Principles of Corticocortical Communication: Proposed Schemes and Design Considerations

Orthogonal neural representations support perceptual judgements of natural stimuli

In natural behavior, observers must separate relevant information from a barrage of irrelevant information. Many studies have investigated the neural underpinnings of this ability using artificial stimuli presented on simple backgrounds. Natural viewing, however, carries a set of challenges that are inaccessible using artificial stimuli, including neural responses to background objects that are task-irrelevant. An emerging body of evidence suggests that the visual abilities of humans and animals can be modeled through the linear decoding of task-relevant information from visual cortex. This idea suggests the hypothesis that irrelevant features of a natural scene should impair performance on a visual task only if their neural representations intrude on the linear readout of the task relevant feature, as would occur if the representations of task-relevant and irrelevant features are not orthogonal in the underlying neural population. We tested this hypothesis using human psychophysics and monkey neurophysiology, in response to parametrically variable naturalistic stimuli. We demonstrate that 1) the neural representation of one feature (the position of a central object) in visual area V4 is orthogonal to those of several background features, 2) the ability of human observers to precisely judge object position was largely unaffected by task-irrelevant variation in those background features, and 3) many features of the object and the background are orthogonally represented by V4 neural responses. Our observations are consistent with the hypothesis that orthogonal neural representations can support stable perception of objects and features despite the tremendous richness of natural visual scenes.

Distinct feedforward and feedback pathways for cell-type specific attention effects

Selective attention is thought to depend on enhanced firing activity in extrastriate areas. Theories suggest that this enhancement depends on selective inter-areal communication via gamma (30-80 Hz) phase-locking. To test this, we simultaneously recorded from different cell types and cortical layers of macaque V1 and V4. We find that while V1-V4 gamma phase-locking between local field potentials increases with attention, the V1 gamma rhythm does not engage V4 excitatory-neurons, but only fast-spiking interneurons in L4 of V4. By contrast, attention enhances V4 spike-rates in both excitatory and inhibitory cells, most strongly in L2/3. The rate increase in L2/3 of V4 precedes V1 in time. These findings suggest enhanced signal transmission with attention does not depend on inter-areal gamma phase-locking and show that the endogenous gamma rhythm has cell-type- and layer-specific effects on downstream target areas. Similar findings were made in the mouse visual system, based on opto-tagging of identified interneurons.

Distributed and dynamical communication: a mechanism for flexible cortico-cortical interactions and its functional roles in visual attention

Perceptual and cognitive processing relies on flexible communication among cortical areas; however, the underlying neural mechanism remains unclear. Here we report a mechanism based on the realistic spatiotemporal dynamics of propagating wave patterns in neural population activity. Using a biophysically plausible, multiarea spiking neural circuit model, we demonstrate that these wave patterns, characterized by their rich and complex dynamics, can account for a wide variety of empirically observed neural processes. The coordinated interactions of these wave patterns give rise to distributed and dynamic communication (DDC) that enables flexible and rapid routing of neural activity across cortical areas. We elucidate how DDC unifies the previously proposed oscillation synchronization-based and subspace-based views of interareal communication, offering experimentally testable predictions that we validate through the analysis of Allen Institute Neuropixels data. Furthermore, we demonstrate that DDC can be effectively modulated during attention tasks through the interplay of neuromodulators and cortical feedback loops. This modulation process explains many neural effects of attention, underscoring the fundamental functional role of DDC in cognition.

The pulvinar as a hub of visual processing and cortical integration

The pulvinar nucleus of the thalamus is a crucial component of the visual system and plays significant roles in sensory processing and cognitive integration. The pulvinar's extensive connectivity with cortical regions allows for bidirectional communication, contributing to the integration of sensory information across the visual hierarchy. Recent findings underscore the pulvinar's involvement in attentional modulation, feature binding, and predictive coding. In this review, we highlight recent advances in clarifying the pulvinar's circuitry and function. We discuss the contributions of the pulvinar to signal modulation across the global cortical network and place these findings within theoretical frameworks of cortical processing, particularly the global neuronal workspace (GNW) theory and predictive coding.

Ramping dynamics and theta oscillations reflect dissociable signatures during rule-guided human behavior

Contextual cues and prior evidence guide human goal-directed behavior. The neurophysiological mechanisms that implement contextual priors to guide subsequent actions in the human brain remain unclear. Using intracranial electroencephalography (iEEG), we demonstrate that increasing uncertainty introduces a shift from a purely oscillatory to a mixed processing regime with an additional ramping component. Oscillatory and ramping dynamics reflect dissociable signatures, which likely differentially contribute to the encoding and transfer of different cognitive variables in a cue-guided motor task. The results support the idea that prefrontal activity encodes rules and ensuing actions in distinct coding subspaces, while theta oscillations synchronize the prefrontal-motor network, possibly to guide action execution. Collectively, our results reveal how two key features of large-scale neural population activity, namely continuous ramping dynamics and oscillatory synchrony, jointly support rule-guided human behavior.

