Emergent reliability in sensory cortical coding and inter-area communication

Reliable sensory processing of superficial cortical interneurons is modulated by behavioral state

GABAergic interneurons in cortical layer 1 (L1) integrate sensory and top-down inputs to modulate network activity and support learning-related plasticity. However, little is known about how sensory inputs drive L1 interneuron activity. We used two-photon calcium imaging to measure sound-evoked responses in two L1 interneuron populations expressing vasoactive intestinal peptide (VIP) or neuron-derived neurotrophic factor (NDNF) in mouse auditory cortex. We found that L1 interneurons respond to both simple and complex sounds, but their responses are highly variable across trials. Despite this variability, these interneurons respond reliably to a narrow range of stimuli, reflecting selectivity for specific spectrotemporal sound features. Response reliability was modulated by behavioral state and predicted by the activity of neighboring interneurons. These findings reveal that L1 interneurons exhibit sensory tuning and identify the modulation of response reliability as a potential mechanism by which L1 relays state-dependent cues to shape sensory representations.

Noise correlations and neuronal diversity may limit the utility of winner-take-all readout in a pop out visual search task

Visual search involves active scanning of the environment to locate objects of interest against a background of irrelevant distractors. One widely accepted theory posits that pop out visual search is computed by a winner-take-all (WTA) competition between contextually modulated cells that form a saliency map. However, previous studies have shown that the ability of WTA mechanisms to accumulate information from large populations of neurons is limited, thus raising the question of whether WTA can underlie pop out visual search. To address this question, we conducted a modeling study to investigate how accurately the WTA mechanism can detect the deviant stimulus in a pop out task. We analyzed two types of WTA readout mechanisms: single-best-cell WTA, where the decision is made based on a single winning cell, and a generalized population-based WTA, where the decision is based on the winning population of similarly tuned cells. Our results show that neither WTA mechanism can account for the high accuracy found in behavioral experiments. The inherent neuronal heterogeneity prevents the single-best-cell WTA from accumulating information even from large populations, whereas the accuracy of the generalized population-based WTA algorithm is negatively affected by the widely reported noise correlations. These findings underscore the need to revisit the key assumptions explored in our theoretical analysis, particularly concerning the decoding mechanism and the statistical properties of neuronal population responses to pop out stimuli. The analysis identifies specific response statistics that require further empirical characterization to accurately predict WTA performance in biologically plausible models of visual pop out detection.

Dynamic categorization rules alter representations in human visual cortex

Everyday tasks often require stimuli to be categorized dynamically, such that an identical object can elicit different responses based on the current decision rule. Traditionally, sensory regions have been viewed as separate from such context-dependent processing, functioning primarily to process incoming inputs. However, an alternative view suggests sensory regions also integrate inputs with current task goals, facilitating more efficient information relay to higher-level areas. Here we test this by asking human participants to visually categorize novel shape stimuli based on different decision boundaries. Using fMRI and multivariate analyses of retinotopically-defined visual areas, we show that cortical shape representations become more distinct across relevant decision boundaries in a context-dependent manner, with the largest changes in discriminability observed for stimuli near the decision boundary. Importantly, these modulations are associated with improved task performance. These findings demonstrate that visual cortex representations are adaptively modulated to support dynamic behavior.

Neural heterogeneity enhances reliable neural information processing: Local sensitivity and globally input-slaved transient dynamics

Cortical neuronal activity varies over time and across repeated trials, yet consistently represents stimulus features. The dynamical mechanism underlying this reliable representation and computation remains elusive. This study uncovers a mechanism for reliable neural information processing, leveraging a biologically plausible network model incorporating neural heterogeneity. First, we investigate neuronal timescale diversity, revealing that it disrupts intrinsic coherent spatiotemporal patterns, induces firing rate heterogeneity, enhances local responsive sensitivity, and aligns network activity closely with input. The system exhibits globally input-slaved transient dynamics, essential for reliable neural information processing. Other neural heterogeneities, such as nonuniform input connections, spike threshold heterogeneity, and network in-degree heterogeneity, play similar roles, highlighting the importance of neural heterogeneity in shaping consistent stimulus representation. This mechanism offers a potentially general framework for understanding neural heterogeneity in reliable computation and informs the design of reservoir computing models endowed with liquid wave reservoirs for neuromorphic computing.

Excitation-Inhibition Balance, Neural Criticality, and Activities in Neuronal Circuits

Neural activities in local circuits exhibit complex and multilevel dynamic features. Individual neurons spike irregularly, which is believed to originate from receiving balanced amounts of excitatory and inhibitory inputs, known as the excitation-inhibition balance. The spatial-temporal cascades of clustered neuronal spikes occur in variable sizes and durations, manifested as neural avalanches with scale-free features. These may be explained by the neural criticality hypothesis, which posits that neural systems operate around the transition between distinct dynamic states. Here, we summarize the experimental evidence for and the underlying theory of excitation-inhibition balance and neural criticality. Furthermore, we review recent studies of excitatory-inhibitory networks with synaptic kinetics as a simple solution to reconcile these two apparently distinct theories in a single circuit model. This provides a more unified understanding of multilevel neural activities in local circuits, from spontaneous to stimulus-response dynamics.

