed a computational model that replicated the connectivity patterns and electrophysiological properties of layer 2/3 of mouse V1. We identified ensembles of excitatory model neurons using K-means clustering. We then stimulated pairs of neurons in identified ensembles while tracking the activity of the entire ensemble. Our analysis of ensemble activity quantified a neuron pair's power to activate an ensemble using a novel metric called pattern completion capability (PCC) based on the mean pre-stimulation voltage across the ensemble. We found that PCC was directly correlated with multiple graph theory parameters, such as degree and closeness centrality. To improve selection of pattern completion neurons in vivo, we computed a novel latency metric that was correlated with PCC and could potentially be estimated from modern physiological recordings. Lastly, we found that stimulation of five neurons could reliably activate ensembles. These findings can help researchers identify pattern completion neurons to stimulate in vivo during behavioral studies to control ensemble activation.

DIRECT, a low-cost system for high-speed, low-noise imaging of fluorescent bio-samples

A targeted imaging system has been developed for applications requiring recording from stationary samples at high spatiotemporal resolutions. It works by illuminating regions of interest in rapid sequence, and recording the signal from the whole field of view onto a single photodetector. It can be implemented at low cost on an existing microscope without compromising existing functionality. The system is characterized in terms of speed, spatial resolution, and tissue penetration depth, before being used to record individual action potentials from ASAP-3 expressing neurons in an ex vivo mouse brain slice preparation.

Stimulation-mediated reverse engineering of silent neural networks

Reconstructing connectivity of neuronal networks from single-cell activity is essential to understanding brain function, but the challenge of deciphering connections from populations of silent neurons has been largely unmet. We demonstrate a protocol for deriving connectivity of simulated silent neuronal networks using stimulation combined with a supervised learning algorithm, which enables inferring connection weights with high fidelity and predicting spike trains at the single-spike and single-cell levels with high accuracy. We apply our method on rat cortical recordings fed through a circuit of heterogeneously connected leaky integrate-and-fire neurons firing at typical lognormal distributions and demonstrate improved performance during stimulation for multiple subpopulations. These testable predictions about the number and protocol of the required stimulations are expected to enhance future efforts for deriving neuronal connectivity and drive new experiments to better understand brain function.NEW & NOTEWORTHY We introduce a new concept for reverse engineering silent neuronal networks using a supervised learning algorithm combined with stimulation. We quantify the performance of the algorithm and the precision of deriving synaptic weights in inhibitory and excitatory subpopulations. We then show that stimulation enables deciphering connectivity of heterogeneous circuits fed with real electrode array recordings, which could extend in the future to deciphering connectivity in broad biological and artificial neural networks.

Sensitivity optimization of a rhodopsin-based fluorescent voltage indicator

The ability to optically image cellular transmembrane voltages at millisecond-timescale resolutions can offer unprecedented insight into the function of living brains in behaving animals. Here, we present a point mutation that increases the sensitivity of Ace2 opsin-based voltage indicators. We use the mutation to develop Voltron2, an improved chemigeneic voltage indicator that has a 65% higher sensitivity to single APs and 3-fold higher sensitivity to subthreshold potentials than Voltron. Voltron2 retained the sub-millisecond kinetics and photostability of its predecessor, although with lower baseline fluorescence. In multiple in vitro and in vivo comparisons with its predecessor across multiple species, we found Voltron2 to be more sensitive to APs and subthreshold fluctuations. Finally, we used Voltron2 to study and evaluate the possible mechanisms of interneuron synchronization in the mouse hippocampus. Overall, we have discovered a generalizable mutation that significantly increases the sensitivity of Ace2 rhodopsin-based sensors, improving their voltage reporting capability.

