Activity Of Individual Neurons Research Articles

Cell assemblies, sequences and temporal coding in the hippocampus.

Like social networks, neurons in the brain are organized in neuronal ensembles that constrain and at the same time enrich the role and temporal precision of activity of individual neurons. Changes in coordinated firing across cortical neurons as well as selective changes in timing and sequential order across neurons that are important for encoding of novel information have collectively been known as ensemble temporal coding. Here we review recent findings on the role of online and offline temporal coding within sequential cell assemblies from the rodent hippocampus thought be important for memory encoding and consolidation and for spatial navigation. We propose that temporal coding in the rodent hippocampus represented as plasticity in replay activity relies primarily on subtle and selective changes in coordinated firing within the microstructure of individual cell assembly organization during sleep.

In vivo large-scale analysis of Drosophila neuronal calcium traces by automated tracking of single somata

How does the concerted activity of neuronal populations shape behavior? Impediments to address this question are primarily due to critical experimental barriers. An integrated perspective on large scale neural information processing requires an in vivo approach that can combine the advantages of exhaustively observing all neurons dedicated to a given type of stimulus, and simultaneously achieve a resolution that is precise enough to capture individual neuron activity. Current experimental data from in vivo observations are either restricted to a small fraction of the total number of neurons, or are based on larger brain volumes but at a low spatial and temporal resolution. Consequently, fundamental questions as to how sensory information is represented on a population scale remain unanswered. In Drosophila melanogaster, the mushroom body (MB) represents an excellent model to analyze sensory coding and memory plasticity. In this work, we present an experimental setup coupled with a dedicated computational method that provides in vivo measurements of the activity of hundreds of densely packed somata uniformly spread in the MB. We exploit spinning-disk confocal 3D imaging over time of the whole MB cell body layer in vivo while it is exposed to olfactory stimulation. Importantly, to derive individual signal from densely packed somata, we have developed a fully automated image analysis procedure that takes advantage of the specificities of our data. After anisotropy correction, our approach operates a dedicated spot detection and registration over the entire time sequence to transform trajectories to identifiable clusters. This enabled us to discard spurious detections and reconstruct missing ones in a robust way. We demonstrate that this approach outperformed existing methods in this specific context and made possible high-throughput analysis of approximately 500 single somata uniformly spread over the MB in various conditions. Applying this approach, we find that learned experiences change the population code of odor representations in the MB. After long-term memory (LTM) formation, we quantified an increase in responsive somata count and a stable single neuron signal. We predict that this method, which should further enable studying the population pattern of neuronal activity, has the potential to uncover fine details of sensory processing and memory plasticity.

Brain-Wide Mapping of Water Flow Perception in Zebrafish.

Information about water flow, detected by lateral line organs, is critical to the behavior and survival of fish and amphibians. While certain aspects of water flow processing have been revealed through electrophysiology, we lack a comprehensive description of the neurons that respond to water flow and the network that they form. Here, we use brain-wide calcium imaging in combination with microfluidic stimulation to map out, at cellular resolution, neuronal responses involved in perceiving and processing water flow information in larval zebrafish. We find a diverse array of neurons responding to head-to-tail (h-t) flow, tail-to-head (t-h) flow, or both. Early in this pathway, in the lateral line ganglia, neurons respond almost exclusively to the simple presence of h-t or t-h flow, but later processing includes neurons responding specifically to flow onset, representing the accumulated displacement of flow during a stimulus, or encoding the speed of the flow. The neurons reporting on these more nuanced details are located across numerous brain regions, including some not previously implicated in water flow processing. A graph theory-based analysis of the brain-wide water flow network shows that a majority of this processing is dedicated to h-t flow detection, and this is reinforced by our finding that details like flow velocity and the total accumulated flow are only encoded for the h-t direction. The results represent the first brain-wide description of processing for this important modality, and provide a departure point for more detailed studies of the flow of information through this network.SIGNIFICANCE STATEMENT In aquatic animals, the lateral line is important for detecting water flow stimuli, but the brain networks that interpret this information remain mysterious. Here, we have imaged the activity of individual neurons across the entire brains of larval zebrafish, revealing all response types and their brain locations as water flow processing occurs. We find neurons that respond to the simple presence of water flow, and others attuned to the direction, speed, and duration of flow, or the accumulated displacement of water that has passed during the stimulus. With this information, we modeled the underlying network, describing a system that is nuanced in its processing of water flow simulating head-to-tail motion but rudimentary in processing flow in the tail-to-head direction.