Critical Avalanches in Excitation-Inhibition Balanced Networks Reconcile Response Reliability with Sensitivity for Optimal Neural Representation

Neural criticality has emerged as a unified framework that reconciles diverse multiscale neuronal dynamics such as the irregular firing of individual neurons, sparse synchrony in neuronal populations, and the emergence of scale-free avalanches. However, the functional role of neuronal criticality remains ambiguous. Here, we investigate the neural dynamics and representations in response to external signals in excitation-inhibition balanced networks. We reveal that, in contrast with the case for the traditional critical branching model, the critical state of the balanced network simultaneously achieves maximal response sensitivity, maximal response reliability, and the optimal representation of external signals due to the presence of reliable avalanches induced by external signals. We further demonstrate that heterogeneity in inhibitory connections is a mechanism underlying the reliable critical avalanches and optimal representation. Our study addresses a longstanding challenge concerning the functional significance of neuronal criticality, namely the intricate coexistence of reliability and sensitivity.

Discretized representations in V1 predict suboptimal orientation discrimination

Neuronal population activity in sensory cortices is the substrate for perceptual decisions. Yet, we still do not understand how neuronal information content in sensory cortices relates to behavioral reports. To reconcile neurometric and psychometric performance, we recorded the activity of V1 neurons in mice performing a Go/NoGo orientation discrimination task. We found that, around the discrimination threshold, V1 does not represent the orientation of the stimuli as canonically expected. Instead, it forms categorical representations characterized by a relocation of activity at task-relevant domains of the orientation representational space. The relative neuronal activity at those discrete domains accurately predicted the probabilities of the animals' decisions. Our results thus suggest that the categorical integration of discretized feature representations from sensory cortices explains perceptual decisions.

Stimulus-invariant aspects of the retinal code drive discriminability of natural scenes

Everything that the brain sees must first be encoded by the retina, which maintains a reliable representation of the visual world in many different, complex natural scenes while also adapting to stimulus changes. This study quantifies whether and how the brain selectively encodes stimulus features about scene identity in complex naturalistic environments. While a wealth of previous work has dug into the static and dynamic features of the population code in retinal ganglion cells (RGCs), less is known about how populations form both flexible and reliable encoding in natural moving scenes. We record from the larval salamander retina responding to five different natural movies, over many repeats, and use these data to characterize the population code in terms of single-cell fluctuations in rate and pairwise couplings between cells. Decomposing the population code into independent and cell-cell interactions reveals how broad scene structure is encoded in the retinal output. while the single-cell activity adapts to different stimuli, the population structure captured in the sparse, strong couplings is consistent across natural movies as well as synthetic stimuli. We show that these interactions contribute to encoding scene identity. We also demonstrate that this structure likely arises in part from shared bipolar cell input as well as from gap junctions between RGCs and amacrine cells.

Measuring Stimulus Information Transfer Between Neural Populations through the Communication Subspace

Sensory processing arises from the communication between neural populations across multiple brain areas. While the widespread presence of neural response variability shared throughout a neural population limits the amount of stimulus-related information those populations can accurately represent, how this variability affects the interareal communication of sensory information is unknown. We propose a mathematical framework to understand the impact of neural population response variability on sensory information transmission. We combine linear Fisher information, a metric connecting stimulus representation and variability, with the framework of communication subspaces, which suggests that functional mappings between cortical populations are low-dimensional relative to the space of population activity patterns. From this, we partition Fisher information depending on the alignment between the population covariance and the mean tuning direction projected onto the communication subspace or its orthogonal complement. We provide mathematical and numerical analyses of our proposed decomposition of Fisher information and examine theoretical scenarios that demonstrate how to leverage communication subspaces for flexible routing and gating of stimulus information. This work will provide researchers investigating interareal communication with a theoretical lens through which to understand sensory information transmission and guide experimental design.

Brain-wide neural recordings in mice navigating physical spaces enabled by robotic neural recording headstages

Technologies that can record neural activity at cellular resolution at multiple spatial and temporal scales are typically much larger than the animals that are being recorded from and are thus limited to recording from head-fixed subjects. Here we have engineered robotic neural recording devices-'cranial exoskeletons'-that assist mice in maneuvering recording headstages that are orders of magnitude larger and heavier than the mice, while they navigate physical behavioral environments. We discovered optimal controller parameters that enable mice to locomote at physiologically realistic velocities while maintaining natural walking gaits. We show that mice learn to work with the robot to make turns and perform decision-making tasks. Robotic imaging and electrophysiology headstages were used to record recordings of Ca2+ activity of thousands of neurons distributed across the dorsal cortex and spiking activity of hundreds of neurons across multiple brain regions and multiple days, respectively.