Sensory input to cortex encoded on low-dimensional periphery-correlated subspaces

As information about the world is conveyed from the sensory periphery to central neural circuits, it mixes with complex ongoing cortical activity. How do neural populations keep track of sensory signals, separating them from noisy ongoing activity? Here, we show that sensory signals are encoded more reliably in certain low-dimensional subspaces. These coding subspaces are defined by correlations between neural activity in the primary sensory cortex and upstream sensory brain regions; the most correlated dimensions were best for decoding. We analytically show that these correlation-based coding subspaces improve, reaching optimal limits (without an ideal observer), as noise correlations between cortex and upstream regions are reduced. We show that this principle generalizes across diverse sensory stimuli in the olfactory system and the visual system of awake mice. Our results demonstrate an algorithm the cortex may use to multiplex different functions, processing sensory input in low-dimensional subspaces separate from other ongoing functions.

Visual experience induces 4-8 Hz synchrony between V1 and higher-order visual areas

Visual perceptual experience induces persistent 4-8 Hz oscillations in the mouse primary visual cortex (V1), encoding visual familiarity. Recent studies suggest that higher-order visual areas (HVAs) are functionally specialized and segregated into information streams processing distinct visual features. However, whether visual memories are processed and stored within the distinct streams is not understood. We report here that V1 and lateromedial (LM), but not V1 and anterolateral, become more phase synchronized in 4-8 Hz after the entrainment of visual stimulus that maximally induces responses in LM. Directed information analysis reveals changes in the top-down functional connectivity between V1 and HVAs. Optogenetic inactivation of LM reduces post-stimulus oscillation peaks in V1 and impairs visual discrimination behavior. Our results demonstrate that 4-8 Hz familiarity-evoked oscillations are specific for the distinct visual features and are present in the corresponding HVAs, where they may be used for the inter-areal communication with V1 during memory-related behaviors.

Early selection of task-relevant features through population gating

Brains can gracefully weed out irrelevant stimuli to guide behavior. This feat is believed to rely on a progressive selection of task-relevant stimuli across the cortical hierarchy, but the specific across-area interactions enabling stimulus selection are still unclear. Here, we propose that population gating, occurring within primary auditory cortex (A1) but controlled by top-down inputs from prelimbic region of medial prefrontal cortex (mPFC), can support across-area stimulus selection. Examining single-unit activity recorded while rats performed an auditory context-dependent task, we found that A1 encoded relevant and irrelevant stimuli along a common dimension of its neural space. Yet, the relevant stimulus encoding was enhanced along an extra dimension. In turn, mPFC encoded only the stimulus relevant to the ongoing context. To identify candidate mechanisms for stimulus selection within A1, we reverse-engineered low-rank RNNs trained on a similar task. Our analyses predicted that two context-modulated neural populations gated their preferred stimulus in opposite contexts, which we confirmed in further analyses of A1. Finally, we show in a two-region RNN how population gating within A1 could be controlled by top-down inputs from PFC, enabling flexible across-area communication despite fixed inter-areal connectivity.

Transthalamic input to higher-order cortex selectively conveys state information

Communication among different neocortical areas is largely thought to be mediated by long-range synaptic interactions between cortical neurons, with the thalamus providing only an initial relay of information from the sensory periphery. Higher-order thalamic nuclei receive strong synaptic inputs from the cortex and send robust projections back to other cortical areas, providing a distinct and potentially critical route for cortico-cortical communication. However, the relative contributions of corticocortical and thalamocortical inputs to higher-order cortical function remain unclear. Using imaging of cortical neurons and projection axon terminals in combination with optogenetic manipulations, we find that the higher-order visual thalamus of mice conveys a specialized stream of information to higher-order visual cortex. Whereas corticocortical projections from lower cortical areas convey robust visual information, higher-order thalamocortical projections convey strong behavioral state information. Together, these findings suggest a key role for higher-order thalamus in providing contextual signals that flexibly modulate sensory processing in higher-order cortex.

Inter-areal transmission of multiple neural signals through frequency-division-multiplexing communication

Inter-areal information transmission in the brain cortex relates to cognitive functions. Researches used to pay attention to activity pattern transmission, signals gating, or routing in neuronal networks. However, the underlying mechanism of simultaneous transmission of multiple neural signals in the same channel across networks remains unclear. In this work, we construct a two-layer feedforward neuronal network (sender-receiver) with each layer's intrinsic rhythms consisting of slow- (low-frequency) and fast- gamma rhythms (high-frequency), investigating how to realize simultaneous transmission of multiple signals in neuronal systems. With the aid of resonance and frequency analysis, it is shown that low- and high-frequency signals can be transmitted simultaneously in such a feedforward network through frequency division multiplexing (FDM) communication. The transmission performance is related to the local resonance, connectivity, as well as background noise. Moreover, low- and high-frequency signals can also be gated or selected with appropriate adjustments of recurrent connection strength and delay, and background noise. Our model might provide a novel insight into the underlying mechanism of complex signals communication between different cortex areas.

Self-motion perception and sequential decision-making: where are we heading?