Collapse of Global Neuronal States in Caenorhabditis elegans under Isoflurane Anesthesia.

A comprehensive understanding of how anesthetics facilitate a reversible collapse of system-wide neuronal function requires measurement of neuronal activity with single-cell resolution. Multineuron recording was performed in Caenorhabditis elegans to measure neuronal activity at varying depths of anesthesia. The authors hypothesized that anesthesia is characterized by dyssynchrony between neurons resulting in a collapse of organized system states. Using light-sheet microscopy and transgenic expression of the calcium-sensitive fluorophore GCaMP6s, a majority of neurons (n = 120) in the C. elegans head were simultaneously imaged in vivo and neuronal activity was measured. Neural activity and system-wide dynamics were compared in 10 animals, progressively dosed at 0%, 4%, and 8% isoflurane. System-wide neuronal activity was analyzed using principal component analysis. Unanesthetized animals display distinct global neuronal states that are reflected in a high degree of correlation (R = 0.196 ± 0.070) between neurons and low-frequency, large-amplitude neuronal dynamics. At 4% isoflurane, the average correlation between neurons is significantly diminished (R = 0.026 ± 0.010; P < 0.0001 vs. unanesthetized) and neuron dynamics shift toward higher frequencies but with smaller dynamic range. At 8% isoflurane, interneuronal correlations indicate that neuronal activity remains uncoordinated (R = 0.053 ± 0.029; P < 0.0001 vs. unanesthetized) with high-frequency dynamics that are even further restricted. Principal component analysis of unanesthetized neuronal activity reveals distinct structure corresponding to known behavioral states. At 4% and 8% isoflurane this structure is lost and replaced with randomized dynamics, as quantified by the percentage of total ensemble variance captured by the first three principal components. In unanesthetized worms, this captured variance is high (88.9 ± 5.4%), reflecting a highly organized system, falling significantly at 4% and 8% isoflurane (57.9 ± 11.2%, P < 0.0001 vs. unanesthetized, and 76.0 ± 7.9%, P < 0.001 vs. unanesthetized, respectively) and corresponding to increased randomization and collapse of system-wide organization. Anesthesia with isoflurane in C. elegans corresponds to high-frequency randomization of individual neuron activity, loss of coordination between neurons, and a collapse of system-wide functional organization.

Cumulative Effects of Social Stress on Reward-Guided Actions and Prefrontal Cortical Activity

BackgroundWhen exposed to chronic social stress, animals display behavioral changes that are relevant to depressive-like phenotypes. However, the cascading relationship between incremental stress exposure and neural dysfunctions over time remains incompletely understood. MethodsWe characterized the longitudinal effect of social defeat on goal-directed actions and prefrontal cortical activity in mice using a novel head-fixed sucrose preference task and two-photon calcium imaging. ResultsBehaviorally, stress-induced loss of reward sensitivity intensifies over days. Motivational anhedonia, the failure to translate positive reinforcements into future actions, requires multiple sessions of stress exposure to become fully established. For neural activity, individual layer 2/3 pyramidal neurons in the cingulate and medial secondary motor subregions of the medial prefrontal cortex have heterogeneous responses to stress. Changes in ensemble activity differ significantly between susceptible and resilient mice after the first defeat session and continue to diverge following successive stress episodes before reaching persistent abnormal levels. ConclusionsCollectively, these results demonstrate that the cumulative impact of an ethologically relevant stress can be observed at the level of cellular activity of individual prefrontal neurons. The distinct neural responses associated with resilience versus susceptibility suggests the hypothesis that the negative impact of social stress is neutralized in resilient animals, in part through an adaptive reorganization of prefrontal cortical activity.

