Imprinting and recalling cortical ensembles

Creation of Neuronal Ensembles and Cell-Specific Homeostatic Plasticity through Chronic Sparse Optogenetic Stimulation

Cortical computations emerge from the dynamics of neurons embedded in complex cortical circuits. Within these circuits, neuronal ensembles, which represent subnetworks with shared functional connectivity, emerge in an experience-dependent manner. Here we induced ensembles in ex vivo cortical circuits from mice of either sex by differentially activating subpopulations through chronic optogenetic stimulation. We observed a decrease in voltage correlation, and importantly a synaptic decoupling between the stimulated and nonstimulated populations. We also observed a decrease in firing rate during Up-states in the stimulated population. These ensemble-specific changes were accompanied by decreases in intrinsic excitability in the stimulated population, and a decrease in connectivity between stimulated and nonstimulated pyramidal neurons. By incorporating the empirically observed changes in intrinsic excitability and connectivity into a spiking neural network model, we were able to demonstrate that changes in both intrinsic excitability and connectivity accounted for the decreased firing rate, but only changes in connectivity accounted for the observed decorrelation. Our findings help ascertain the mechanisms underlying the ability of chronic patterned stimulation to create ensembles within cortical circuits and, importantly, show that while Up-states are a global network-wide phenomenon, functionally distinct ensembles can preserve their identity during Up-states through differential firing rates and correlations.SIGNIFICANCE STATEMENT The connectivity and activity patterns of local cortical circuits are shaped by experience. This experience-dependent reorganization of cortical circuits is driven by complex interactions between different local learning rules, external input, and reciprocal feedback between many distinct brain areas. Here we used an ex vivo approach to demonstrate how simple forms of chronic external stimulation can shape local cortical circuits in terms of their correlated activity and functional connectivity. The absence of feedback between different brain areas and full control of external input allowed for a tractable system to study the underlying mechanisms and development of a computational model. Results show that differential stimulation of subpopulations of neurons significantly reshapes cortical circuits and forms subnetworks referred to as neuronal ensembles.

Controlling neuronal assemblies: a fundamental function of respiration-related brain oscillations in neuronal networks

Respiration exerts profound influence on cognition, which is presumed to rely on the generation of local respiration-coherent brain oscillations and the entrainment of cortical neurons. Here, we propose an addition to that view by emphasizing the role of respiration in pacing cortical assemblies (i.e., groups of synchronized, coactive neurons). We review recent findings of how respiration directly entrains identified assembly patterns and discuss how respiration-dependent pacing of assembly activations might be beneficial for cognitive functions.

Unraveling the mysteries of dendritic spine dynamics: Five key principles shaping memory and cognition

Recent research extends our understanding of brain processes beyond just action potentials and chemical transmissions within neural circuits, emphasizing the mechanical forces generated by excitatory synapses on dendritic spines to modulate presynaptic function. From in vivo and in vitro studies, we outline five central principles of synaptic mechanics in brain function: P1: Stability - Underpinning the integral relationship between the structure and function of the spine synapses. P2: Extrinsic dynamics - Highlighting synapse-selective structural plasticity which plays a crucial role in Hebbian associative learning, distinct from pathway-selective long-term potentiation (LTP) and depression (LTD). P3: Neuromodulation - Analyzing the role of G-protein-coupled receptors, particularly dopamine receptors, in time-sensitive modulation of associative learning frameworks such as Pavlovian classical conditioning and Thorndike's reinforcement learning (RL). P4: Instability - Addressing the intrinsic dynamics crucial to memory management during continual learning, spotlighting their role in "spine dysgenesis" associated with mental disorders. P5: Mechanics - Exploring how synaptic mechanics influence both sides of synapses to establish structural traces of short- and long-term memory, thereby aiding the integration of mental functions. We also delve into the historical background and foresee impending challenges.

Psychosis spectrum illnesses as disorders of prefrontal critical period plasticity

Emerging research on neuroplasticity processes in psychosis spectrum illnesses-from the synaptic to the macrocircuit levels-fill key gaps in our models of pathophysiology and open up important treatment considerations. In this selective narrative review, we focus on three themes, emphasizing alterations in spike-timing dependent and Hebbian plasticity that occur during adolescence, the critical period for prefrontal system development: (1) Experience-dependent dysplasticity in psychosis emerges from activity decorrelation within neuronal ensembles. (2) Plasticity processes operate bidirectionally: deleterious environmental and experiential inputs shape microcircuits. (3) Dysregulated plasticity processes interact across levels of scale and time and include compensatory mechanisms that have pathogenic importance. We present evidence that-given the centrality of progressive dysplastic changes, especially in prefrontal cortex-pharmacologic or neuromodulatory interventions will need to be supplemented by corrective learning experiences for the brain if we are to help people living with these illnesses to fully thrive.

Visual cortex encodes timing information in humans and mice

Despite the importance of timing in our daily lives, our understanding of how the human brain mediates second-scale time perception is limited. Here, we combined intracranial stereoelectroencephalography (SEEG) recordings in epileptic patients and circuit dissection in mice to show that visual cortex (VC) encodes timing information. We first asked human participants to perform an interval-timing task and found VC to be a key timing brain area. We then conducted optogenetic experiments in mice and showed that VC plays an important role in the interval-timing behavior. We further found that VC neurons fired in a time-keeping sequential manner and exhibited increased excitability in a timed manner. Finally, we used a computational model to illustrate a self-correcting learning process that generates interval-timed activities with scalar-timing property. Our work reveals how localized oscillations in VC occurring in the seconds to deca-seconds range relate timing information from the external world to guide behavior.

A nociceptive neuronal ensemble in the dorsomedial prefrontal cortex underlies pain chronicity

Pain chronicity involves unpleasant experience in both somatosensory and affective aspects, accompanied with the prefrontal cortex (PFC) neuroplastic alterations. However, whether specific PFC neuronal ensembles underlie pain chronicity remains elusive. Here we identify a nociceptive neuronal ensemble in the dorsomedial prefrontal cortex (dmPFC), which shows prominent reactivity to nociceptive stimuli. We observed that this ensemble shows distinct molecular characteristics and is densely connected to pain-related regions including basolateral amygdala (BLA) and lateral parabrachial nuclei (LPB). Prolonged chemogenetic activation of this nociceptive neuronal ensemble, but not a randomly transfected subset of dmPFC neurons, induces chronic pain-like behaviors in normal mice. By contrast, silencing the nociceptive dmPFC neurons relieves both pain hypersensitivity and anxiety in mice with chronic inflammatory pain. These results suggest the presence of specific dmPFC neuronal ensembles in processing nociceptive information and regulating pain chronicity.