To navigate and guide adaptive behaviour in a dynamic environment, animals must accurately estimate their own motion relative to the external world. This is a fundamentally multisensory process involving integration of visual, vestibular and kinesthetic inputs. Ideal observer models, paired with careful neurophysiological investigation, helped to reveal how visual and vestibular signals are combined to support perception of linear self-motion direction, or heading. Recent work has extended these findings by emphasizing the dimension of time, both with regard to stimulus dynamics and the trade-off between speed and accuracy. Both time and certainty-i.e. the degree of confidence in a multisensory decision-are essential to the ecological goals of the system: terminating a decision process is necessary for timely action, and predicting one's accuracy is critical for making multiple decisions in a sequence, as in navigation. Here, we summarize a leading model for multisensory decision-making, then show how the model can be extended to study confidence in heading discrimination. Lastly, we preview ongoing efforts to bridge self-motion perception and navigation per se, including closed-loop virtual reality and active self-motion. The design of unconstrained, ethologically inspired tasks, accompanied by large-scale neural recordings, raise promise for a deeper understanding of spatial perception and decision-making in the behaving animal. This article is part of the theme issue 'Decision and control processes in multisensory perception'.

Identification of interacting neural populations: methods and statistical considerations

As improved recording technologies have created new opportunities for neurophysiological investigation, emphasis has shifted from individual neurons to multiple populations that form circuits, and it has become important to provide evidence of cross-population coordinated activity. We review various methods for doing so, placing them in six major categories while avoiding technical descriptions and instead focusing on high-level motivations and concerns. Our aim is to indicate what the methods can achieve and the circumstances under which they are likely to succeed. Toward this end, we include a discussion of four cross-cutting issues: the definition of neural populations, trial-to-trial variability and Poisson-like noise, time-varying dynamics, and causality.

Specialized structure of neural population codes in parietal cortex outputs

Do cortical neurons that send axonal projections to the same target area form specialized population codes for transmitting information? We used calcium imaging in mouse posterior parietal cortex (PPC), retrograde labeling, and statistical multivariate models to address this question during a delayed match-to-sample task. We found that PPC broadcasts sensory, choice, and locomotion signals widely, but sensory information is enriched in the output to anterior cingulate cortex. Neurons projecting to the same area have elevated pairwise activity correlations. These correlations are structured as information-limiting and information-enhancing interaction networks that collectively enhance information levels. This network structure is unique to sub-populations projecting to the same target and strikingly absent in surrounding neural populations with unidentified projections. Furthermore, this structure is only present when mice make correct, but not incorrect, behavioral choices. Therefore, cortical neurons comprising an output pathway form uniquely structured population codes that enhance information transmission to guide accurate behavior.

Posterior theta activity reveals an early signal of self-face recognition

Self-related visual information, especially one's own face and name, are processed in a specific, prioritized way. However, the spatio-temporal brain dynamics of self-prioritization have remained elusive. Moreover, it has been unclear whether this prioritization is an effect of enhancement and amplification, or rather a facilitating automatization of processing self-referential information. In this EEG study, 25 married women (who changed their surnames after marriage, so that their past and present surnames could be used as stimuli) performed a detection task with faces and names from five categories: self, self from the past, friend, famous, and unknown person. The aim was to determine the temporal and spatial characteristics of early electrophysiological markers of self-referential processing. We report results of event-related component (ERP) and time-frequency analyses. In the ERPs, the earliest self-relevance effect was displayed only 300 ms after stimulus onset in the midfrontal N2, and later in the parietal P3b, independently of the stimulus type. No self-relevance effect was found on the N170 component. However, local theta power at the occipito-temporal (visual) areas and inter-regional theta phase coherence between the visual and midfrontal areas showed that self-relevance differentiation of faces began already about 100-300 ms after stimulus onset. No such early effects were found for names. The results are discussed in terms of the time-course, functional localization, stimulus-specificity, and automatization of self-prioritization.

Subspace partitioning in the human prefrontal cortex resolves cognitive interference

The human prefrontal cortex (PFC) constitutes the structural basis underlying flexible cognitive control, where mixed-selective neural populations encode multiple task features to guide subsequent behavior. The mechanisms by which the brain simultaneously encodes multiple task-relevant variables while minimizing interference from task-irrelevant features remain unknown. Leveraging intracranial recordings from the human PFC, we first demonstrate that competition between coexisting representations of past and present task variables incurs a behavioral switch cost. Our results reveal that this interference between past and present states in the PFC is resolved through coding partitioning into distinct low-dimensional neural states; thereby strongly attenuating behavioral switch costs. In sum, these findings uncover a fundamental coding mechanism that constitutes a central building block of flexible cognitive control.

Intra- and inter-brain synchrony oscillations underlying social adjustment

Humans naturally synchronize their behavior with other people. However, although it happens almost automatically, adjusting behavior and conformity to others is a complex phenomenon whose neural mechanisms are still yet to be understood entirely. The present experiment aimed to study the oscillatory synchronization mechanisms underlying automatic dyadic convergence in an EEG hyperscanning experiment. Thirty-six people performed a cooperative decision-making task where dyads had to guess the correct position of a point on a line. A reinforcement learning algorithm was used to model different aspects of the participants' behavior and their expectations of their peers. Intra- and inter-connectivity among electrode sites were assessed using inter-site phase clustering in three main frequency bands (theta, alpha, beta) using a two-level Bayesian mixed-effects modeling approach. The results showed two oscillatory synchronization dynamics related to attention and executive functions in alpha and reinforcement learning in theta. In addition, inter-brain synchrony was mainly driven by beta oscillations. This study contributes preliminary evidence on the phase-coherence mechanism underlying inter-personal behavioral adjustment.