Expanding the family of genetically encoded voltage indicators with a candidate Heliorhodopsin exhibiting near-infrared fluorescence

Genetically encoded voltage indicators (GEVIs), particularly those based on microbial rhodopsins, are gaining traction in neuroscience as fluorescent sensors for imaging voltage dynamics with high-spatiotemporal precision. Here we establish a novel GEVI candidate based on the recently discovered subfamily of the microbial rhodopsin clade, termed heliorhodopsins. We discovered that upon excitation at 530-560nm, wild type heliorhodopsin exhibits near infra-red fluorescence which is sensitive to membrane voltage. We characterized the fluorescence brightness, photostability, voltage sensitivity and kinetics of wild type heliorhodopsin in HEK293T cells and further examined the impact of mutating key residues near the retinal chromophore. The S237A mutation significantly improved the fluorescence response of heliorhodopsin by 76% providing a highly promising starting point for further protein evolution.

Bioelectricity in Developmental Patterning and Size Control: Evidence and Genetically Encoded Tools in the Zebrafish Model

Developmental patterning is essential for regulating cellular events such as axial patterning, segmentation, tissue formation, and organ size determination during embryogenesis. Understanding the patterning mechanisms remains a central challenge and fundamental interest in developmental biology. Ion-channel-regulated bioelectric signals have emerged as a player of the patterning mechanism, which may interact with morphogens. Evidence from multiple model organisms reveals the roles of bioelectricity in embryonic development, regeneration, and cancers. The Zebrafish model is the second most used vertebrate model, next to the mouse model. The zebrafish model has great potential for elucidating the functions of bioelectricity due to many advantages such as external development, transparent early embryogenesis, and tractable genetics. Here, we review genetic evidence from zebrafish mutants with fin-size and pigment changes related to ion channels and bioelectricity. In addition, we review the cell membrane voltage reporting and chemogenetic tools that have already been used or have great potential to be implemented in zebrafish models. Finally, new perspectives and opportunities for bioelectricity research with zebrafish are discussed.

All-optical closed-loop voltage clamp for precise control of muscles and neurons in live animals

Excitable cells can be stimulated or inhibited by optogenetics. Since optogenetic actuation regimes are often static, neurons and circuits can quickly adapt, allowing perturbation, but not true control. Hence, we established an optogenetic voltage-clamp (OVC). The voltage-indicator QuasAr2 provides information for fast, closed-loop optical feedback to the bidirectional optogenetic actuator BiPOLES. Voltage-dependent fluorescence is held within tight margins, thus clamping the cell to distinct potentials. We established the OVC in muscles and neurons of Caenorhabditis elegans, and transferred it to rat hippocampal neurons in slice culture. Fluorescence signals were calibrated to electrically measured potentials, and wavelengths to currents, enabling to determine optical I/V-relationships. The OVC reports on homeostatically altered cellular physiology in mutants and on Ca²⁺-channel properties, and can dynamically clamp spiking in C. elegans. Combining non-invasive imaging with control capabilities of electrophysiology, the OVC facilitates high-throughput, contact-less electrophysiology in individual cells and paves the way for true optogenetic control in behaving animals.

Efficient inference of synaptic plasticity rule with Gaussian process regression

Finding the form of synaptic plasticity is critical to understanding its functions underlying learning and memory. We investigated an efficient method to infer synaptic plasticity rules in various experimental settings. We considered biologically plausible models fitting a wide range of in-vitro studies and examined the recovery of their firing-rate dependence from sparse and noisy data. Among the methods assuming low-rankness or smoothness of plasticity rules, Gaussian process regression (GPR), a nonparametric Bayesian approach, performs the best. Under the conditions measuring changes in synaptic weights directly or measuring changes in neural activities as indirect observables of synaptic plasticity, which leads to different inference problems, GPR performs well. Also, GPR could simultaneously recover multiple plasticity rules and robustly perform under various plasticity rules and noise levels. Such flexibility and efficiency, particularly at the low sampling regime, make GPR suitable for recent experimental developments and inferring a broader class of plasticity models.