Dimensionality reduction and recurrence analysis reveal hidden structures of striatal pathological states

A pipeline is proposed here to describe different features to study brain microcircuits on a histological scale using multi-scale analyses, including the uniform manifold approximation and projection (UMAP) dimensional reduction technique and modularity algorithm to identify neuronal ensembles, Runs tests to show significant ensembles activation, graph theory to show trajectories between ensembles, and recurrence analyses to describe how regular or chaotic ensembles dynamics are. The data set includes ex-vivo NMDA-activated striatal tissue in control conditions as well as experimental models of disease states: decorticated, dopamine depleted, and L-DOPA-induced dyskinetic rodent samples. The goal was to separate neuronal ensembles that have correlated activity patterns. The pipeline allows for the demonstration of differences between disease states in a brain slice. First, the ensembles were projected in distinctive locations in the UMAP space. Second, graphs revealed functional connectivity between neurons comprising neuronal ensembles. Third, the Runs test detected significant peaks of coactivity within neuronal ensembles. Fourth, significant peaks of coactivity were used to show activity transitions between ensembles, revealing recurrent temporal sequences between them. Fifth, recurrence analysis shows how deterministic, chaotic, or recurrent these circuits are. We found that all revealed circuits had recurrent activity except for the decorticated circuits, which tended to be divergent and chaotic. The Parkinsonian circuits exhibit fewer transitions, becoming rigid and deterministic, exhibiting a predominant temporal sequence that disrupts transitions found in the controls, thus resembling the clinical signs of rigidity and paucity of movements. Dyskinetic circuits display a higher recurrence rate between neuronal ensembles transitions, paralleling clinical findings: enhancement in involuntary movements. These findings confirm that looking at neuronal circuits at the histological scale, recording dozens of neurons simultaneously, can show clear differences between control and diseased striatal states: "fingerprints" of the disease states. Therefore, the present analysis is coherent with previous ones of striatal disease states, showing that data obtained from the tissue are robust. At the same time, it adds heuristic ways to interpret circuitry activity in different states.

Wide-Field Calcium Imaging of Neuronal Network Dynamics In Vivo

A central tenet of neuroscience is that sensory, motor, and cognitive behaviors are generated by the communications and interactions among neurons, distributed within and across anatomically and functionally distinct brain regions. Therefore, to decipher how the brain plans, learns, and executes behaviors requires characterizing neuronal activity at multiple spatial and temporal scales. This includes simultaneously recording neuronal dynamics at the mesoscale level to understand the interactions among brain regions during different behavioral and brain states. Wide-field Ca2+ imaging, which uses single photon excitation and improved genetically encoded Ca2+ indicators, allows for simultaneous recordings of large brain areas and is proving to be a powerful tool to study neuronal activity at the mesoscopic scale in behaving animals. This review details the techniques used for wide-field Ca2+ imaging and the various approaches employed for the analyses of the rich neuronal-behavioral data sets obtained. Also discussed is how wide-field Ca2+ imaging is providing novel insights into both normal and altered neural processing in disease. Finally, we examine the limitations of the approach and new developments in wide-field Ca2+ imaging that are bringing new capabilities to this important technique for investigating large-scale neuronal dynamics.

From synapses to circuits and back: Bridging levels of understanding in animal models of Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by behavioural changes that include memory loss and cognitive decline and is associated with the appearance of amyloid-β plaques and neurofibrillary tangles throughout the brain. Although aspects of the disease percolate across multiple levels of neuronal organization, from the cellular to the behavioural, it is increasingly clear that circuits are a critical junction between the cellular pathology and the behavioural phenotypes that bookend these levels of analyses. In this review, we discuss critical aspects of neural circuit research, beginning with synapses and progressing to network activity and how they influence our understanding of disease processed in AD.

Conceptual framework for neuronal ensemble identification and manipulation related to behavior using calcium imaging

Significance: The identification and manipulation of spatially identified neuronal ensembles with optical methods have been recently used to prove the causal link between neuronal ensemble activity and learned behaviors. However, the standardization of a conceptual framework to identify and manipulate neuronal ensembles from calcium imaging recordings is still lacking. Aim: We propose a conceptual framework for the identification and manipulation of neuronal ensembles using simultaneous calcium imaging and two-photon optogenetics in behaving mice. Approach: We review the computational approaches that have been used to identify and manipulate neuronal ensembles with single cell resolution during behavior in different brain regions using all-optical methods. Results: We proposed three steps as a conceptual framework that could be applied to calcium imaging recordings to identify and manipulate neuronal ensembles in behaving mice: (1) transformation of calcium transients into binary arrays; (2) identification of neuronal ensembles as similar population vectors; and (3) targeting of neuronal ensemble members that significantly impact behavioral performance. Conclusions: The use of simultaneous two-photon calcium imaging and two-photon optogenetics allowed for the experimental demonstration of the causal relation of population activity and learned behaviors. The standardization of analytical tools to identify and manipulate neuronal ensembles could accelerate interventional experiments aiming to reprogram the brain in normal and pathological conditions.

3D optogenetic control of arteriole diameter in vivo

Modulation of brain arteriole diameter is critical for maintaining cerebral blood pressure and controlling regional hyperemia during neural activity. However, studies of hemodynamic function in health and disease have lacked a method to control arteriole diameter independently with high spatiotemporal resolution. Here, we describe an all-optical approach to manipulate and monitor brain arteriole contractility in mice in three dimensions using combined in vivo two-photon optogenetics and imaging. The expression of the red-shifted excitatory opsin, ReaChR, in vascular smooth muscle cells enabled rapid and repeated vasoconstriction controlled by brief light pulses. Two-photon activation of ReaChR using a spatial light modulator produced highly localized constrictions when targeted to individual arterioles within the neocortex. We demonstrate the utility of this method for examining arteriole contractile dynamics and creating transient focal blood flow reductions. Additionally, we show that optogenetic constriction can be used to reshape vasodilatory responses to sensory stimulation, providing a valuable tool to dissociate blood flow changes from neural activity.