Long-term mesoscale imaging of 3D intercellular dynamics across a mammalian organ

A comprehensive understanding of physio-pathological processes necessitates non-invasive intravital three-dimensional (3D) imaging over varying spatial and temporal scales. However, huge data throughput, optical heterogeneity, surface irregularity, and phototoxicity pose great challenges, leading to an inevitable trade-off between volume size, resolution, speed, sample health, and system complexity. Here, we introduce a compact real-time, ultra-large-scale, high-resolution 3D mesoscope (RUSH3D), achieving uniform resolutions of 2.6 × 2.6 × 6 μm3 across a volume of 8,000 × 6,000 × 400 μm3 at 20 Hz with low phototoxicity. Through the integration of multiple computational imaging techniques, RUSH3D facilitates a 13-fold improvement in data throughput and an orders-of-magnitude reduction in system size and cost. With these advantages, we observed premovement neural activity and cross-day visual representational drift across the mouse cortex, the formation and progression of multiple germinal centers in mouse inguinal lymph nodes, and heterogeneous immune responses following traumatic brain injury-all at single-cell resolution, opening up a horizon for intravital mesoscale study of large-scale intercellular interactions at the organ level.

Neuronal firing rate diversity lowers the dimension of population covariability

Populations of neurons produce activity with two central features. First, neuronal responses are very diverse - specific stimuli or behaviors prompt some neurons to emit many action potentials, while other neurons remain relatively silent. Second, the trial-to-trial fluctuations of neuronal response occupy a low dimensional space, owing to significant correlations between the activity of neurons. These two features define the quality of neuronal representation. We link these two aspects of population response using a recurrent circuit model and derive the following relation: the more diverse the firing rates of neurons in a population, the lower the effective dimension of population trial-to-trial covariability. This surprising prediction is tested and validated using simultaneously recorded neuronal populations from numerous brain areas in mice, non-human primates, and in the motor cortex of human participants. Using our relation we present a theory where a more diverse neuronal code leads to better fine discrimination performance from population activity. In line with this theory, we show that neuronal populations across the brain exhibit both more diverse mean responses and lower-dimensional fluctuations when the brain is in more heightened states of information processing. In sum, we present a key organizational principle of neuronal population response that is widely observed across the nervous system and acts to synergistically improve population representation.

Flexible neural population dynamics govern the speed and stability of sensory encoding in mouse visual cortex

Time courses of neural responses underlie real-time sensory processing and perception. How these temporal dynamics change may be fundamental to how sensory systems adapt to different perceptual demands. By simultaneously recording from hundreds of neurons in mouse primary visual cortex, we examined neural population responses to visual stimuli at sub-second timescales, during different behavioural states. We discovered that during active behavioural states characterised by locomotion, single-neurons shift from transient to sustained response modes, facilitating rapid emergence of visual stimulus tuning. Differences in single-neuron response dynamics were associated with changes in temporal dynamics of neural correlations, including faster stabilisation of stimulus-evoked changes in the structure of correlations during locomotion. Using Factor Analysis, we examined temporal dynamics of latent population responses and discovered that trajectories of population activity make more direct transitions between baseline and stimulus-encoding neural states during locomotion. This could be partly explained by dampening of oscillatory dynamics present during stationary behavioural states. Functionally, changes in temporal response dynamics collectively enabled faster, more stable and more efficient encoding of new visual information during locomotion. These findings reveal a principle of how sensory systems adapt to perceptual demands, where flexible neural population dynamics govern the speed and stability of sensory encoding.

Pan-cortical cellular imaging in freely behaving mice using a miniaturized micro-camera array microscope (mini-MCAM)

Understanding how circuits in the brain simultaneously coordinate their activity to mediate complex ethnologically relevant behaviors requires recording neural activities from distributed populations of neurons in freely behaving animals. Current miniaturized imaging microscopes are typically limited to imaging a relatively small field of view, precluding the measurement of neural activities across multiple brain regions. Here we present a miniaturized micro-camera array microscope (mini-MCAM) that consists of four fluorescence imaging micro-cameras, each capable of capturing neural activity across a 4.5 mm x 2.55 mm field of view (FOV). Cumulatively, the mini-MCAM images over 30 mm 2 area of sparsely expressed GCaMP6s neurons distributed throughout the dorsal cortex, in regions including the primary and secondary motor, somatosensory, visual, retrosplenial, and association cortices across both hemispheres. We demonstrate cortex-wide cellular resolution in vivo Calcium (Ca 2+ ) imaging using the mini-MCAM in both head-fixed and freely behaving mice.

Context-invariant beliefs are supported by dynamic reconfiguration of single unit functional connectivity in prefrontal cortex of male macaques

Natural behaviors occur in closed action-perception loops and are supported by dynamic and flexible beliefs abstracted away from our immediate sensory milieu. How this real-world flexibility is instantiated in neural circuits remains unknown. Here, we have male macaques navigate in a virtual environment by primarily leveraging sensory (optic flow) signals, or by more heavily relying on acquired internal models. We record single-unit spiking activity simultaneously from the dorsomedial superior temporal area (MSTd), parietal area 7a, and the dorso-lateral prefrontal cortex (dlPFC). Results show that while animals were able to maintain adaptive task-relevant beliefs regardless of sensory context, the fine-grain statistical dependencies between neurons, particularly in 7a and dlPFC, dynamically remapped with the changing computational demands. In dlPFC, but not 7a, destroying these statistical dependencies abolished the area's ability for cross-context decoding. Lastly, correlational analyses suggested that the more unit-to-unit couplings remapped in dlPFC, and the less they did so in MSTd, the less were population codes and behavior impacted by the loss of sensory evidence. We conclude that dynamic functional connectivity between neurons in prefrontal cortex maintain a stable population code and context-invariant beliefs during naturalistic behavior.