Opposing Somatic and Dendritic Expression of Stimulus-Selective Response Plasticity in Mouse Primary Visual Cortex.

Daily exposure of awake mice to a phase-reversing visual grating stimulus leads to enhancement of the visual-evoked potential (VEP) in layer 4 of the primary visual cortex (V1). This stimulus-selective response potentiation (SRP) resembles and shares mechanistic requirements with canonical long-term synaptic potentiation (LTP). However, it remains to be determined how this augmentation of a population response translates into altered neuronal activity of individual V1 neurons. To address this question, we performed longitudinal calcium imaging of layer 4 excitatory neurons in V1 and tracked changes associated with the induction and expression of SRP. We found no evidence for a net change in the fraction of visually responsive neurons as the stimulus became familiar. However, endoscopic calcium imaging of layer 4 principal neurons revealed that somatic calcium transients in response to phase-reversals of the familiar visual stimulus are reduced and undergo strong within-session adaptation. Conversely, neuropil calcium responses and VEPs are enhanced during familiar stimulus viewing, and the VEPs show reduced within-session adaptation. Consistent with the exquisite selectivity of SRP, the plasticity of cellular responses to phase-reversing gratings did not translate into altered orientation selectivity to drifting gratings. Our findings suggest a model in which augmentation of fast, short-latency synaptic (dendritic) responses, manifested as enhanced layer 4 VEPs, recruits inhibition to suppress cellular activity. Reduced cellular activity to the familiar stimulus may account for the behavioral correlate of SRP, orientation-selective long-term habituation.

Extent of Single-Neuron Activity Modulation by Hippocampal Interictal Discharges Predicts Declarative Memory Disruption in Humans.

Memory deficits are common in epilepsy patients. In these patients, the interictal EEG commonly shows interictal epileptiform discharges (IEDs). While IEDs are associated with transient cognitive impairments, it remains poorly understood why this is. We investigated the effects of human (male and female) hippocampal IEDs on single-neuron activity during a memory task in patients with medically refractory epilepsy undergoing depth electrode monitoring. We quantified the effects of hippocampal IEDs on single-neuron activity and the impact of this modulation on subjectively declared memory strength. Across all recorded neurons, the activity of 50 of 728 neurons were significantly modulated by IEDs, with the strongest modulation in the medial temporal lobe (33 of 416) and in particular the right hippocampus (12 of 58). Putative inhibitory neurons, as identified by their extracellular signature, were more likely to be modulated by IEDs than putative excitatory neurons (19 of 157 vs 31 of 571). Behaviorally, the occurrence of hippocampal IEDs was accompanied by a disruption of recognition of familiar images only if they occurred up to 2 s before stimulus onset. In contrast, IEDs did not impair encoding or recognition of novel images, indicating high temporal and task specificity of the effects of IEDs. The degree of modulation of individual neurons by an IED correlated with the declared confidence of a retrieval trial, with higher firing rates indicative of reduced confidence. Together, these data link the transient modulation of individual neurons by IEDs to specific declarative memory deficits in specific cell types, thereby revealing a mechanism by which IEDs disrupt medial temporal lobe-dependent declarative memory retrieval processes.SIGNIFICANCE STATEMENT Interictal epileptiform discharges (IEDs) are thought to be a cause of memory deficits in chronic epilepsy patients, but the underlying mechanisms are not understood. Utilizing single-neuron recordings in epilepsy patients, we found that hippocampal IEDs transiently change firing of hippocampal neurons and disrupted selectively the retrieval, but not encoding, of declarative memories. The extent of the modulation of the individual firing of hippocampal neurons by an IED predicted the extent of reduction of subjective retrieval confidence. Together, these data reveal a specific kind of transient cognitive impairment caused by IEDs and link this impairment to the modulation of the activity of individual neurons. Understanding the mechanisms by which IEDs impact memory is critical for understanding memory impairments in epilepsy patients.