Principles of large-scale neural interactions

What mechanisms underlie flexible inter-areal communication in the cortex? We consider four mechanisms for temporal coordination and their contributions to communication: (1) Oscillatory synchronization (communication-through-coherence); (2) communication-through-resonance; (3) non-linear integration; and (4) linear signal transmission (coherence-through-communication). We discuss major challenges for communication-through-coherence based on layer- and cell-type-specific analyses of spike phase-locking, heterogeneity of dynamics across networks and states, and computational models for selective communication. We argue that resonance and non-linear integration are viable alternative mechanisms that facilitate computation and selective communication in recurrent networks. Finally, we consider communication in relation to cortical hierarchy and critically examine the hypothesis that feedforward and feedback communication use fast (gamma) and slow (alpha/beta) frequencies, respectively. Instead, we propose that feedforward propagation of prediction errors relies on the non-linear amplification of aperiodic transients, whereas gamma and beta rhythms represent rhythmic equilibrium states that facilitate sustained and efficient information encoding and amplification of short-range feedback via resonance.

Narrowband gamma oscillations propagate and synchronize throughout the mouse thalamocortical visual system

Oscillations of neural activity permeate sensory systems. In the visual system, broadband gamma oscillations (30-80 Hz) are thought to act as a communication mechanism underlying perception. However, these oscillations show widely varying frequency and phase, providing constraints for coordinating spike timing across areas. Here, we examined Allen Brain Observatory data and performed causal experiments to show that narrowband gamma (NBG) oscillations (50-70 Hz) propagate and synchronize throughout the awake mouse visual system. Lateral geniculate nucleus (LGN) neurons fired precisely relative to NBG phase in primary visual cortex (V1) and multiple higher visual areas (HVAs). NBG neurons across areas showed a higher likelihood of functional connectivity and stronger visual responses; remarkably, NBG neurons in LGN, preferring bright (ON) versus dark (OFF), fired at distinct NBG phases aligned across the cortical hierarchy. NBG oscillations may thus serve to coordinate spike timing across brain areas and facilitate communication of distinct visual features during perception.

A local circuit-basis for spatial navigation and memory processes in hippocampal area CA1

The hippocampus is a multi-stage neural circuit that is critical for memory formation. Its distinct anatomy has long inspired theories that rely on local interactions between neurons within each subregion in order to perform serial operations important for memory encoding and storage. These local computations have received less attention in CA1 area, the primary output node of the hippocampus, where excitatory neurons are thought to be only very sparsely interconnected. However, recent findings have demonstrated the power of local circuitry in CA1, with evidence for strong functional interactions among excitatory neurons, regulation by diverse inhibitory microcircuits, and novel plasticity rules that can profoundly reshape the hippocampal ensemble code. Here we review how these properties expand the dynamical repertoire of CA1 beyond the confines of feedforward processing, and what implications they have for hippocampo-cortical functions in memory formation.

A deep generative adversarial network capturing complex spiral waves in disinhibited circuits of the cerebral cortex

BACKGROUND

In the cerebral cortex, disinhibited activity is characterized by propagating waves that spread across neural tissue. In this pathological state, a widely reported form of activity are spiral waves that travel in a circular pattern around a fixed spatial locus termed the center of mass. Spiral waves exhibit stereotypical activity and involve broad patterns of co-fluctuations, suggesting that they may be of lower complexity than healthy activity.

RESULTS

To evaluate this hypothesis, we performed dense multi-electrode recordings of cortical networks where disinhibition was induced by perfusing a pro-epileptiform solution containing 4-Aminopyridine as well as increased potassium and decreased magnesium. Spiral waves were identified based on a spatially delimited center of mass and a broad distribution of instantaneous phases across electrodes. Individual waves were decomposed into "snapshots" that captured instantaneous neural activation across the entire network. The complexity of these snapshots was examined using a measure termed the participation ratio. Contrary to our expectations, an eigenspectrum analysis of these snapshots revealed a broad distribution of eigenvalues and an increase in complexity compared to baseline networks. A deep generative adversarial network was trained to generate novel exemplars of snapshots that closely captured cortical spiral waves. These synthetic waves replicated key features of experimental data including a tight center of mass, a broad eigenvalue distribution, spatially-dependent correlations, and a high complexity. By adjusting the input to the model, new samples were generated that deviated in systematic ways from the experimental data, thus allowing the exploration of a broad range of states from healthy to pathologically disinhibited neural networks.

CONCLUSIONS

Together, results show that the complexity of population activity serves as a marker along a continuum from healthy to disinhibited brain states. The proposed generative adversarial network opens avenues for replicating the dynamics of cortical seizures and accelerating the design of optimal neurostimulation aimed at suppressing pathological brain activity.

Multiplexed Subspaces Route Neural Activity Across Brain-wide Networks

Cognition is flexible. Behaviors can change on a moment-by-moment basis. Such flexibility is thought to rely on the brain's ability to route information through different networks of brain regions in order to support different cognitive computations. However, the mechanisms that determine which network of brain regions is engaged are unknown. To address this, we combined cortex-wide calcium imaging with high-density electrophysiological recordings in eight cortical and subcortical regions of mice. Different dimensions within the population activity of each brain region were functionally connected with different cortex-wide 'subspace networks' of regions. These subspace networks were multiplexed, allowing a brain region to simultaneously interact with multiple independent, yet overlapping, networks. Alignment of neural activity within a region to a specific subspace network dimension predicted how neural activity propagated between regions. Thus, changing the geometry of the neural representation within a brain region could be a mechanism to selectively engage different brain-wide networks to support cognitive flexibility.