A miniaturized mesoscope for the large-scale single-neuron-resolved imaging of neuronal activity in freely behaving mice

Exploring the relationship between neuronal dynamics and ethologically relevant behaviour involves recording neuronal-population activity using technologies that are compatible with unrestricted animal behaviour. However, head-mounted microscopes that accommodate weight limits to allow for free animal behaviour typically compromise field of view, resolution or depth range, and are susceptible to movement-induced artefacts. Here we report a miniaturized head-mounted fluorescent mesoscope that we systematically optimized for calcium imaging at single-neuron resolution, for increased fields of view and depth of field, and for robustness against motion-generated artefacts. Weighing less than 2.5 g, the mesoscope enabled recordings of neuronal-population activity at up to 16 Hz, with 4 μm resolution over 300 μm depth-of-field across a field of view of 3.6 × 3.6 mm2 in the cortex of freely moving mice. We used the mesoscope to record large-scale neuronal-population activity in socially interacting mice during free exploration and during fear-conditioning experiments, and to investigate neurovascular coupling across multiple cortical regions.

Basic Properties of Coordinated Neuronal Ensembles in the Auditory Thalamus

Coordinated neuronal activity has been identified to play an important role in information processing and transmission in the brain. However, current research predominantly focuses on understanding the properties and functions of neuronal coordination in hippocampal and cortical areas, leaving subcortical regions relatively unexplored. In this study, we use single-unit recordings in female Sprague Dawley rats to investigate the properties and functions of groups of neurons exhibiting coordinated activity in the auditory thalamus-the medial geniculate body (MGB). We reliably identify coordinated neuronal ensembles (cNEs), which are groups of neurons that fire synchronously, in the MGB. cNEs are shown not to be the result of false-positive detections or by-products of slow-state oscillations in anesthetized animals. We demonstrate that cNEs in the MGB have enhanced information-encoding properties over individual neurons. Their neuronal composition is stable between spontaneous and evoked activity, suggesting limited stimulus-induced ensemble dynamics. These MGB cNE properties are similar to what is observed in cNEs in the primary auditory cortex (A1), suggesting that ensembles serve as a ubiquitous mechanism for organizing local networks and play a fundamental role in sensory processing within the brain.

Cross-hemispheric communication: Insights on lateralized brain functions

On the surface, the two hemispheres of vertebrate brains look almost perfectly symmetrical, but several motor, sensory, and cognitive systems show a deeply lateralized organization. Importantly, the two hemispheres are connected by various commissures, white matter tracts that cross the brain's midline and enable cross-hemispheric communication. Cross-hemispheric communication has been suggested to play an important role in the emergence of lateralized brain functions. Here, we review current advances in understanding cross-hemispheric communication that have been made using modern neuroscientific tools in rodents and other model species, such as genetic labeling, large-scale recordings of neuronal activity, spatiotemporally precise perturbation, and quantitative behavior analyses. These findings suggest that the emergence of lateralized brain functions cannot be fully explained by largely static factors such as genetic variation and differences in structural brain asymmetries. In addition, learning-dependent asymmetric interactions between the left and right hemispheres shape lateralized brain functions.

Space wandering in the rodent default mode network

The default mode network (DMN) is a large-scale brain network known to be suppressed during a wide range of cognitive tasks. However, our comprehension of its role in naturalistic and unconstrained behaviors has remained elusive because most research on the DMN has been conducted within the restrictive confines of MRI scanners. Here, we use multisite GCaMP (a genetically encoded calcium indicator) fiber photometry with simultaneous videography to probe DMN function in awake, freely exploring rats. We examined neural dynamics in three core DMN nodes-the retrosplenial cortex, cingulate cortex, and prelimbic cortex-as well as the anterior insula node of the salience network, and their association with the rats' spatial exploration behaviors. We found that DMN nodes displayed a hierarchical functional organization during spatial exploration, characterized by stronger coupling with each other than with the anterior insula. Crucially, these DMN nodes encoded the kinematics of spatial exploration, including linear and angular velocity. Additionally, we identified latent brain states that encoded distinct patterns of time-varying exploration behaviors and found that higher linear velocity was associated with enhanced DMN activity, heightened synchronization among DMN nodes, and increased anticorrelation between the DMN and anterior insula. Our findings highlight the involvement of the DMN in collectively and dynamically encoding spatial exploration in a real-world setting. Our findings challenge the notion that the DMN is primarily a "task-negative" network disengaged from the external world. By illuminating the DMN's role in naturalistic behaviors, our study underscores the importance of investigating brain network function in ecologically valid contexts.

