Task-Dependent Changes in the Large-Scale Dynamics and Necessity of Cortical Regions

Multiplexed subspaces route neural activity across brain-wide networks

Cognition is flexible, allowing behavior to change on a moment-by-moment basis. Such flexibility relies on the brain's ability to route information through different networks of brain regions to perform different cognitive computations. However, the mechanisms that determine which network of regions is active are not well understood. Here, we combined cortex-wide calcium imaging with high-density electrophysiological recordings in eight cortical and subcortical regions of mice to understand the interactions between regions. We found different dimensions within the population activity of each region were functionally connected with different cortex-wide 'subspace networks' of regions. These subspace networks were multiplexed; each region was functionally connected with multiple independent, yet overlapping, subspace networks. The subspace network that was active changed from moment-to-moment. These changes were associated with changes in the geometric relationship between the neural response within a region and the subspace dimensions: when neural responses were aligned with (i.e., projected along) a subspace dimension, neural activity was increased in the associated regions. Together, our results suggest that changing the geometry of neural representations within a brain region may allow the brain to flexibly engage different brain-wide networks, thereby supporting cognitive flexibility.

Frontal noradrenergic and cholinergic transients exhibit distinct spatiotemporal dynamics during competitive decision-making

Norepinephrine (NE) and acetylcholine (ACh) are crucial for learning and decision-making. In the cortex, NE and ACh are released transiently at specific sites along neuromodulatory axons, but how the spatiotemporal patterns of NE and ACh signaling link to behavioral events is unknown. Here, we use two-photon microscopy to visualize neuromodulatory signals in the premotor cortex (medial M2) as mice engage in a competitive matching pennies game. Spatially, NE signals are more segregated with choice and outcome encoded at distinct locations, whereas ACh signals can multiplex and reflect different behavioral correlates at the same site. Temporally, task-driven NE transients were more synchronized and peaked earlier than ACh transients. To test functional relevance, we stimulated neuromodulatory signals using optogenetics to find that NE, but not ACh, increases the animals' propensity to explore alternate options. Together, the results reveal distinct subcellular spatiotemporal patterns of ACh and NE transients during decision-making in mice.

Three-photon population imaging of subcortical brain regions

Recording activity from large cell populations in deep neural circuits is essential for understanding brain function. Three-photon (3P) imaging is an emerging technology that allows for imaging of structure and function in subcortical brain structures. However, increased tissue heating, as well as the low repetition rate sources inherent to 3P imaging, have limited the fields of view (FOV) to areas of ≤0.3 mm 2 . Here we present a Large Imaging Field of view Three-photon (LIFT) microscope with a FOV of >3 mm 2 . LIFT combines high numerical aperture (NA) optimized sampling, using a custom scanning module, with deep learning-based denoising, to enable population imaging in deep brain regions. We demonstrate non-invasive calcium imaging in the mouse brain from >1500 cells across CA1, the surrounding white matter, and adjacent deep layers of the cortex, and show population imaging with high signal-to-noise in the rat cortex at a depth of 1.2 mm. The LIFT microscope was built with all off-the-shelf components and allows for a flexible choice of imaging scale and rate.

Structure of activity in multiregion recurrent neural networks

Neural circuits comprise multiple interconnected regions, each with complex dynamics. The interplay between local and global activity is thought to underlie computational flexibility, yet the structure of multiregion neural activity and its origins in synaptic connectivity remain poorly understood. We investigate recurrent neural networks with multiple regions, each containing neurons with random and structured connections. Inspired by experimental evidence of communication subspaces, we use low-rank connectivity between regions to enable selective activity routing. These networks exhibit high-dimensional fluctuations within regions and low-dimensional signal transmission between them. Using dynamical mean-field theory, with cross-region currents as order parameters, we show that regions act as both generators and transmitters of activity-roles that are often in tension. Taming within-region activity can be crucial for effective signal routing. Unlike previous models that suppressed neural activity to control signal flow, our model achieves routing by exciting different high-dimensional activity patterns through connectivity structure and nonlinear dynamics. Our analysis of this disordered system offers insights into multiregion neural data and trained neural networks.