Neural Mechanisms That Make Perceptual Decisions Flexible

Neural mechanisms of perceptual decision making have been extensively studied in experimental settings that mimic stable environments with repeating stimuli, fixed rules, and payoffs. In contrast, we live in an ever-changing environment and have varying goals and behavioral demands. To accommodate variability, our brain flexibly adjusts decision-making processes depending on context. Here, we review a growing body of research that explores the neural mechanisms underlying this flexibility. We highlight diverse forms of context dependency in decision making implemented through a variety of neural computations. Context-dependent neural activity is observed in a distributed network of brain structures, including posterior parietal, sensory, motor, and subcortical regions, as well as the prefrontal areas classically implicated in cognitive control. We propose that investigating the distributed network underlying flexible decisions is key to advancing our understanding and discuss a path forward for experimental and theoretical investigations.

Acute head-fixed recordings in awake mice with multiple Neuropixels probes

Multi-electrode arrays such as Neuropixels probes enable electrophysiological recordings from large populations of single neurons with high temporal resolution. By using such probes, the activity from functionally interacting, yet distinct, brain regions can be measured simultaneously by inserting multiple probes into the same subject. However, the use of multiple probes in small animals such as mice requires the removal of a sizable fraction of the skull, while also minimizing tissue damage and keeping the brain stable during the recordings. Here, we describe a step-by-step process designed to facilitate reliable recordings from up to six Neuropixels probes simultaneously in awake, head-fixed mice. The procedure involves four stages: the implantation of a headframe and a removable glass coverslip, the precise positioning of the Neuropixels probes at targeted points on the brain surface, the placement of a perforated plastic imaging window and the insertion of the probes into the brain of an awake mouse. The approach provides access to multiple brain regions and has been successfully applied across hundreds of mice. The procedure has been optimized for dense recordings from the mouse visual system, but it can be adapted for alternative recording configurations to target multiple probes in other brain areas. The protocol is suitable for users with experience in stereotaxic surgery in mice.

Hemodynamic transient and functional connectivity follow structural connectivity and cell type over the brain hierarchy

The neural circuit of the brain is organized as a hierarchy of functional units with wide-ranging connections that support information flow and functional connectivity. Studies using MRI indicate a moderate coupling between structural and functional connectivity at the system level. However, how do connections of different directions (feedforward and feedback) and regions with different excitatory and inhibitory (E/I) neurons shape the hemodynamic activity and functional connectivity over the hierarchy are unknown. Here, we used functional MRI to detect optogenetic-evoked and resting-state activities over a somatosensory pathway in the mouse brain in relation to axonal projection and E/I distribution. Using a highly sensitive ultrafast imaging, we identified extensive activation in regions up to the third order of axonal projections following optogenetic excitation of the ventral posteriomedial nucleus of the thalamus. The evoked response and functional connectivity correlated with feedforward projections more than feedback projections and weakened with the hierarchy. The hemodynamic response exhibited regional and hierarchical differences, with slower and more variable responses in high-order areas and bipolar response predominantly in the contralateral cortex. Electrophysiological recordings suggest that these reflect differences in neural activity rather than neurovascular coupling. Importantly, the positive and negative parts of the hemodynamic response correlated with E/I neuronal densities, respectively. Furthermore, resting-state functional connectivity was more associated with E/I distribution, whereas stimulus-evoked effective connectivity followed structural wiring. These findings indicate that the structure-function relationship is projection-, cell-type- and hierarchy-dependent. Hemodynamic transients could reflect E/I activity and the increased complexity of hierarchical processing.

Information-processing dynamics in neural networks of macaque cerebral cortex reflect cognitive state and behavior

One of the essential functions of biological neural networks is the processing of information. This includes everything from processing sensory information to perceive the environment, up to processing motor information to interact with the environment. Due to methodological limitations, it has been historically unclear how information processing changes during different cognitive or behavioral states and to what extent information is processed within or between the network of neurons in different brain areas. In this study, we leverage recent advances in the calculation of information dynamics to explore neural-level processing within and between the frontoparietal areas AIP, F5, and M1 during a delayed grasping task performed by three macaque monkeys. While information processing was high within all areas during all cognitive and behavioral states of the task, interareal processing varied widely: During visuomotor transformation, AIP and F5 formed a reciprocally connected processing unit, while no processing was present between areas during the memory period. Movement execution was processed globally across all areas with predominance of processing in the feedback direction. Furthermore, the fine-scale network structure reconfigured at the neuron level in response to different grasping conditions, despite no differences in the overall amount of information present. These results suggest that areas dynamically form higher-order processing units according to the cognitive or behavioral demand and that the information-processing network is hierarchically organized at the neuron level, with the coarse network structure determining the behavioral state and finer changes reflecting different conditions.