Dopamine neuron ensembles signal the content of sensory prediction errors.

Dopamine neurons respond to errors in predicting value-neutral sensory information. These data, combined with causal evidence that dopamine transients support sensory-based associative learning, suggest that the dopamine system signals a multidimensional prediction error. Yet such complexity is not evident in the activity of individual neurons or population averages. How then do downstream areas know what to learn in response to these signals? One possibility is that information about content is contained in the pattern of firing across many dopamine neurons. Consistent with this, here we show that the pattern of firing across a small group of dopamine neurons recorded in rats signals the identity of a mis-predicted sensory event. Further, this same information is reflected in the BOLD response elicited by sensory prediction errors in human midbrain. These data provide evidence that ensembles of dopamine neurons provide highly specific teaching signals, opening new possibilities for how this system might contribute to learning.

Compressive Sensing Inference of Neuronal Network Connectivity in Balanced Neuronal Dynamics.

Determining the structure of a network is of central importance to understanding its function in both neuroscience and applied mathematics. However, recovering the structural connectivity of neuronal networks remains a fundamental challenge both theoretically and experimentally. While neuronal networks operate in certain dynamical regimes, which may influence their connectivity reconstruction, there is widespread experimental evidence of a balanced neuronal operating state in which strong excitatory and inhibitory inputs are dynamically adjusted such that neuronal voltages primarily remain near resting potential. Utilizing the dynamics of model neurons in such a balanced regime in conjunction with the ubiquitous sparse connectivity structure of neuronal networks, we develop a compressive sensing theoretical framework for efficiently reconstructing network connections by measuring individual neuronal activity in response to a relatively small ensemble of random stimuli injected over a short time scale. By tuning the network dynamical regime, we determine that the highest fidelity reconstructions are achievable in the balanced state. We hypothesize the balanced dynamics observed in vivo may therefore be a result of evolutionary selection for optimal information encoding and expect the methodology developed to be generalizable for alternative model networks as well as experimental paradigms.

Understanding trained CNNs by indexing neuron selectivity

The impressive performance of Convolutional Neural Networks (CNNs) when solving different vision problems is shadowed by their black-box nature and our consequent lack of understanding of the representations they build and how these representations are organized. To help understanding these issues, we propose to describe the activity of individual neurons by their Neuron Feature visualization and quantify their inherent selectivity with two specific properties. We explore selectivity indexes for: an image feature (color); and an image label (class membership). Our contribution is a framework to seek or classify neurons by indexing on these selectivity properties. It helps to find color selective neurons, such as a red-mushroom neuron in layer Conv4 or class selective neurons such as dog-face neurons in layer Conv5 in VGG-M, and establishes a methodology to derive other selectivity properties. Indexing on neuron selectivity can statistically draw how features and classes are represented through layers in a moment when the size of trained nets is growing and automatic tools to index neurons can be helpful.

Book Review

Complexity theory proposes that to understand how complex systems such as the economy, the brain or the law work, we should focus on analysing the interactions rather than the individual components of the system. Studying the relatively simple behaviour of individual ants is not sufficient for understanding how colonies can build nests and bridges or studying the activity of individual neurons is not sufficient to explain thoughts, feelings and other large-scale brain activities. Similarly, Professor Steven Wheatley invites us to understand the concept of human rights that emerges from the complex interactions of states and non-state actors in the international law (IL) system, not as a static idea of natural rights or a positive collection of rules but as a dynamic practice that changes over time. With that in mind, one of the most significant contributions of this book to the literature on IL and international human rights law (IHRL) is the use of complexity theory in the understanding of the IL system. In recent years, there has been a growing body of literature focused on conceptualising the law as a complex system. Initially developed by the work of J.B. Ruhl in the mid-1990s, legal scholars including Julian Webb, Jamie Murray, Daniel Katz, Mark Chinen, Anna Marie Brennan and Thomas Webb have explored the idea of complexity in different areas of law.1 Continuing that endeavour, this book addresses an existing gap by exploring the dynamics of the IL system as a complex whole.