Uncovering Dynamical Equations of Stochastic Decision Models Using Data-Driven SINDy Algorithm

Decision formation in perceptual decision making involves sensory evidence accumulation instantiated by the temporal integration of an internal decision variable toward some decision criterion or threshold, as described by sequential sampling theoretical models. The decision variable can be represented in the form of experimentally observable neural activities. Hence, elucidating the appropriate theoretical model becomes crucial to understanding the mechanisms underlying perceptual decision formation. Existing computational methods are limited to either fitting of choice behavioral data or linear model estimation from neural activity data. In this work, we made use of sparse identification of nonlinear dynamics (SINDy), a data-driven approach, to elucidate the deterministic linear and nonlinear components of often-used stochastic decision models within reaction time task paradigms. Based on the simulated decision variable activities of the models and assuming the noise coefficient term is known beforehand, SINDy, enhanced with approaches using multiple trials, could readily estimate the deterministic terms in the dynamical equations, choice accuracy, and decision time of the models across a range of signal-to-noise ratio values. In particular, SINDy performed the best using the more memory-intensive multi-trial approach while trial-averaging of parameters performed more moderately. The single-trial approach, although expectedly not performing as well, may be useful for real-time modeling. Taken together, our work offers alternative approaches for SINDy to uncover the dynamics in perceptual decision making and, more generally, for first-passage time problems.

Aging disrupts the link between network centrality and functional properties of prefrontal neurons during memory-guided behavior

The prefrontal cortex (PFC) is vital for higher cognitive functions and displays neuronal heterogeneity, with neuronal activity varying significantly across individual neurons. Using calcium imaging in the medial PFC (mPFC) of mice, we investigate whether differences in degree centrality-a measure of connectivity strength within local circuits-could explain this neuronal diversity and its functional implications. In young adults, neurons with high degree centrality, inferred from resting-state activity, exhibit reliable and stable action-plan selectivity during memory-guided tasks, suggesting that connectivity strength is closely linked to functional heterogeneity. This relationship, however, deteriorates in middle-aged and older mice. A computational model simulating age-related declines in synaptic plasticity reproduces these results. In young adults, degree centrality also predicts cross-modal action-plan selectivity, but this predictive power diminishes with age. Furthermore, neurons with high action-plan selectivity are spatially clustered, a pattern that fades with aging. These findings reveal the significant aging impact on the network properties in parallel with the functional and spatial organization of the mPFC.

Cognitive network interactions through communication subspaces in large-scale models of the neocortex

Neocortex-wide neural activity is organized into distinct networks of areas engaged in different cognitive processes. To elucidate the underlying mechanism of flexible network reconfiguration, we developed connectivity-constrained macaque and human whole-cortex models. In our model, within-area connectivity consists of a mixture of symmetric, asymmetric, and random motifs that give rise to stable (attractor) or transient (sequential) heterogeneous dynamics. Assuming sparse low-rank plus random inter-areal connectivity constrained by cognitive networks' activation maps, we show that our model captures key aspects of the cognitive networks' dynamics and interactions observed experimentally. In particular, the anti-correlation between the default mode network and the dorsal attention network. Communication between networks is shaped by the alignment of long-range communication subspaces with local connectivity motifs and is switchable in a bottom-up salience-dependent routing mechanism. Furthermore, the frontoparietal multiple-demand network displays a coexistence of stable and dynamic coding, suitable for top-down cognitive control. Our work provides a theoretical framework for understanding the dynamic routing in the cortical networks during cognition.