Learning shapes cortical dynamics to enhance integration of relevant sensory input

Adaptive sensory behavior is thought to depend on processing in recurrent cortical circuits, but how dynamics in these circuits shapes the integration and transmission of sensory information is not well understood. Here, we study neural coding in recurrently connected networks of neurons driven by sensory input. We show analytically how information available in the network output varies with the alignment between feedforward input and the integrating modes of the circuit dynamics. In light of this theory, we analyzed neural population activity in the visual cortex of mice that learned to discriminate visual features. We found that over learning, slow patterns of network dynamics realigned to better integrate input relevant to the discrimination task. This realignment of network dynamics could be explained by changes in excitatory-inhibitory connectivity among neurons tuned to relevant features. These results suggest that learning tunes the temporal dynamics of cortical circuits to optimally integrate relevant sensory input.

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.

Unraveling the Entangled Brain: How Do We Go About It?

An impactful understanding of the brain will require entirely new approaches and unprecedented collaborative efforts. The next steps will require brain researchers to develop theoretical frameworks that allow them to tease apart dependencies and causality in complex dynamical systems, as well as the ability to maintain awe while not getting lost in the effort. The outstanding question is: How do we go about it?

Geometry of spiking patterns in early visual cortex: a topological data analytic approach

In the brain, spiking patterns live in a high-dimensional space of neurons and time. Thus, determining the intrinsic structure of this space presents a theoretical and experimental challenge. To address this challenge, we introduce a new framework for applying topological data analysis (TDA) to spike train data and use it to determine the geometry of spiking patterns in the visual cortex. Key to our approach is a parametrized family of distances based on the timing of spikes that quantifies the dissimilarity between neuronal responses. We applied TDA to visually driven single-unit and multiple single-unit spiking activity in macaque V1 and V2. TDA across timescales reveals a common geometry for spiking patterns in V1 and V2 which, among simple models, is most similar to that of a low-dimensional space endowed with Euclidean or hyperbolic geometry with modest curvature. Remarkably, the inferred geometry depends on timescale and is clearest for the timescales that are important for encoding contrast, orientation and spatial correlations.

Multiregion neuronal activity: the forest and the trees

The past decade has witnessed remarkable advances in the simultaneous measurement of neuronal activity across many brain regions, enabling fundamentally new explorations of the brain-spanning cellular dynamics that underlie sensation, cognition and action. These recently developed multiregion recording techniques have provided many experimental opportunities, but thoughtful consideration of methodological trade-offs is necessary, especially regarding field of view, temporal acquisition rate and ability to guarantee cellular resolution. When applied in concert with modern optogenetic and computational tools, multiregion recording has already made possible fundamental biological discoveries - in part via the unprecedented ability to perform unbiased neural activity screens for principles of brain function, spanning dozens of brain areas and from local to global scales.

Disentangling the flow of signals between populations of neurons

Technological advances now allow us to record from large populations of neurons across multiple brain areas. These recordings may illuminate how communication between areas contributes to brain function, yet a substantial barrier remains: how do we disentangle the concurrent, bidirectional flow of signals between populations of neurons? We propose here a dimensionality reduction framework, delayed latents across groups (DLAG), that disentangles signals relayed in each direction, identifies how these signals are represented by each population and characterizes how they evolve within and across trials. We demonstrate that DLAG performs well on synthetic datasets similar in scale to current neurophysiological recordings. Then we study simultaneously recorded populations in primate visual areas V1 and V2, where DLAG reveals signatures of bidirectional yet selective communication. Our framework lays a foundation for dissecting the intricate flow of signals across populations of neurons, and how this signalling contributes to cortical computation.

Theory of the Multiregional Neocortex: Large-Scale Neural Dynamics and Distributed Cognition

The neocortex is a complex neurobiological system with many interacting regions. How these regions work together to subserve flexible behavior and cognition has become increasingly amenable to rigorous research. Here, I review recent experimental and theoretical work on the modus operandi of a multiregional cortex. These studies revealed several general principles for the neocortical interareal connectivity, low-dimensional macroscopic gradients of biological properties across cortical areas, and a hierarchy of timescales for information processing. Theoretical work suggests testable predictions regarding differential excitation and inhibition along feedforward and feedback pathways in the cortical hierarchy. Furthermore, modeling of distributed working memory and simple decision-making has given rise to a novel mathematical concept, dubbed bifurcation in space, that potentially explains how different cortical areas, with a canonical circuit organization but gradients of biological heterogeneities, are able to subserve their respective (e.g., sensory coding versus executive control) functions in a modularly organized brain.

The structures and functions of correlations in neural population codes

The collective activity of a population of neurons, beyond the properties of individual cells, is crucial for many brain functions. A fundamental question is how activity correlations between neurons affect how neural populations process information. Over the past 30 years, major progress has been made on how the levels and structures of correlations shape the encoding of information in population codes. Correlations influence population coding through the organization of pairwise-activity correlations with respect to the similarity of tuning of individual neurons, by their stimulus modulation and by the presence of higher-order correlations. Recent work has shown that correlations also profoundly shape other important functions performed by neural populations, including generating codes across multiple timescales and facilitating information transmission to, and readout by, downstream brain areas to guide behaviour. Here, we review this recent work and discuss how the structures of correlations can have opposite effects on the different functions of neural populations, thus creating trade-offs and constraints for the structure-function relationships of population codes. Further, we present ideas on how to combine large-scale simultaneous recordings of neural populations, computational models, analyses of behaviour, optogenetics and anatomy to unravel how the structures of correlations might be optimized to serve multiple functions.