What does the mean mean? A simple test for neuroscience

Trial-averaged metrics, e.g. tuning curves or population response vectors, are a ubiquitous way of characterizing neuronal activity. But how relevant are such trial-averaged responses to neuronal computation itself? Here we present a simple test to estimate whether average responses reflect aspects of neuronal activity that contribute to neuronal processing. The test probes two assumptions implicitly made whenever average metrics are treated as meaningful representations of neuronal activity: Reliability: Neuronal responses repeat consistently enough across trials that they convey a recognizable reflection of the average response to downstream regions.Behavioural relevance: If a single-trial response is more similar to the average template, it is more likely to evoke correct behavioural responses. We apply this test to two data sets: (1) Two-photon recordings in primary somatosensory cortices (S1 and S2) of mice trained to detect optogenetic stimulation in S1; and (2) Electrophysiological recordings from 71 brain areas in mice performing a contrast discrimination task. Under the highly controlled settings of Data set 1, both assumptions were largely fulfilled. In contrast, the less restrictive paradigm of Data set 2 met neither assumption. Simulations predict that the larger diversity of neuronal response preferences, rather than higher cross-trial reliability, drives the better performance of Data set 1. We conclude that when behaviour is less tightly restricted, average responses do not seem particularly relevant to neuronal computation, potentially because information is encoded more dynamically. Most importantly, we encourage researchers to apply this simple test of computational relevance whenever using trial-averaged neuronal metrics, in order to gauge how representative cross-trial averages are in a given context.

Representational maps in the brain: concepts, approaches, and applications

Neural systems have evolved to process sensory stimuli in a way that allows for efficient and adaptive behavior in a complex environment. Recent technological advances enable us to investigate sensory processing in animal models by simultaneously recording the activity of large populations of neurons with single-cell resolution, yielding high-dimensional datasets. In this review, we discuss concepts and approaches for assessing the population-level representation of sensory stimuli in the form of a representational map. In such a map, not only are the identities of stimuli distinctly represented, but their relational similarity is also mapped onto the space of neuronal activity. We highlight example studies in which the structure of representational maps in the brain are estimated from recordings in humans as well as animals and compare their methodological approaches. Finally, we integrate these aspects and provide an outlook for how the concept of representational maps could be applied to various fields in basic and clinical neuroscience.

Behavior-relevant top-down cross-modal predictions in mouse neocortex

Animals adapt to a constantly changing world by predicting their environment and the consequences of their actions. The predictive coding hypothesis proposes that the brain generates predictions and continuously compares them with sensory inputs to guide behavior. However, how the brain reconciles conflicting top-down predictions and bottom-up sensory information remains unclear. To address this question, we simultaneously imaged neuronal populations in the mouse somatosensory barrel cortex and posterior parietal cortex during an auditory-cued texture discrimination task. In mice that had learned the task with fixed tone-texture matching, the presentation of mismatched pairing induced conflicts between tone-based texture predictions and actual texture inputs. When decisions were based on the predicted rather than the actual texture, top-down information flow was dominant and texture representations in both areas were modified, whereas dominant bottom-up information flow led to correct representations and behavioral choice. Our findings provide evidence for hierarchical predictive coding in the mouse neocortex.

Modern Methods for Unraveling Cell- and Circuit-Level Mechanisms of Neurophysiological Biomarkers in Psychiatry

Methods for studying the mammalian brain in vivo have advanced dramatically in the past two decades. State-of-the-art optical and electrophysiological techniques allow direct recordings of the functional dynamics of thousands of neurons across distributed brain circuits with single-cell resolution. With transgenic tools, specific neuron types, pathways, and/or neurotransmitters can be targeted in user-determined brain areas for precise measurement and manipulation. In this chapter, we catalog these advancements. We emphasize that the impact of this methodological revolution on neuropsychiatry remains uncertain. This stems from the fact that these tools remain mostly limited to research in mice. And while translational paradigms are needed, recapitulations of human psychiatric disease states (e.g., schizophrenia) in animal models are inherently challenging to validate and may have limited utility in heterogeneous disease populations. Here we focus on an alternative strategy aimed at the study of neurophysiological biomarkers-the subject of this volume-translated to animal models, where precision neuroscience tools can be applied to provide molecular, cellular, and circuit-level insights and novel therapeutic targets. We summarize several examples of this approach throughout the chapter and emphasize the importance of careful experimental design and choice of dependent measures.

Silences, spikes and bursts: Three-part knot of the neural code

When a neuron breaks silence, it can emit action potentials in a number of patterns. Some responses are so sudden and intense that electrophysiologists felt the need to single them out, labelling action potentials emitted at a particularly high frequency with a metonym - bursts. Is there more to bursts than a figure of speech? After all, sudden bouts of high-frequency firing are expected to occur whenever inputs surge. The burst coding hypothesis advances that the neural code has three syllables: silences, spikes and bursts. We review evidence supporting this ternary code in terms of devoted mechanisms for burst generation, synaptic transmission and synaptic plasticity. We also review the learning and attention theories for which such a triad is beneficial.