Selective connectivity enhances storage capacity in attractor models of memory function

Autoassociative neural networks provide a simple model of how memories can be stored through Hebbian synaptic plasticity as retrievable patterns of neural activity. Although progress has been made along the last decades in understanding the biological implementation of autoassociative networks, their modest theoretical storage capacity has remained a major constraint. While most previous approaches utilize randomly connected networks, here we explore the possibility of optimizing network performance by selective connectivity between neurons, that could be implemented in the brain through creation and pruning of synaptic connections. We show through numerical simulations that a reconfiguration of the connectivity matrix can improve the storage capacity of autoassociative networks up to one order of magnitude compared to randomly connected networks, either by reducing the noise or by making it reinforce the signal. Our results indicate that the signal-reinforcement scenario is not only the best performing but also the most adequate for brain-like highly diluted connectivity. In this scenario, the optimized network tends to select synapses characterized by a high consensus across stored patterns. We also introduced an online algorithm in which the network modifies its connectivity while learning new patterns. We observed that, similarly to what happens in the human brain, creation of connections dominated in an initial stage, followed by a stage characterized by pruning, leading to an equilibrium state that was independent of the initial connectivity of the network. Our results suggest that selective connectivity could be a key component to make attractor networks in the brain viable in terms of storage capacity.

Formation and computational implications of assemblies in neural circuits

In the brain, patterns of neural activity represent sensory information and store it in non-random synaptic connectivity. A prominent theoretical hypothesis states that assemblies, groups of neurons that are strongly connected to each other, are the key computational units underlying perception and memory formation. Compatible with these hypothesised assemblies, experiments have revealed groups of neurons that display synchronous activity, either spontaneously or upon stimulus presentation, and exhibit behavioural relevance. While it remains unclear how assemblies form in the brain, theoretical work has vastly contributed to the understanding of various interacting mechanisms in this process. Here, we review the recent theoretical literature on assembly formation by categorising the involved mechanisms into four components: synaptic plasticity, symmetry breaking, competition and stability. We highlight different approaches and assumptions behind assembly formation and discuss recent ideas of assemblies as the key computational unit in the brain. Abstract figure legend Assembly Formation. Assemblies are groups of strongly connected neurons formed by the interaction of multiple mechanisms and with vast computational implications. Four interacting components are thought to drive assembly formation: synaptic plasticity, symmetry breaking, competition and stability. This article is protected by copyright. All rights reserved.

Translational neuronal ensembles: Neuronal microcircuits in psychology, physiology, pharmacology and pathology

Multi-recording techniques show evidence that neurons coordinate their firing forming ensembles and that brain networks are made by connections between ensembles. While “canonical” microcircuits are composed of interconnected principal neurons and interneurons, it is not clear how they participate in recorded neuronal ensembles: “groups of neurons that show spatiotemporal co-activation”. Understanding synapses and their plasticity has become complex, making hard to consider all details to fill the gap between cellular-synaptic and circuit levels. Therefore, two assumptions became necessary: First, whatever the nature of the synapses these may be simplified by “functional connections”. Second, whatever the mechanisms to achieve synaptic potentiation or depression, the resultant synaptic weights are relatively stable. Both assumptions have experimental basis cited in this review, and tools to analyze neuronal populations are being developed based on them. Microcircuitry processing followed with multi-recording techniques show temporal sequences of neuronal ensembles resembling computational routines. These sequences can be aligned with the steps of behavioral tasks and behavior can be modified upon their manipulation, supporting the hypothesis that they are memory traces. In vitro, recordings show that these temporal sequences can be contained in isolated tissue of histological scale. Sequences found in control conditions differ from those recorded in pathological tissue obtained from animal disease models and those recorded after the actions of clinically useful drugs to treat disease states, setting the basis for new bioassays to test drugs with potential clinical use. These findings make the neuronal ensembles theoretical framework a dynamic neuroscience paradigm.

SmaRT2P: a software for generating and processing smart line recording trajectories for population two-photon calcium imaging

Two-photon fluorescence calcium imaging allows recording the activity of large neural populations with subcellular spatial resolution, but it is typically characterized by low signal-to-noise ratio (SNR) and poor accuracy in detecting single or few action potentials when large number of neurons are imaged. We recently showed that implementing a smart line scanning approach using trajectories that optimally sample the regions of interest increases both the SNR fluorescence signals and the accuracy of single spike detection in population imaging in vivo. However, smart line scanning requires highly specialised software to design recording trajectories, interface with acquisition hardware, and efficiently process acquired data. Furthermore, smart line scanning needs optimized strategies to cope with movement artefacts and neuropil contamination. Here, we develop and validate SmaRT2P, an open-source, user-friendly and easy-to-interface Matlab-based software environment to perform optimized smart line scanning in two-photon calcium imaging experiments. SmaRT2P is designed to interface with popular acquisition software (e.g., ScanImage) and implements novel strategies to detect motion artefacts, estimate neuropil contamination, and minimize their impact on functional signals extracted from neuronal population imaging. SmaRT2P is structured in a modular way to allow flexibility in the processing pipeline, requiring minimal user intervention in parameter setting. The use of SmaRT2P for smart line scanning has the potential to facilitate the functional investigation of large neuronal populations with increased SNR and accuracy in detecting the discharge of single and few action potentials.

Holographic optogenetic stimulation with calcium imaging as an all optical tool for cardiac electrophysiology

All optical approaches to control and read out the electrical activity in a cardiac syncytium can improve our understanding of cardiac electrophysiology. Here, we demonstrate optogenetic stimulation of cardiomyocytes with high spatial precision using light foci generated with a ferroelectric spatial light modulator. Computer generated holograms binarized by bidirectional error diffusion create multiple foci with more even intensity distribution compared with thresholding approach. We evoke the electrical activity of cardiac HL1 cells expressing the channelrhodopsin-2 variant, ChR2(H134R) using single and multiple light foci and at the same time visualize the action potential using a calcium sensitive indicator called Cal-630. We show that localized regions in the cardiac monolayer can be stimulated enabling us to initiate signal propagation from a precise location. Furthermore, we demonstrate that probing the cardiac cells with multiple light foci enhances the excitability of the cardiac network. This approach opens new applications in manipulating and visualizing the electrical activity in a cardiac syncytium.