Detection of Membrane Potential-Dependent Rhodopsin Fluorescence Using Low-Intensity Light Emitting Diode for Long-Term Imaging

Microbial rhodopsin is a family of photoreceptive membrane proteins that commonly consist of a seven-transmembrane domain and a derivative of vitamin-A, retinal, as a chromophore. In 2011, archaeorhodopsin-3 (AR3) was shown to exhibit voltage-dependent fluorescence changes in mammalian cells. Since then, AR3 and its variants have been used as genetically encoded voltage indicators, in which mostly intense laser stimulation (1-1000 W/cm2) is used for the detection of dim fluorescence of rhodopsin, leading to high spatiotemporal resolution. However, intense laser stimulation potentially causes serious cell damage, particularly during long-term imaging over minutes. In this study, we present the successful detection of voltage-sensitive fluorescence of AR3 and its high fluorescence mutant Archon1 in a variety of mammalian cell lines using low-intensity light emitting diode stimulation (0.15 W/cm2) with long exposure time (500 ms). The detection system enables real-time imaging of drug-induced slow changes in voltage within the cells for minutes harmlessly and without fluorescence bleaching. Therefore, we demonstrate a method to quantitatively understand the dynamics of slow changes in membrane voltage on long time scales.

All-optical physiology resolves a synaptic basis for behavioral timescale plasticity

Learning has been associated with modifications of synaptic and circuit properties, but the precise changes storing information in mammals have remained largely unclear. We combined genetically targeted voltage imaging with targeted optogenetic activation and silencing of pre- and post-synaptic neurons to study the mechanisms underlying hippocampal behavioral timescale plasticity. In mice navigating a virtual-reality environment, targeted optogenetic activation of individual CA1 cells at specific places induced stable representations of these places in the targeted cells. Optical elicitation, recording, and modulation of synaptic transmission in behaving mice revealed that activity in presynaptic CA2/3 cells was required for the induction of plasticity in CA1 and, furthermore, that during induction of these place fields in single CA1 cells, synaptic input from CA2/3 onto these same cells was potentiated. These results reveal synaptic implementation of hippocampal behavioral timescale plasticity and define a methodology to resolve synaptic plasticity during learning and memory in behaving mammals.

Reconstruction of sparse recurrent connectivity and inputs from the nonlinear dynamics of neuronal networks

Reconstructing the recurrent structural connectivity of neuronal networks is a challenge crucial to address in characterizing neuronal computations. While directly measuring the detailed connectivity structure is generally prohibitive for large networks, we develop a novel framework for reverse-engineering large-scale recurrent network connectivity matrices from neuronal dynamics by utilizing the widespread sparsity of neuronal connections. We derive a linear input-output mapping that underlies the irregular dynamics of a model network composed of both excitatory and inhibitory integrate-and-fire neurons with pulse coupling, thereby relating network inputs to evoked neuronal activity. Using this embedded mapping and experimentally feasible measurements of the firing rate as well as voltage dynamics in response to a relatively small ensemble of random input stimuli, we efficiently reconstruct the recurrent network connectivity via compressive sensing techniques. Through analogous analysis, we then recover high dimensional natural stimuli from evoked neuronal network dynamics over a short time horizon. This work provides a generalizable methodology for rapidly recovering sparse neuronal network data and underlines the natural role of sparsity in facilitating the efficient encoding of network data in neuronal dynamics.

Genetically encoded fluorescent sensors for imaging neuronal dynamics in vivo

The brain relies on many forms of dynamic activities in individual neurons, from synaptic transmission to electrical activity and intracellular signaling events. Monitoring these neuronal activities with high spatiotemporal resolution in the context of animal behavior is a necessary step to achieve a mechanistic understanding of brain function. With the rapid development and dissemination of highly optimized genetically encoded fluorescent sensors, a growing number of brain activities can now be visualized in vivo. To date, cellular calcium imaging, which has been largely used as a proxy for electrical activity, has become a mainstay in systems neuroscience. While challenges remain, voltage imaging of neural populations is now possible. In addition, it is becoming increasingly practical to image over half a dozen neurotransmitters, as well as certain intracellular signaling and metabolic activities. These new capabilities enable neuroscientists to test previously unattainable hypotheses and questions. This review summarizes recent progress in the development and delivery of genetically encoded fluorescent sensors, and highlights example applications in the context of in vivo imaging.