Dynamic causal communication channels between neocortical areas

Processing of sensory information depends on the interactions between hierarchically connected neocortical regions, but it remains unclear how the activity in one area causally influences the activity dynamics in another and how rapidly such interactions change with time. Here, we show that the communication between the primary visual cortex (V1) and high-order visual area LM is context-dependent and surprisingly dynamic over time. By momentarily silencing one area while recording activity in the other, we find that both areas reliably affected changing subpopulations of target neurons within one hundred milliseconds while mice observed a visual stimulus. The influence of LM feedback on V1 responses became even more dynamic when the visual stimuli predicted a reward, causing fast changes in the geometry of V1 population activity and affecting stimulus coding in a context-dependent manner. Therefore, the functional interactions between cortical areas are not static but unfold through rapidly shifting communication subspaces whose dynamics depend on context when processing sensory information.

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Optogenetic perturbations reveal the causal structure of long-range cortical influences

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How visual areas influence each other changes dynamically over tens of milliseconds

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Feedback to V1 improves visual stimulus encoding required for behavior

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The dynamics of feedback influences depend on the behavioral context

Feedforward and feedback interactions between visual cortical areas use different population activity patterns

Brain function relies on the coordination of activity across multiple, recurrently connected brain areas. For instance, sensory information encoded in early sensory areas is relayed to, and further processed by, higher cortical areas and then fed back. However, the way in which feedforward and feedback signaling interact with one another is incompletely understood. Here we investigate this question by leveraging simultaneous neuronal population recordings in early and midlevel visual areas (V1-V2 and V1-V4). Using a dimensionality reduction approach, we find that population interactions are feedforward-dominated shortly after stimulus onset and feedback-dominated during spontaneous activity. The population activity patterns most correlated across areas were distinct during feedforward- and feedback-dominated periods. These results suggest that feedforward and feedback signaling rely on separate "channels", which allows feedback signals to not directly affect activity that is fed forward.

Multi-regional module-based signal transmission in mouse visual cortex

The visual cortex is hierarchically organized, yet the presence of extensive recurrent and parallel pathways make it challenging to decipher how signals flow between neuronal populations. Here, we tracked the flow of spiking activity recorded from six interconnected levels of the mouse visual hierarchy. By analyzing leading and lagging spike-timing relationships among pairs of simultaneously recorded neurons, we created a cellular-scale directed network graph. Using a module-detection algorithm to cluster neurons based on shared connectivity patterns, we uncovered two multi-regional communication modules distributed across the hierarchy. The direction of signal flow both between and within these modules, differences in layer and area distributions, and distinct temporal dynamics suggest that one module transmits feedforward sensory signals, whereas the other integrates inputs for recurrent processing. These results suggest that multi-regional functional modules may be a fundamental feature of organization beyond cortical areas that supports signal propagation across hierarchical recurrent networks.

Information Exchange between Cortical Areas: The Visual System as a Model

As nearly all brain functions, perception, motion, and higher-order cognitive functions require coordinated neural information processing within distributed cortical networks. Over the past decades, new theories and techniques emerged that advanced our understanding of how information is transferred between cortical areas. This review surveys critical aspects of interareal information exchange. We begin by examining the brain’s structural connectivity, which provides the basic framework for interareal communication. We then illustrate information exchange between cortical areas using the visual system as an example. Next, well-studied and newly proposed theories that may underlie principles of neural communication are reviewed, highlighting recent work that offers new perspectives on interareal information exchange. We finally discuss open questions in the study of the neural mechanisms underlying interareal information exchange.

Cellular components of the idiopathic epiretinal membrane

Idiopathic epiretinal membrane (iERM) is a fibrocellular proliferation on the inner surface of the retina, which leads to decreased visual acuity and even central visual loss. As iERM is associated to advanced age and posterior vitreous detachment, a higher prevalence is expected with increasing life expectancy and aging of the global population. Although various cell types of retinal and extra-retinal origin have been described in iERMs (Müller glial cells, astrocytes, hyalocytes, retinal pigment epithelium cells, myofibroblasts, and fibroblasts), myofibroblasts have a central role in collagen production and contractile activity. Thus, myofibroblast differentiation is considered a key event for the iERM formation and progression, and fibroblasts, Müller glial cells, hyalocytes, and retinal pigment epithelium have been identified as myofibroblast precursors. On the other side, the different cell types synthesize growth factors, cytokines, and extracellular matrix, which have a crucial role in ERM pathogenesis. In the present review, the major cellular components and their functions are summarized, and their possible roles in the iERM formation are discussed. By exploring in detail the cellular and molecular aspects of iERM, we seek to contribute for better understanding of this fibrotic disease and the origin of myofibroblasts, which may eventually drive to more targeted therapeutic approaches.