All-optical interrogation of neural circuits in behaving mice

Recent advances combining two-photon calcium imaging and two-photon optogenetics with computer-generated holography now allow us to read and write the activity of large populations of neurons in vivo at cellular resolution and with high temporal resolution. Such 'all-optical' techniques enable experimenters to probe the effects of functionally defined neurons on neural circuit function and behavioral output with new levels of precision. This greatly increases flexibility, resolution, targeting specificity and throughput compared with alternative approaches based on electrophysiology and/or one-photon optogenetics and can interrogate larger and more densely labeled populations of neurons than current voltage imaging-based implementations. This protocol describes the experimental workflow for all-optical interrogation experiments in awake, behaving head-fixed mice. We describe modular procedures for the setup and calibration of an all-optical system (~3 h), the preparation of an indicator and opsin-expressing and task-performing animal (~3-6 weeks), the characterization of functional and photostimulation responses (~2 h per field of view) and the design and implementation of an all-optical experiment (achievable within the timescale of a normal behavioral experiment; ~3-5 h per field of view). We discuss optimizations for efficiently selecting and targeting neuronal ensembles for photostimulation sequences, as well as generating photostimulation response maps from the imaging data that can be used to examine the impact of photostimulation on the local circuit. We demonstrate the utility of this strategy in three brain areas by using different experimental setups. This approach can in principle be adapted to any brain area to probe functional connectivity in neural circuits and investigate the relationship between neural circuit activity and behavior.

Existing function in primary visual cortex is not perturbed by new skill acquisition of a non-matched sensory task

Acquisition of new skills has the potential to disturb existing network function. To directly assess whether previously acquired cortical function is altered during learning, mice were trained in an abstract task in which selected activity patterns were rewarded using an optical brain-computer interface device coupled to primary visual cortex (V1) neurons. Excitatory neurons were longitudinally recorded using 2-photon calcium imaging. Despite significant changes in local neural activity during task performance, tuning properties and stimulus encoding assessed outside of the trained context were not perturbed. Similarly, stimulus tuning was stable in neurons that remained responsive following a different, visual discrimination training task. However, visual discrimination training increased the rate of representational drift. Our results indicate that while some forms of perceptual learning may modify the contribution of individual neurons to stimulus encoding, new skill learning is not inherently disruptive to the quality of stimulus representation in adult V1.

Wide-Field Calcium Imaging of Dynamic Cortical Networks during Locomotion

Motor behavior results in complex exchanges of motor and sensory information across cortical regions. Therefore, fully understanding the cerebral cortex's role in motor behavior requires a mesoscopic-level description of the cortical regions engaged, their functional interactions, and how these functional interactions change with behavioral state. Mesoscopic Ca2+ imaging through transparent polymer skulls in mice reveals elevated activation of the dorsal cerebral cortex during locomotion. Using the correlations between the time series of Ca2+ fluorescence from 28 regions (nodes) obtained using spatial independent component analysis (sICA), we examined the changes in functional connectivity of the cortex from rest to locomotion with a goal of understanding the changes to the cortical functional state that facilitate locomotion. Both the transitions from rest to locomotion and from locomotion to rest show marked increases in correlation among most nodes. However, once a steady state of continued locomotion is reached, many nodes, including primary motor and somatosensory nodes, show decreases in correlations, while retrosplenial and the most anterior nodes of the secondary motor cortex show increases. These results highlight the changes in functional connectivity in the cerebral cortex, representing a series of changes in the cortical state from rest to locomotion and on return to rest.

Optogenetic Methods to Investigate Brain Alterations in Preclinical Models

Investigating the neuronal dynamics supporting brain functions and understanding how the alterations in these mechanisms result in pathological conditions represents a fundamental challenge. Preclinical research on model organisms allows for a multiscale and multiparametric analysis in vivo of the neuronal mechanisms and holds the potential for better linking the symptoms of a neurological disorder to the underlying cellular and circuit alterations, eventually leading to the identification of therapeutic/rescue strategies. In recent years, brain research in model organisms has taken advantage, along with other techniques, of the development and continuous refinement of methods that use light and optical approaches to reconstruct the activity of brain circuits at the cellular and system levels, and to probe the impact of the different neuronal components in the observed dynamics. These tools, combining low-invasiveness of optical approaches with the power of genetic engineering, are currently revolutionizing the way, the scale and the perspective of investigating brain diseases. The aim of this review is to describe how brain functions can be investigated with optical approaches currently available and to illustrate how these techniques have been adopted to study pathological alterations of brain physiology.

Impact of optogenetic pulse design on CA3 learning and replay: A neural model

Optogenetic manipulation of hippocampal circuitry is an important tool for investigating learning in vivo. Numerous approaches to pulse design have been employed to elicit desirable circuit and behavioral outcomes. Here, we systematically test the outcome of different single-pulse waveforms in a rate-based model of hippocampal memory function at the level of mnemonic replay extension and de novo synaptic weight formation in CA3 and CA1. Lower-power waveforms with long forward or forward and backward ramps yield more natural sequence replay dynamics and induce synaptic plasticity that allows for more natural memory replay timing, in contrast to square or backward ramps. These differences between waveform shape and amplitude are preserved with the addition of noise in membrane potential, light scattering, and protein expression, improving the potential validity of predictions for in vivo work. These results inform future optogenetic experimental design choices in the field of learning and memory.

Intrinsic excitability mechanisms of neuronal ensemble formation

Neuronal ensembles are coactive groups of cortical neurons, found in spontaneous and evoked activity, that can mediate perception and behavior. To understand the mechanisms that lead to the formation of ensembles, we co-activated layer 2/3 pyramidal neurons in brain slices from mouse visual cortex, in animals of both sexes, replicating in vitro an optogenetic protocol to generate ensembles in vivo. Using whole-cell and perforated patch-clamp pair recordings we found that, after optogenetic or electrical stimulation, coactivated neurons increased their correlated activity, a hallmark of ensemble formation. Coactivated neurons showed small biphasic changes in presynaptic plasticity, with an initial depression followed by a potentiation after a recovery period. Optogenetic and electrical stimulation also induced significant increases in frequency and amplitude of spontaneous EPSPs, even after single-cell stimulation. In addition, we observed unexpected strong and persistent increases in neuronal excitability after stimulation, with increases in membrane resistance and reductions in spike threshold. A pharmacological agent that blocks changes in membrane resistance reverted this effect. These significant increases in excitability can explain the observed biphasic synaptic plasticity. We conclude that cell-intrinsic changes in excitability are involved in the formation of neuronal ensembles. We propose an ‘iceberg’ model, by which increased neuronal excitability makes subthreshold connections suprathreshold, enhancing the effect of already existing synapses, and generating a new neuronal ensemble.

In the brain, groups of neurons that are activated together – also known as neuronal ensembles – are the basic units that underpin perception and behavior. Yet, exactly how these coactive circuits are established remains under investigation.

In 1949, Canadian psychologist Donald Hebb proposed that, when brains learn something new, the neurons which are activated together connect to form ensembles, and their connections become stronger each time this specific piece of knowledge is recalled. This idea that ‘neurons that fire together, wire together’ can explain how memories are acquired and recalled, by strengthening their wiring.