Recent strategies for neural dynamics observation at a larger scale and wider scope

The tremendous technical progress in neuroscience offers opportunities to observe a more minor or/and broader dynamic picture of the brain. Moreover, the large-scale neural activity of individual neurons enables the dissection of detailed mechanistic links between neural populations and behaviors. To measure neural activity in-vivo, multi-neuron recording, and neuroimaging techniques are employed and developed to acquire more neurons. The tools introduced concurrently recorded dozens to hundreds of neurons in the coordinated brain regions and elucidated the neuronal ensembles from a massive population perspective of diverse neurons at cellular resolution. In particular, the increasing spatiotemporal resolution of neuronal monitoring across the whole brain dramatically facilitates our understanding of additional nervous system functions in health and disease. Here, we will introduce state-of-the-art neuroscience tools involving large-scale neural population recording and the long-range connections spanning multiple brain regions. Their synergic effects provide to clarify the controversial circuitry underlying neuroscience. These challenging neural tools present a promising outlook for the fundamental dynamic interplay across levels of synaptic cellular, circuit organization, and brain-wide. Hence, more observations of neural dynamics will provide more clues to elucidate brain functions and push forward innovative technology at the intersection of neural engineering disciplines. We hope this review will provide insight into the use or development of recent neural techniques considering spatiotemporal scales of brain observation.

Internal feedback in the cortical perception-action loop enables fast and accurate behavior

Animals move smoothly and reliably in unpredictable environments. Models of sensorimotor control, drawing on control theory, have assumed that sensory information from the environment leads to actions, which then act back on the environment, creating a single, unidirectional perception-action loop. However, the sensorimotor loop contains internal delays in sensory and motor pathways, which can lead to unstable control. We show here that these delays can be compensated by internal feedback signals that flow backward, from motor toward sensory areas. This internal feedback is ubiquitous in neural sensorimotor systems, and we show how internal feedback compensates internal delays. This is accomplished by filtering out self-generated and other predictable changes so that unpredicted, actionable information can be rapidly transmitted toward action by the fastest components, effectively compressing the sensory input to more efficiently use feedforward pathways: Tracts of fast, giant neurons necessarily convey less accurate signals than tracts with many smaller neurons, but they are crucial for fast and accurate behavior. We use a mathematically tractable control model to show that internal feedback has an indispensable role in achieving state estimation, localization of function (how different parts of the cortex control different parts of the body), and attention, all of which are crucial for effective sensorimotor control. This control model can explain anatomical, physiological, and behavioral observations, including motor signals in the visual cortex, heterogeneous kinetics of sensory receptors, and the presence of giant cells in the cortex of humans as well as internal feedback patterns and unexplained heterogeneity in neural systems.

Space wandering in the rodent default mode network

The default mode network (DMN) is a large-scale brain network known to be suppressed during a wide range of cognitive tasks. However, our comprehension of its role in naturalistic and unconstrained behaviors has remained elusive because most research on the DMN has been conducted within the restrictive confines of MRI scanners. Here we use multisite GCaMP fiber photometry with simultaneous videography to probe DMN function in awake, freely exploring rats. We examined neural dynamics in three core DMN nodes- the retrosplenial cortex, cingulate cortex, and prelimbic cortex- as well as the anterior insula node of the salience network, and their association with the rats' spatial exploration behaviors. We found that DMN nodes displayed a hierarchical functional organization during spatial exploration, characterized by stronger coupling with each other than with the anterior insula. Crucially, these DMN nodes encoded the kinematics of spatial exploration, including linear and angular velocity. Additionally, we identified latent brain states that encoded distinct patterns of time-varying exploration behaviors and discovered that higher linear velocity was associated with enhanced DMN activity, heightened synchronization among DMN nodes, and increased anticorrelation between the DMN and anterior insula. Our findings highlight the involvement of the DMN in collectively and dynamically encoding spatial exploration in a real-world setting. Our findings challenge the notion that the DMN is primarily a "task-negative" network disengaged from the external world. By illuminating the DMN's role in naturalistic behaviors, our study underscores the importance of investigating brain network function in ecologically valid contexts.

20 years of the default mode network: A review and synthesis

The discovery of the default mode network (DMN) has revolutionized our understanding of the workings of the human brain. Here, I review developments that led to the discovery of the DMN, offer a personal reflection, and consider how our ideas of DMN function have evolved over the past two decades. I summarize literature examining the role of the DMN in self-reference, social cognition, episodic and autobiographical memory, language and semantic memory, and mind wandering. I identify unifying themes and propose new perspectives on the DMN's role in human cognition. I argue that the DMN integrates and broadcasts memory, language, and semantic representations to create a coherent "internal narrative" reflecting our individual experiences. This narrative is central to the construction of a sense of self, shapes how we perceive ourselves and interact with others, may have ontogenetic origins in self-directed speech during childhood, and forms a vital component of human consciousness.

Representational drift as a window into neural and behavioural plasticity

Large-scale recordings of neural activity over days and weeks have revealed that neural representations of familiar tasks, precepts and actions continually evolve without obvious changes in behaviour. We hypothesise that this steady drift in neural activity and accompanying physiological changes is due in part to the continuous application of a learning rule at the cellular and population level. Explicit predictions of this drift can be found in neural network models that use iterative learning to optimise weights. Drift therefore provides a measurable signal that can reveal systems-level properties of biological plasticity mechanisms, such as their precision and effective learning rates.