Whole-cell patch clamp for monitoring low--energy focused ultrasound (LEFUS)-induced electrical activity in individual neurons

The objective of this project was to integrate a LEFUS-system to a patch-clamp platform for exploration of LEFUS-modulated and/or -stimulated electrophysiological responses from individual cultured neurons. Mouse primary neurons and human neural progenitor cells were cultured and plated onto 35-mm diameter Petri dishes. A whole-cell patch-clamp setup in current-clamp mode was used to record electrophysiological activity generated by the neurons. The LEFUS system consisted of a 2.2-MHz planar transducer (diameter: 10-mm), administering 0.5–10 ms pulses at pressures of 6–50 kPa. Neuromodulation studies consisted in measuring changes in the activation threshold to electrical stimulation (current density required to trigger an action potential: AP) in LEFUS-treated neurons. Neurostimulation experiments consisted in measuring electrophysiological responses to LEFUS exposures in the form of triggered APs or electrical discharges from the patched neuron. Neuromodulation results showed that the activation threshold required for triggering APs could be either elevated or lowered by approximately 25% following LEFUS treatment. In neurostimulation experiments, LEFUS pressures of 25 kPa and pulse durations of 10 ms were sufficient for either disturbing the membrane potential of the cell and, occasionally, depolarizing the neuron by approximately 70 mV. [This project was supported by the French National Research Agency (ANR-16-TERC-0017), LabEx DevWeCan, and Focused Ultrasound Foundation (Centers of Excellence).]

Transplantation of Chemogenetically Engineered Cortical Interneuron Progenitors into Early Postnatal Mouse Brains.

Neuronal development is regulated by a complex combination of environmental and genetic factors. Assessing the relative contribution of each component is a complicated task, which is particularly difficult in regards to the development of γ-aminobutyric acid (GABA)ergic cortical interneurons (CIs). CIs are the main inhibitory neurons in the cerebral cortex, and they play key roles in neuronal networks, by regulating both the activity of individual pyramidal neurons, as well as the oscillatory behavior of neuronal ensembles. They are generated in transient embryonic structures (medial and caudal ganglionic eminences -MGE and CGE) that are very difficult to efficiently target using in utero electroporation approaches. Interneuron progenitors migrate long distances during normal embryonic development, before they integrate in the cortical circuit. This remarkable ability to disperse and integrate into a developing network can be hijacked by transplanting embryonic interneuron precursors into early post-natal host cortices. Here, we present a protocol that allows genetic modification of embryonic interneuron progenitors using focal ex vivo electroporation. These engineered interneuron precursors are then transplanted into early post-natal host cortices, where they will mature into easily identifiable CIs. This protocol allows the use of multiple genetically encoded tools, or the ability to regulate the expression of specific genes in interneuron progenitors, in order to investigate the impact of either genetic or environmental variables on the maturation and integration of CIs.

Firing dynamics in hybrid coupled populations of bistable neurons

We study the firing behavior of bistable neurons that are coupled by both electrical and chemical synapses, constituting a hybrid coupled population. We use stochastic Hodgkin–Huxley model neurons for the dynamics of individual bistable units within the population and the connectivity is implemented with a scale-free network topology which is a widely used network paradigm in theoretical studies of neural circuits. By analyzing the electrical activity of both whole population and individual neurons, it is shown that population mean firing rate exhibits a non-monotonic behavior in the space of electrical and chemical coupling strengths. We identify dynamical mechanisms that shape such non-monotonic spiking behavior and show that different types of population spiking patterns can emerge by fine-tuning the coupling strengths of electrical and chemical synapses. Furthermore, we map the transitions between observed dynamical states in the parameter space of interest depending on the level of individual neuron bistability, existence probability of a synapse type and intrinsic ion channel noise.