However, recent studies have questioned whether strengthening connections is the only mechanism by which neural ensembles can be created. Changes in the excitability of neurons (how easily they are to fire and become activated) may also play a role. In other words, ensembles could emerge because certain neurons become more excitable and fire more readily.

To solve this conundrum, Alejandre-García et al. examined both hypotheses in the same system. Neurons in slices of the mouse visual cortex were stimulated electrically or optically, via a technique that controls neural activity with light. The activity of individual neurons and their connections was then measured with electrodes.

Spontaneous activity among connected neurons increased after stimulation, indicative of the formation of neuronal ensembles. Connected neurons also showed small changes in the strength of their connections, which first decreased and then rebounded after an initial recovery period.

Intriguingly, cells also showed unexpected strong and persistent increases in neuronal excitability after stimulation, such that neurons fired more readily to the same stimulus. In other words, neurons maintained a cellular memory of having been stimulated. The authors conclude that ensembles form because connected neurons become more excitable, which in turn, may strengthen connections of the circuit at a later stage.

These results provide fresh insights about the neural circuits underpinning learning and memory. In time, the findings could also help to understand disorders such as Alzheimer’s disease and schizophrenia, which are characterised by memory impairments and disordered thinking.

An increase in spontaneous activity mediates visual habituation

The cerebral cortex is spontaneously active, but the function of this ongoing activity remains unclear. To test whether spontaneous activity encodes learned experiences, we measured the response of neuronal populations in mouse primary visual cortex with chronic two-photon calcium imaging during visual habituation to a specific oriented stimulus. We find that, during habituation, spontaneous activity increases in neurons across the full range of orientation selectivity, eventually matching that of evoked levels. This increase in spontaneous activity robustly correlates with the degree of habituation. Moreover, boosting spontaneous activity with two-photon optogenetic stimulation to the levels of visually evoked activity accelerates habituation. Our study shows that cortical spontaneous activity is linked to habituation, and we propose that habituation unfolds by minimizing the difference between spontaneous and stimulus-evoked activity levels. We conclude that baseline spontaneous activity could gate incoming sensory information to the cortex based on the learned experience of the animal.

Microcircuits for spatial coding in the medial entorhinal cortex

The hippocampal formation is critically involved in learning and memory and contains a large proportion of neurons encoding aspects of the organism's spatial surroundings. In the medial entorhinal cortex (MEC), this includes grid cells with their distinctive hexagonal firing fields as well as a host of other functionally defined cell types including head direction cells, speed cells, border cells, and object-vector cells. Such spatial coding emerges from the processing of external inputs by local microcircuits. However, it remains unclear exactly how local microcircuits and their dynamics within the MEC contribute to spatial discharge patterns. In this review we focus on recent investigations of intrinsic MEC connectivity, which have started to describe and quantify both excitatory and inhibitory wiring in the superficial layers of the MEC. Although the picture is far from complete, it appears that these layers contain robust recurrent connectivity that could sustain the attractor dynamics posited to underlie grid pattern formation. These findings pave the way to a deeper understanding of the mechanisms underlying spatial navigation and memory.

Striatal neuronal ensembles reveal differential actions of amantadine and clozapine to ameliorate mice L-DOPA-induced dyskinesia

Amantadine and clozapine have proved to reduce abnormal involuntary movements (AIMs) in preclinical and clinical studies of L-DOPA-Induced Dyskinesias (LID). Even though both drugs decrease AIMs, they may have different action mechanisms by using different receptors and signaling profiles. Here we asked whether there are differences in how they modulate neuronal activity of multiple striatal neurons within the striatal microcircuit at histological level during the dose-peak of L-DOPA in ex-vivo brain slices obtained from dyskinetic mice. To answer this question, we used calcium imaging to record the activity of dozens of neurons of the dorsolateral striatum before and after drugs administration in vitro. We also developed an analysis framework to extract encoding insights from calcium imaging data by quantifying neuronal activity, identifying neuronal ensembles by linking neurons that coactivate using hierarchical cluster analysis and extracting network parameters using Graph Theory. The results show that while both drugs reduce LIDs scores behaviorally in a similar way, they have several different and specific actions on modulating the dyskinetic striatal microcircuit. The extracted features were highly accurate in separating amantadine and clozapine effects by means of principal components analysis (PCA) and support vector machine (SVM) algorithms. These results predict possible synergistic actions of amantadine and clozapine on the dyskinetic striatal microcircuit establishing a framework for a bioassay to test novel antidyskinetic drugs or treatments in vitro.

The neural hierarchy of consciousness

The chief undertaking in the studies of consciousness is that of unravelling "the minimal set of neural processes that are together sufficient for the conscious experience of a particular content - the neural correlates of consciousness". To this day, this crusade remains at an impasse, with a clash of two main theories: consciousness may arise either in a graded and cortically-localised fashion, or in an all-or-none and widespread one. In spite of the long-lasting theoretical debates, neurophysiological theories of consciousness have been mostly dissociated from them. Herein, a theoretical review will be put forth with the aim to change that. In its first half, we will cover the hard available evidence on the neurophysiology of consciousness, whereas in its second half we will weave a series of considerations on both theories and substantiate a novel take on conscious awareness: the levels of processing approach, partitioning the conscious architecture into lower- and higher-order, graded and nonlinear.

Orchestration of Ion Channels and Transporters in Neocortical Development and Neurological Disorders

Electrical activity plays crucial roles in neural circuit formation and remodeling. During neocortical development, neurons are generated in the ventricular zone, migrate to their correct position, elongate dendrites and axons, and form synapses. In this review, we summarize the functions of ion channels and transporters in neocortical development. Next, we discuss links between neurological disorders caused by dysfunction of ion channels (channelopathies) and neocortical development. Finally, we introduce emerging optical techniques with potential applications in physiological studies of neocortical development and the pathophysiology of channelopathies.

Nonlinear transient amplification in recurrent neural networks with short-term plasticity

To rapidly process information, neural circuits have to amplify specific activity patterns transiently. How the brain performs this nonlinear operation remains elusive. Hebbian assemblies are one possibility whereby strong recurrent excitatory connections boost neuronal activity. However, such Hebbian amplification is often associated with dynamical slowing of network dynamics, non-transient attractor states, and pathological run-away activity. Feedback inhibition can alleviate these effects but typically linearizes responses and reduces amplification gain. Here, we study nonlinear transient amplification (NTA), a plausible alternative mechanism that reconciles strong recurrent excitation with rapid amplification while avoiding the above issues. NTA has two distinct temporal phases. Initially, positive feedback excitation selectively amplifies inputs that exceed a critical threshold. Subsequently, short-term plasticity quenches the run-away dynamics into an inhibition-stabilized network state. By characterizing NTA in supralinear network models, we establish that the resulting onset transients are stimulus selective and well-suited for speedy information processing. Further, we find that excitatory-inhibitory co-tuning widens the parameter regime in which NTA is possible in the absence of persistent activity. In summary, NTA provides a parsimonious explanation for how excitatory-inhibitory co-tuning and short-term plasticity collaborate in recurrent networks to achieve transient amplification.