Context-invariant beliefs are supported by dynamic reconfiguration of single unit functional connectivity in prefrontal cortex

Natural behaviors occur in closed action-perception loops and are supported by dynamic and flexible beliefs abstracted away from our immediate sensory milieu. How this real-world flexibility is instantiated in neural circuits remains unknown. Here we have macaques navigate in a virtual environment by primarily leveraging sensory (optic flow) signals, or by more heavily relying on acquired internal models. We record single-unit spiking activity simultaneously from the dorsomedial superior temporal area (MSTd), parietal area 7a, and the dorso-lateral prefrontal cortex (dlPFC). Results show that while animals were able to maintain adaptive task-relevant beliefs regardless of sensory context, the fine-grain statistical dependencies between neurons, particularly in 7a and dlPFC, dynamically remapped with the changing computational demands. In dlPFC, but not 7a, destroying these statistical dependencies abolished the area's ability for cross-context decoding. Lastly, correlation analyses suggested that the more unit-to-unit couplings remapped in dlPFC, and the less they did so in MSTd, the less were population codes and behavior impacted by the loss of sensory evidence. We conclude that dynamic functional connectivity between prefrontal cortex neurons maintains a stable population code and context-invariant beliefs during naturalistic behavior with closed action-perception loops.

Representations in human primary visual cortex drift over time

Primary sensory regions are believed to instantiate stable neural representations, yet a number of recent rodent studies suggest instead that representations drift over time. To test whether sensory representations are stable in human visual cortex, we analyzed a large longitudinal dataset of fMRI responses to images of natural scenes. We fit the fMRI responses using an image-computable encoding model and tested how well the model generalized across sessions. We found systematic changes in model fits that exhibited cumulative drift over many months. Convergent analyses pinpoint changes in neural responsivity as the source of the drift, while population-level representational dissimilarities between visual stimuli were unchanged. These observations suggest that downstream cortical areas may read-out a stable representation, even as representations within V1 exhibit drift.

Active experience, not time, determines within-day representational drift in dorsal CA1

Memories of past events can be recalled long after the event, indicating stability. But new experiences are also integrated into existing memories, indicating plasticity. In the hippocampus, spatial representations are known to remain stable but have also been shown to drift over long periods of time. We hypothesized that experience, more than the passage of time, is the driving force behind representational drift. We compared the within-day stability of place cells' representations in dorsal CA1 of the hippocampus of mice traversing two similar, familiar tracks for different durations. We found that the more time the animals spent actively traversing the environment, the greater the representational drift, regardless of the total elapsed time between visits. Our results suggest that spatial representation is a dynamic process, related to the ongoing experiences within a specific context, and is related to memory update rather than to passive forgetting.

Brain-wide neural recordings in mice navigating physical spaces enabled by a cranial exoskeleton

Complex behaviors are mediated by neural computations occurring throughout the brain. In recent years, tremendous progress has been made in developing technologies that can record neural activity at cellular resolution at multiple spatial and temporal scales. However, these technologies are primarily designed for studying the mammalian brain during head fixation - wherein the behavior of the animal is highly constrained. Miniaturized devices for studying neural activity in freely behaving animals are largely confined to recording from small brain regions owing to performance limitations. We present a cranial exoskeleton that assists mice in maneuvering neural recording headstages that are orders of magnitude larger and heavier than the mice, while they navigate physical behavioral environments. Force sensors embedded within the headstage are used to detect the mouse's milli-Newton scale cranial forces which then control the x, y, and yaw motion of the exoskeleton via an admittance controller. We discovered optimal controller tuning parameters that enable mice to locomote at physiologically realistic velocities and accelerations while maintaining natural walking gait. Mice maneuvering headstages weighing up to 1.5 kg can make turns, navigate 2D arenas, and perform a navigational decision-making task with the same performance as when freely behaving. We designed an imaging headstage and an electrophysiology headstage for the cranial exoskeleton to record brain-wide neural activity in mice navigating 2D arenas. The imaging headstage enabled recordings of Ca2+ activity of 1000s of neurons distributed across the dorsal cortex. The electrophysiology headstage supported independent control of up to 4 silicon probes, enabling simultaneous recordings from 100s of neurons across multiple brain regions and multiple days. Cranial exoskeletons provide flexible platforms for largescale neural recording during the exploration of physical spaces, a critical new paradigm for unraveling the brain-wide neural mechanisms that control complex behavior.