A Sparse Probabilistic Code Underlies the Limits of Behavioral Discrimination.

The cortical code that underlies perception must enable subjects to perceive the world at time scales relevant for behavior. We find that mice can integrate visual stimuli very quickly (<100 ms) to reach plateau performance in an orientation discrimination task. To define features of cortical activity that underlie performance at these time scales, we measured single-unit responses in the mouse visual cortex at time scales relevant to this task. In contrast to high-contrast stimuli of longer duration, which elicit reliable activity in individual neurons, stimuli at the threshold of perception elicit extremely sparse and unreliable responses in the primary visual cortex such that the activity of individual neurons does not reliably report orientation. Integrating information across neurons, however, quickly improves performance. Using a linear decoding model, we estimate that integrating information over 50–100 neurons is sufficient to account for behavioral performance. Thus, at the limits of visual perception, the visual system integrates information encoded in the probabilistic firing of unreliable single units to generate reliable behavior.

Spike-field Granger causality for hybrid neural data analysis.

Neurotechnological innovations allow for simultaneous recording at various scales, ranging from spiking activity of individual neurons to large neural populations' local field potentials (LFPs). This capability necessitates developing multiscale analysis of spike-field activity. A joint analysis of the hybrid neural data is crucial for bridging the scales between single neurons and local networks. Granger causality is a fundamental measure to evaluate directional influences among neural signals. However, it is mainly limited to inferring causal influence between the same type of signals-either LFPs or spike trains-and not well developed between two different signal types. Here we propose a model-free, nonparametric spike-field Granger causality measure for hybrid data analysis. Our measure is distinct from existing methods in that we use "binless" spikes (precise spike timing) rather than "binned" spikes (spike counts within small consecutive time windows). The latter clearly distort the information in the mixed analysis of spikes and LFP. Therefore, our spectral estimate of spike trains is directly applied to the neural point process itself, i.e., sequences of spike times rather than spike counts. Our measure is validated by an extensive set of simulated data. When the measure is applied to LFPs and spiking activity simultaneously recorded from visual areas V1 and V4 of monkeys performing a contour detection task, we are able to confirm computationally the long-standing experimental finding of the input-output relationship between LFPs and spikes. Importantly, we demonstrate that spike-field Granger causality can be used to reveal the modulatory effects that are inaccessible by traditional methods, such that spike→LFP Granger causality is modulated by the behavioral task, whereas LFP→spike Granger causality is mainly related to the average synaptic input.NEW & NOTEWORTHY It is a pressing question to study the directional interactions between local field potential (LFP) and spiking activity. In this report, we propose a model-free, nonparametric spike-field Granger causality measure that can be used to reveal directional influences between spikes and LFPs. This new measure is crucial for bridging the scales between single neurons and neural networks; hence it represents an important step to explicate how the brain orchestrates information processing.

Individual Neurons in the Cingulate Cortex Encode Action Monitoring, Not Selection, during Adaptive Decision-Making.