Language and Aphasias

PURPOSE OF REVIEW

This article reveals how it is possible for a brain composed of 100 billion highly interconnected, lipid-encased, reticular electrochemical devices to support complex functions such as language and how language disorders can be understood as a reflection of degradation of one or more domains of knowledge.

RECENT FINDINGS

Ongoing research, building on landmark work regarding parallel distributed processing (PDP), provides the basis for understanding cognitive functions as a manifestation of the activity of populations of millions or billions of neurons in various highly interconnected networks. Population encoding networks have the following intrinsic properties that provide an orderly explanation for normal and degraded language: (1) a capacity for settling into stable "attractor" states; (2) processing occurs in and knowledge (long-term memories) is stored in exactly the same network; (3) a capacity for incorporating statistical regularities of experience, frequency, and age of acquisition; (4) support of content-addressable memory; and (5) graceful degradation, such that lesions increase the probability of errors but do not fundamentally transform network operations. Knowledge in parallel distributed processing networks resides in the strength of connections between units (synapses in the brain). Aphasia, whether stemming from stroke or dementing disorders, can be understood in terms of the degradation of one or more domains of knowledge.

SUMMARY

Understanding the brain as a population encoding machine incorporating vast interconnectivity provides an orderly explanation for language function, both normal and abnormal.

The contribution of low contrast-preferring neurons to information representation in the primary visual cortex after learning

Animals exhibit improved perception of lower-contrast visual objects after training. We explored this neuronal mechanism using multiple single-unit recordings from deep layers of the primary visual cortex (V1) of trained rats during orientation discrimination. We found that the firing rates of a subset of neurons increased by reducing luminance contrast, being at least above basal activities at low contrast. These low contrast–preferring neurons were rare during passive viewing without training or anesthesia after training. They fired more frequently in correct-choice than incorrect-choice trials. At single-neuron and population levels, they efficiently represented low-contrast orientations. Following training, in addition to generally enhanced excitation, the phase synchronization of spikes to beta oscillations at high contrast was stronger in putative inhibitory than excitatory neurons. The change in excitation-inhibition balance might contribute to low-contrast preference. Thus, low-contrast preference in V1 activity is strengthened in an experience-dependent manner, which may contribute to low-contrast visual discrimination.

Aberrant Patterns of Sensory-Evoked Activity in the Olfactory Bulb of LRRK2 Knockout Mice

The LRRK2 gene is the major genetic determinant of familiar Parkinson's disease (PD). Leucine-rich repeat kinase 2 (LRRK2) is a multidomain protein involved in several intracellular signaling pathways. A wealth of evidence indicates that LRRK2 is enriched at the presynaptic compartment where it regulates vesicle trafficking and neurotransmitter release. However, whether the role of LRRK2 affects neuronal networks dynamic at systems level remains unknown. Addressing this question is critical to unravel the impact of LRRK2 on brain function. Here, combining behavioral tests, electrophysiological recordings, and functional imaging, we investigated neuronal network dynamics, in vivo, in the olfactory bulb of mice carrying a null mutation in LRRK2 gene (LRRK2 knockout, LRRK2 KO, mice). We found that LRRK2 KO mice exhibit olfactory behavioral deficits. At the circuit level, the lack of LRRK2 expression results in altered gamma rhythms and odorant-evoked activity with significant impairments, while the spontaneous activity exhibited limited alterations. Overall, our data in the olfactory bulb suggest that the multifaced role of LRRK2 has a strong impact at system level when the network is engaged in active sensory processing.

Excitatory-inhibitory balance modulates the formation and dynamics of neuronal assemblies in cortical networks

Repetitive activation of subpopulations of neurons leads to the formation of neuronal assemblies, which can guide learning and behavior. Recent technological advances have made the artificial induction of these assemblies feasible, yet how various parameters of induction can be optimized is not clear. Here, we studied this question in large-scale cortical network models with excitatory-inhibitory balance. We found that the background network in which assemblies are embedded can strongly modulate their dynamics and formation. Networks with dominant excitatory interactions enabled a fast formation of assemblies, but this was accompanied by recruitment of other non-perturbed neurons, leading to some degree of nonspecific induction. On the other hand, networks with strong excitatory-inhibitory interactions ensured that the formation of assemblies remained constrained to the perturbed neurons, but slowed down the induction. Our results suggest that these two regimes can be suitable for computational and cognitive tasks with different trade-offs between speed and specificity.

Identification of Pattern Completion Neurons in Neuronal Ensembles Using Probabilistic Graphical Models

Neuronal ensembles are groups of neurons with coordinated activity that could represent sensory, motor, or cognitive states. The study of how neuronal ensembles are built, recalled, and involved in the guiding of complex behaviors has been limited by the lack of experimental and analytical tools to reliably identify and manipulate neurons that have the ability to activate entire ensembles. Such pattern completion neurons have also been proposed as key elements of artificial and biological neural networks. Indeed, the relevance of pattern completion neurons is highlighted by growing evidence that targeting them can activate neuronal ensembles and trigger behavior. As a method to reliably detect pattern completion neurons, we use conditional random fields (CRFs), a type of probabilistic graphical model. We apply CRFs to identify pattern completion neurons in ensembles in experiments using in vivo two-photon calcium imaging from primary visual cortex of male mice and confirm the CRFs predictions with two-photon optogenetics. To test the broader applicability of CRFs we also analyze publicly available calcium imaging data (Allen Institute Brain Observatory dataset) and demonstrate that CRFs can reliably identify neurons that predict specific features of visual stimuli. Finally, to explore the scalability of CRFs we apply them to in silico network simulations and show that CRFs-identified pattern completion neurons have increased functional connectivity. These results demonstrate the potential of CRFs to characterize and selectively manipulate neural circuits.SIGNIFICANCE STATEMENT We describe a graph theory method to identify and optically manipulate neurons with pattern completion capability in mouse cortical circuits. Using calcium imaging and two-photon optogenetics in vivo we confirm that key neurons identified by this method can recall entire neuronal ensembles. This method could be broadly applied to manipulate neuronal ensemble activity to trigger behavior or for therapeutic applications in brain prostheses.