A unifying perspective on neural manifolds and circuits for cognition

Two different perspectives have informed efforts to explain the link between the brain and behaviour. One approach seeks to identify neural circuit elements that carry out specific functions, emphasizing connectivity between neurons as a substrate for neural computations. Another approach centres on neural manifolds - low-dimensional representations of behavioural signals in neural population activity - and suggests that neural computations are realized by emergent dynamics. Although manifolds reveal an interpretable structure in heterogeneous neuronal activity, finding the corresponding structure in connectivity remains a challenge. We highlight examples in which establishing the correspondence between low-dimensional activity and connectivity has been possible, unifying the neural manifold and circuit perspectives. This relationship is conspicuous in systems in which the geometry of neural responses mirrors their spatial layout in the brain, such as the fly navigational system. Furthermore, we describe evidence that, in systems in which neural responses are heterogeneous, the circuit comprises interactions between activity patterns on the manifold via low-rank connectivity. We suggest that unifying the manifold and circuit approaches is important if we are to be able to causally test theories about the neural computations that underlie behaviour.

Comparing representations and computations in single neurons versus neural networks

Single-neuron-level explanations have been the gold standard in neuroscience for decades. Recently, however, neural-network-level explanations have become increasingly popular. This increase in popularity is driven by the fact that the analysis of neural networks can solve problems that cannot be addressed by analyzing neurons independently. In this opinion article, I argue that while both frameworks employ the same general logic to link physical and mental phenomena, in many cases the neural network framework provides better explanatory objects to understand representations and computations related to mental phenomena. I discuss what constitutes a mechanistic explanation in neural systems, provide examples, and conclude by highlighting a number of the challenges and considerations associated with the use of analyses of neural networks to study brain function.

Local myelin damage in the hippocampus fluctuates gut microbiome profile and memory

The concept of the gut-brain axis has focused research on how gut dysbiosis affects myelin biology in the brain. However, this axis has not been tested to determine whether it conveys the effects of myelin damage on the gut microbiome profile. Therefore, we aimed to investigate how myelin biology is correlated with gut microbiome profile. The impact of local myelin damage in the hippocampus on gut microbiome profile was investigated with 16S rRNA metagenomic sequence and molecular analysis of myelin biology-associated proteins, and its reflections on memory performance were tested with behavioral tests. Local myelin damage in the hippocampus triggered severe gut dysbiosis, p < .05, changed memory performance, p < .05, and deviated emotional responses. Moreover, myelin treatment with clemastine improved gut dysbiosis and behavioral deviations. Our study provides animal-based evidence on the direct interaction between glial biology in the hippocampus and gut microbiome profile. This study proposes a framework for generating new hypotheses bridging different systems to the gut-brain axis.

Residual dynamics resolves recurrent contributions to neural computation

Relating neural activity to behavior requires an understanding of how neural computations arise from the coordinated dynamics of distributed, recurrently connected neural populations. However, inferring the nature of recurrent dynamics from partial recordings of a neural circuit presents considerable challenges. Here we show that some of these challenges can be overcome by a fine-grained analysis of the dynamics of neural residuals-that is, trial-by-trial variability around the mean neural population trajectory for a given task condition. Residual dynamics in macaque prefrontal cortex (PFC) in a saccade-based perceptual decision-making task reveals recurrent dynamics that is time dependent, but consistently stable, and suggests that pronounced rotational structure in PFC trajectories during saccades is driven by inputs from upstream areas. The properties of residual dynamics restrict the possible contributions of PFC to decision-making and saccade generation and suggest a path toward fully characterizing distributed neural computations with large-scale neural recordings and targeted causal perturbations.

Angular gyrus: an anatomical case study for association cortex

The angular gyrus is associated with a spectrum of higher order cognitive functions. This mini-review undertakes a broad survey of putative neuroanatomical substrates, guided by the premise that area-specific specializations derive from a combination of extrinsic connections and intrinsic area properties. Three levels of spatial resolution are discussed: cellular, supracellular connectivity, and synaptic micro-scale, with examples necessarily drawn mainly from experimental work with nonhuman primates. A significant factor in the functional specialization of the human parietal cortex is the pronounced enlargement. In addition to "more" cells, synapses, and connections, however, the heterogeneity itself can be considered an important property. Multiple anatomical features support the idea of overlapping and temporally dynamic membership in several brain wide subnetworks, but how these features operate in the context of higher cognitive functions remains for continued investigations.

Local networks from different parts of the human cerebral cortex generate and share the same population dynamic

A major goal of neuroscience is to reveal mechanisms supporting collaborative actions of neurons in local and larger-scale networks. However, no clear overall principle of operation has emerged despite decades-long experimental efforts. Here, we used an unbiased method to extract and identify the dynamics of local postsynaptic network states contained in the cortical field potential. Field potentials were recorded by depth electrodes targeting a wide selection of cortical regions during spontaneous activities, and sensory, motor, and cognitive experimental tasks. Despite different architectures and different activities, all local cortical networks generated the same type of dynamic confined to one region only of state space. Surprisingly, within this region, state trajectories expanded and contracted continuously during all brain activities and generated a single expansion followed by a contraction in a single trial. This behavior deviates from known attractors and attractor networks. The state-space contractions of particular subsets of brain regions cross-correlated during perceptive, motor, and cognitive tasks. Our results imply that the cortex does not need to change its dynamic to shift between different activities, making task-switching inherent in the dynamic of collective cortical operations. Our results provide a mathematically described general explanation of local and larger scale cortical dynamic.