The cingulate cortex contributes to complex, adaptive behaviors, but the exact nature of its contributions remains unresolved. Proposals from previous studies, including evaluating past actions or selecting future ones, have been difficult to distinguish in part because of an incomplete understanding of the task-relevant variables that are encoded by individual cingulate neurons. In this study, we recorded from individual neurons in parts of both the anterior cingulate cortex (ACC) and posterior cingulate cortex (PCC) in 2 male rhesus monkeys performing a saccadic reward task. The task required them to use adaptive, feedback-driven strategies to infer the spatial location of a rewarded saccade target in the presence of different forms of uncertainty. We found that task-relevant, spatially selective feedback signals were encoded by the activity of individual neurons in both brain regions, with stronger selectivity for spatial choice and reward-target signals in PCC and stronger selectivity for feedback in ACC. Moreover, neurons in both regions were sensitive to sequential effects of feedback that partly reflected sequential behavioral patterns. However, neither brain region exhibited systematic modulations by the blockwise conditions that governed the reliability of the trial-by-trial feedback and drove adaptive behavioral patterns. There was also little evidence that single-neuron responses in either brain region directly predicted the extent to which feedback and contextual information were used to inform choices on the subsequent trial. Thus, certain cingulate neurons encode diverse, evaluative signals needed for adaptive, feedback-driven decision-making, but those signals may be integrated elsewhere in the brain to guide actions.SIGNIFICANCE STATEMENT Effective decision-making in dynamic environments requires adapting to changes in feedback and context. The anterior and posterior cingulate cortex have been implicated in adaptive decision-making, but the exact nature of their respective roles remains unresolved. Here we compare patterns of task-driven activity of subsets of individual neurons from parts of the two brain regions in monkeys performing a saccadic task with dynamically changing reward locations. We find evidence for regional specializations in neural representations of choice and feedback, including task-relevant modulations of activity that could be used for performance monitoring. However, we find little evidence that these neural representations are used directly to adjust choice behavior, which thus likely requires integration of these signals elsewhere in the brain.

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients.

Intracranial recordings from patients with intractable epilepsy provide a unique opportunity to study the activity of individual human neurons during active behavior. An important tool for quantifying behavior is eye tracking, which is an indispensable tool for studying visual attention. However, eye tracking is challenging to use concurrently with invasive electrophysiology and this approach has consequently been little used. Here, we present a proven experimental protocol to conduct single-neuron recordings with simultaneous eye tracking in humans. We describe how the systems are connected and the optimal settings to record neurons and eye movements. To illustrate the utility of this method, we summarize results that were made possible by this setup. This data shows how using eye tracking in a memory-guided visual search task allowed us to describe a new class of neurons called target neurons, whose response was reflective of top-down attention to the current search target. Lastly, we discuss the significance and solutions to potential problems of this setup. Together, our protocol and results suggest that single-neuron recordings with simultaneous eye tracking in humans are an effective method to study human brain function. It provides a key missing link between animal neurophysiology and human cognitive neuroscience.

Wide and Deep Imaging of Neuronal Activities by a Wearable NeuroImager Reveals Premotor Activity in the Whole Motor Cortex

Wearable technologies for functional whole brain imaging in freely moving animals would advance our understanding of cognitive processing and adaptive behavior. Fluorescence imaging can visualize the activity of individual neurons in real time, but conventional microscopes have limited sample coverage in both the width and depth of view. Here we developed a novel head-mounted laser camera (HLC) with macro and deep-focus lenses that enable fluorescence imaging at cellular resolution for comprehensive imaging in mice expressing a layer- and cell type-specific calcium probe. We visualized orientation selectivity in individual excitatory neurons across the whole visual cortex of one hemisphere, and cell assembly expressing the premotor activity that precedes voluntary movement across the motor cortex of both hemispheres. Including options for multiplex and wireless interfaces, our wearable, wide- and deep-imaging HLC technology could enable simple and economical mapping of neuronal populations underlying cognition and behavior.

Neuronal synchronization enhanced by neuron–astrocyte interaction

In this study, we consider the problem of signal processing in neuron–astrocyte networks where the intercellular communication is described on the basis of a tripartite synapse concept. This type of communication involves combined contributions of pre- and postsynaptic neuronal compartments and components of the surrounding astrocyte to the processes of information transmission. Astrocyte-mediated regulation of neuronal activity is considered through the analysis of the response changes in the classical Hodgkin–Huxley model driven by excitatory synaptic current. It is shown that the complicated astrocyte-dependent dynamics of this current can lead to non-trivial changes in individual postsynaptic neuronal activity and, hence, in the cooperative activation of neuronal groups linked by the “astrocyte-mediated bridge”.