Probing neural codes with two-photon holographic optogenetics

Optogenetics ushered in a revolution in how neuroscientists interrogate brain function. Because of technical limitations, the majority of optogenetic studies have used low spatial resolution activation schemes that limit the types of perturbations that can be made. However, neural activity manipulations at finer spatial scales are likely to be important to more fully understand neural computation. Spatially precise multiphoton holographic optogenetics promises to address this challenge and opens up many new classes of experiments that were not previously possible. More specifically, by offering the ability to recreate extremely specific neural activity patterns in both space and time in functionally defined ensembles of neurons, multiphoton holographic optogenetics could allow neuroscientists to reveal fundamental aspects of the neural codes for sensation, cognition and behavior that have been beyond reach. This Review summarizes recent advances in multiphoton holographic optogenetics that substantially expand its capabilities, highlights outstanding technical challenges and provides an overview of the classes of experiments it can execute to test and validate key theoretical models of brain function. Multiphoton holographic optogenetics could substantially accelerate the pace of neuroscience discovery by helping to close the loop between experimental and theoretical neuroscience, leading to fundamental new insights into nervous system function and disorder.

Multiscale representations of community structures in attractor neural networks

Our cognition relies on the ability of the brain to segment hierarchically structured events on multiple scales. Recent evidence suggests that the brain performs this event segmentation based on the structure of state-transition graphs behind sequential experiences. However, the underlying circuit mechanisms are poorly understood. In this paper we propose an extended attractor network model for graph-based hierarchical computation which we call the Laplacian associative memory. This model generates multiscale representations for communities (clusters) of associative links between memory items, and the scale is regulated by the heterogenous modulation of inhibitory circuits. We analytically and numerically show that these representations correspond to graph Laplacian eigenvectors, a popular method for graph segmentation and dimensionality reduction. Finally, we demonstrate that our model exhibits chunked sequential activity patterns resembling hippocampal theta sequences. Our model connects graph theory and attractor dynamics to provide a biologically plausible mechanism for abstraction in the brain.

Our experiences are often hierarchically organized, so is our knowledge. Identifying meaningful segments in hierarchically structured information is crucial for many cognitive functions including visual, auditory, motor, memory, language processing, and reasoning. Herein, we show that the attractor dynamics of recurrent neural circuits offer a biologically plausible way for hierarchical segmentation. We found that an extended model of associative memory autonomously performs segmentation by finding groups of tightly linked memories. We proved that the neural dynamics of our model mathematically coincide with optimal graph segmentation in graph theory and are consistent with the experimentally observed nature of human behaviors and neural activities. Our model established a previously unexpected relationship between attractor neural networks and the graph-theoretic processing of knowledge structures. Our model also provides experimentally testable predictions, particularly regarding the role of inhibitory circuits in controlling representational granularity.

Manipulating neuronal circuits, in concert

Two-photon holographic optogenetics enables precise modulation of brain activity

Long-term stability of cortical ensembles

Neuronal ensembles, coactive groups of neurons found in spontaneous and evoked cortical activity, are causally related to memories and perception, but it is still unknown how stable or flexible they are over time. We used two-photon multiplane calcium imaging to track over weeks the activity of the same pyramidal neurons in layer 2/3 of the visual cortex from awake mice and recorded their spontaneous and visually evoked responses. Less than half of the neurons remained active across any two imaging sessions. These stable neurons formed ensembles that lasted weeks, but some ensembles were also transient and appeared only in one single session. Stable ensembles preserved most of their neurons for up to 46 days, our longest imaged period, and these 'core' cells had stronger functional connectivity. Our results demonstrate that neuronal ensembles can last for weeks and could, in principle, serve as a substrate for long-lasting representation of perceptual states or memories.

Precise 3D computer-generated holography based on non-convex optimization with spherical aberration compensation (SAC-NOVO) for two-photon optogenetics

Computer-generated holography (CGH) has been adopted in two-photon optogenetics as a promising technique for selective excitation of neural ensembles. However, 3D CGH by nonconvex optimization, the state of art method, is susceptible to imprecise axial positioning, due to the quadratic phase approximation in 3D target generation. Even though the misplacement of targets in conventional CGH can be solved by pre-calibration, it still suffers from low efficiency and poor axial resolution of two-photon excitation. Here, we propose a novel CGH method based on non-convex optimization with spherical aberration compensation (SAC-NOVO). Through numerical simulations and two-photon excitation experiments, we verify that SAC-NOVO could achieve precise axial positioning for single and multiple expanded disk patterns, while ensuring high two-photon excitation efficiency. Besides, we experimentally show that SAC-NOVO enables the suppression of dark target areas. This work shows the superiority of SAC-NOVO for two-photon optogenetics.

Functional ultrasound imaging of the spreading activity following optogenetic stimulation of the rat visual cortex

Optogenetics has revolutionized neurosciences by allowing fine control of neuronal activity. An important aspect for this control is assessing the activation and/or adjusting the stimulation, which requires imaging the entire volume of optogenetically-induced neuronal activity. An ideal technique for this aim is fUS imaging, which allows one to generate brain-wide activation maps with submesoscopic spatial resolution. However, optical stimulation of the brain with blue light might lead to non-specific activations at high irradiances. fUS imaging of optogenetic activations can be obtained at these wavelengths using lower light power (< 2mW) but it limits the depth of directly activatable neurons from the cortical surface. Our main goal was to report that we can detect specific optogenetic activations in V1 even in deep layers following stimulation at the cortical surface. Here, we show the possibility to detect deep optogenetic activations in anesthetized rats expressing the red-shifted opsin ChrimsonR in V1 using fUS imaging. We demonstrate the optogenetic specificity of these activations and their neuronal origin with electrophysiological recordings. Finally, we show that the optogenetic response initiated in V1 spreads to downstream (LGN) and upstream (V2) visual areas.

The essence of the engram: Cellular or synaptic?

Memory is composed of various phases including cellular consolidation, systems consolidation, reconsolidation, and extinction. In the last few years it has been shown that simple association memories can be encoded by a subset of the neuronal population called engram cells. Activity of these cells is necessary and sufficient for the recall of association memory. However, it is unclear which molecular mechanisms allow cellular engrams to encode the diverse phases of memory. Further research is needed to examine the possibility that it is the synapses between engram cells (the synaptic engram) that constitute the memory. In this review we summarize recent findings on cellular engrams with a focus on different phases of memory, and discuss the distinct molecular mechanism required for cellular and synaptic engrams.