Collapse of complexity of brain and body activity due to excessive inhibition and MeCP2 disruption

Complex body movements require complex dynamics and coordination among neurons in motor cortex. Conversely, a long-standing theoretical notion supposes that if many neurons in motor cortex become excessively synchronized, they may lack the necessary complexity for healthy motor coding. However, direct experimental support for this idea is rare and underlying mechanisms are unclear. Here we recorded three-dimensional body movements and spiking activity of many single neurons in motor cortex of rats with enhanced synaptic inhibition and a transgenic rat model of Rett syndrome (RTT). For both cases, we found a collapse of complexity in the motor system. Reduced complexity was apparent in lower-dimensional, stereotyped brain-body interactions, neural synchrony, and simpler behavior. Our results demonstrate how imbalanced inhibition can cause excessive synchrony among movement-related neurons and, consequently, a stereotyped motor code. Excessive inhibition and synchrony may underlie abnormal motor function in RTT.

Probing mechanisms of visual spatial attention in mice

The role of spatial attention for visual perception has been thoroughly studied in primates, but less so in mice. Several behavioral tasks in mice reveal spatial attentional effects, with similarities to observations in primates. Pairing these tasks with large-scale, cell-type-specific techniques could enable deeper access to underlying mechanisms, and help define the utility and limitations of resolving attentional effects on visual perception and neural activity in mice. In this Review, we evaluate behavioral and neural evidence for visual spatial attention in mice; assess how specializations of the mouse visual system and behavioral repertoire impact interpretation of spatial attentional effects; and outline how several measurement and manipulation techniques in mice could precisely test and refine models of attentional modulation across scales.

Two views on the cognitive brain

Cognition can be defined as computation over meaningful representations in the brain to produce adaptive behaviour. There are two views on the relationship between cognition and the brain that are largely implicit in the literature. The Sherringtonian view seeks to explain cognition as the result of operations on signals performed at nodes in a network and passed between them that are implemented by specific neurons and their connections in circuits in the brain. The contrasting Hopfieldian view explains cognition as the result of transformations between or movement within representational spaces that are implemented by neural populations. Thus, the Hopfieldian view relegates details regarding the identity of and connections between specific neurons to the status of secondary explainers. Only the Hopfieldian approach has the representational and computational resources needed to develop novel neurofunctional objects that can serve as primary explainers of cognition.

Differential Contributions of Inhibitory Subnetwork to Visual Cortical Modulations Identified via Computational Model of Working Memory

Extrastriate visual neurons show no firing rate change during a working memory (WM) task in the absence of sensory input, but both αβ oscillations and spike phase locking are enhanced, as is the gain of sensory responses. This lack of change in firing rate is at odds with many models of WM, or attentional modulation of sensory networks. In this article we devised a computational model in which this constellation of results can be accounted for via selective activation of inhibitory subnetworks by a top-down working memory signal. We confirmed the model prediction of selective inhibitory activation by segmenting cells in the experimental neural data into putative excitatory and inhibitory cells. We further found that this inhibitory activation plays a dual role in influencing excitatory cells: it both modulates the inhibitory tone of the network, which underlies the enhanced sensory gain, and also produces strong spike-phase entrainment to emergent network oscillations. Using a phase oscillator model we were able to show that inhibitory tone is principally modulated through inhibitory network gain saturation, while the phase-dependent efficacy of inhibitory currents drives the phase locking modulation. The dual contributions of the inhibitory subnetwork to oscillatory and non-oscillatory modulations of neural activity provides two distinct ways for WM to recruit sensory areas, and has relevance to theories of cortical communication.

Measurement, manipulation and modeling of brain-wide neural population dynamics

Neural recording technologies increasingly enable simultaneous measurement of neural activity from multiple brain areas. To gain insight into distributed neural computations, a commensurate advance in experimental and analytical methods is necessary. We discuss two opportunities towards this end: the manipulation and modeling of neural population dynamics.

Statistical methods for dissecting interactions between brain areas

The brain is composed of many functionally distinct areas. This organization supports distributed processing, and requires the coordination of signals across areas. Our understanding of how populations of neurons in different areas interact with each other is still in its infancy. As the availability of recordings from large populations of neurons across multiple brain areas increases, so does the need for statistical methods that are well suited for dissecting and interrogating these recordings. Here we review multivariate statistical methods that have been, or could be, applied to this class of recordings. By leveraging population responses, these methods can provide a rich description of inter-areal interactions. At the same time, these methods can introduce interpretational challenges. We thus conclude by discussing how to interpret the outputs of these methods to further our understanding of inter-areal interactions.

Slow Drift of Neural Activity as a Signature of Impulsivity in Macaque Visual and Prefrontal Cortex

An animal's decision depends not only on incoming sensory evidence but also on its fluctuating internal state. This state embodies multiple cognitive factors, such as arousal and fatigue, but it is unclear how these factors influence the neural processes that encode sensory stimuli and form a decision. We discovered that, unprompted by task conditions, animals slowly shifted their likelihood of detecting stimulus changes over the timescale of tens of minutes. Neural population activity from visual area V4, as well as from prefrontal cortex, slowly drifted together with these behavioral fluctuations. We found that this slow drift, rather than altering the encoding of the sensory stimulus, acted as an impulsivity signal, overriding sensory evidence to dictate the final decision. Overall, this work uncovers an internal state embedded in population activity across multiple brain areas and sheds further light on how internal states contribute to the decision-making process.