Precompensation of 3D field distortions in remote focus two-photon microscopy

Remote focusing is widely used in 3D two-photon microscopy and 3D photostimulation because it enables fast axial scanning without moving the objective lens or specimen. However, due to the design constraints of microscope optics, remote focus units are often located in non-telecentric positions in the optical path, leading to significant depth-dependent 3D field distortions in the imaging volume. To address this limitation, we characterized 3D field distortions arising from non-telecentric remote focusing and present a method for distortion precompensation. We demonstrate its applicability for a 3D two-photon microscope that uses an acousto-optic lens (AOL) for remote focusing and scanning. We show that the distortion precompensation method improves the pointing precision of the AOL microscope to < 0.5 µm throughout the 400 × 400 × 400 µm imaging volume.

Optogenetic strategies for high-efficiency all-optical interrogation using blue-light-sensitive opsins

All-optical methods for imaging and manipulating brain networks with high spatial resolution are fundamental to study how neuronal ensembles drive behavior. Stimulation of neuronal ensembles using two-photon holographic techniques requires high-sensitivity actuators to avoid photodamage and heating. Moreover, two-photon-excitable opsins should be insensitive to light at wavelengths used for imaging. To achieve this goal, we developed a novel soma-targeted variant of the large-conductance blue-light-sensitive opsin CoChR (stCoChR). In the mouse cortex in vivo, we combined holographic two-photon stimulation of stCoChR with an amplified laser tuned at the opsin absorption peak and two-photon imaging of the red-shifted indicator jRCaMP1a. Compared to previously characterized blue-light-sensitive soma-targeted opsins in vivo, stCoChR allowed neuronal stimulation with more than 10-fold lower average power and no spectral crosstalk. The combination of stCoChR, tuned amplified laser stimulation, and red-shifted functional indicators promises to be a powerful tool for large-scale interrogation of neural networks in the intact brain.

Assessment of optogenetically-driven strategies for prosthetic restoration of cortical vision in large-scale neural simulation of V1

The neural encoding of visual features in primary visual cortex (V1) is well understood, with strong correlates to low-level perception, making V1 a strong candidate for vision restoration through neuroprosthetics. However, the functional relevance of neural dynamics evoked through external stimulation directly imposed at the cortical level is poorly understood. Furthermore, protocols for designing cortical stimulation patterns that would induce a naturalistic perception of the encoded stimuli have not yet been established. Here, we demonstrate a proof of concept by solving these issues through a computational model, combining (1) a large-scale spiking neural network model of cat V1 and (2) a virtual prosthetic system transcoding the visual input into tailored light-stimulation patterns which drive in situ the optogenetically modified cortical tissue. Using such virtual experiments, we design a protocol for translating simple Fourier contrasted stimuli (gratings) into activation patterns of the optogenetic matrix stimulator. We then quantify the relationship between spatial configuration of the imposed light pattern and the induced cortical activity. Our simulations in the absence of visual drive (simulated blindness) show that optogenetic stimulation with a spatial resolution as low as 100 [Formula: see text]m, and light intensity as weak as [Formula: see text] photons/s/cm[Formula: see text] is sufficient to evoke activity patterns in V1 close to those evoked by normal vision.

Engineered Illumination Devices for Optogenetic Control of Cellular Signaling Dynamics

Spatially and temporally varying patterns of morphogen signals during development drive cell fate specification at the proper location and time. However, current in vitro methods typically do not allow for precise, dynamic spatiotemporal control of morphogen signaling and are thus insufficient to readily study how morphogen dynamics affect cell behavior. Here, we show that optogenetic Wnt/β-catenin pathway activation can be controlled at user-defined intensities, temporal sequences, and spatial patterns using engineered illumination devices for optogenetic photostimulation and light activation at variable amplitudes (LAVA). By patterning human embryonic stem cell (hESC) cultures with varying light intensities, LAVA devices enabled dose-responsive control of optoWnt activation and Brachyury expression. Furthermore, time-varying and spatially localized patterns of light revealed tissue patterning that models the embryonic presentation of Wnt signals in vitro. LAVA devices thus provide a low-cost, user-friendly method for high-throughput and spatiotemporal optogenetic control of cell signaling for applications in developmental and cell biology.

Neuronal ensembles in memory processes

A neuronal ensemble represents the concomitant activity of a specific group of neurons that could encompass a broad repertoire of brain functions such as motor, perceptual, memory or cognitive states. On the other hand, a memory engram portrays the physical manifestation of memory or the changes that enable learning and retrieval. Engram studies focused for many years on finding where memories are stored as in, which cells or brain regions represent a memory trace, and disregarded the investigation of how neuronal activity patterns give rise to such memories. Recent experiments suggest that the association and reactivation of specific neuronal groups could be the main mechanism underlying the brain's ability to remember past experiences and envision future actions. Thus, the growing consensus is that the interaction between neuronal ensembles could allow sequential activity patterns to become memories and recurrent memories to compose complex behaviors. The goal of this review is to propose how the neuronal ensemble framework could be translated and useful to understand memory processes.

Neuronal mechanisms underlying opioid-induced respiratory depression: our current understanding

Opioid-induced respiratory depression (OIRD) represents the primary cause of death associated with therapeutic and recreational opioid use. Within the United States, the rate of death from opioid abuse since the early 1990s has grown disproportionally, prompting the classification as a nationwide "epidemic." Since this time, we have begun to unravel many fundamental cellular and systems-level mechanisms associated with opioid-related death. However, factors such as individual vulnerability, neuromodulatory compensation, and redundancy of opioid effects across central and peripheral nervous systems have created a barrier to a concise, integrative view of OIRD. Within this review, we bring together multiple perspectives in the field of OIRD to create an overarching viewpoint of what we know, and where we view this essential topic of research going forward into the future.

Multiplexing viral approaches to the study of the neuronal circuits

Neural circuits are composed of multitudes of elaborately interconnected cell types. Understanding neural circuit function requires not only cell-specific knowledge of connectivity, but the ability to record and manipulate distinct cell types independently. Recent advances in viral vectors promise the requisite specificity to perform true "circuit-breaking" experiments. However, such new avenues of multiplexed, cell-specific investigation raise new technical issues: one must ensure that both the viral vectors and their transgene payloads do not overlap with each other in both an anatomical and a functional sense. This review describes benefits and issues regarding the use of viral vectors to analyse the function of neural circuits and provides a resource for the design and implementation of such multiplexing experiments.