The impact of functional correlations on task information coding

State-dependent neural correlations can be understood from a neural coding framework. Noise correlations-trial-to-trial or moment-to-moment covariability-can be interpreted only if the underlying signal correlation-similarity of task selectivity between pairs of neural units-is known. Despite many investigations in local spiking circuits, it remains unclear how this coding framework applies to large-scale brain networks. Here, we investigated relationships between large-scale noise correlations and signal correlations in a multitask human fMRI dataset. We found that task-state noise correlation changes (e.g., functional connectivity) did not typically change in the same direction as their underlying signal correlation (e.g., tuning similarity of two regions). Crucially, noise correlations that changed in the opposite direction as their signal correlation (i.e., anti-aligned correlations) improved information coding of these brain regions. In contrast, noise correlations that changed in the same direction (aligned noise correlations) as their signal correlation did not. Interestingly, these aligned noise correlations were primarily correlation increases, suggesting that most functional correlation increases across fMRI networks actually degrade information coding. These findings illustrate that state-dependent noise correlations shape information coding of functional brain networks, with interpretation of correlation changes requiring knowledge of underlying signal correlations.

Cortex-wide characterization of decision-making neural dynamics during spatial navigation

Decision-making during freely moving behaviors involves complex interactions among many cortical and subcortical regions. However, the spatiotemporal coordination across regions to generate a decision is less understood. Using a head-mounted widefield microscope, cortex-wide calcium dynamics were recorded in mice expressing GCaMP7f as they navigated an 8-maze using two paradigms. The first was an alternating pattern that required short term memory of the previous trial to make the correct decision and the second after a rule change to a fixed path in which rewards were delivered only on the left side. Identification of cortex-wide activation states revealed differences between the two paradigms. There was a higher probability for a visual/retrosplenial cortical state during the alternating paradigm and higher probability of a secondary motor and posterior parietal state during left-only. Three state sequences (motifs) illustrated both anterior and posterior activity propagations across the cortex. The anterior propagating motifs had the highest probability around the decision and posterior propagating motifs peaked following the decision. The latter, likely reflecting internal feedback to influence future actions, were more common in the left-only paradigm. Therefore, the probabilities and sequences of cortical states differ when working memory is required versus a fixed trajectory reward paradigm.

The role of motor cortex in motor sequence execution depends on demands for flexibility

The role of the motor cortex in executing motor sequences is widely debated, with studies supporting disparate views. Here we probe the degree to which the motor cortex's engagement depends on task demands, specifically whether its role differs for highly practiced, or 'automatic', sequences versus flexible sequences informed by external cues. To test this, we trained rats to generate three-element motor sequences either by overtraining them on a single sequence or by having them follow instructive visual cues. Lesioning motor cortex showed that it is necessary for flexible cue-driven motor sequences but dispensable for single automatic behaviors trained in isolation. However, when an automatic motor sequence was practiced alongside the flexible task, it became motor cortex dependent, suggesting that an automatic motor sequence fails to consolidate subcortically when the same sequence is produced also in a flexible context. A simple neural network model recapitulated these results and offered a circuit-level explanation. Our results critically delineate the role of the motor cortex in motor sequence execution, describing the conditions under which it is engaged and the functions it fulfills, thus reconciling seemingly conflicting views about motor cortex's role in motor sequence generation.

An optical brain-machine interface reveals a causal role of posterior parietal cortex in goal-directed navigation

Cortical circuits contain diverse sensory, motor, and cognitive signals, and form densely recurrent networks. This creates challenges for identifying causal relationships between neural populations and behavior. We developed a calcium imaging-based brain-machine interface (BMI) to study the role of posterior parietal cortex (PPC) in controlling navigation in virtual reality. By training a decoder to estimate navigational heading and velocity from PPC activity during virtual navigation, we discovered that mice could immediately navigate toward goal locations when control was switched to BMI. No learning or adaptation was observed during BMI, indicating that naturally occurring PPC activity patterns are sufficient to drive navigational trajectories in real time. During successful BMI trials, decoded trajectories decoupled from the mouse's physical movements, suggesting that PPC activity relates to intended trajectories. Our work demonstrates a role for PPC in navigation and offers a BMI approach for investigating causal links between neural activity and behavior.

Simultaneous mesoscopic measurement and manipulation of mouse cortical activity

Dynamics of activity across the cerebral cortex at the mesoscopic scale - coordinated fluctuations of local populations of neurons - are essential to perception and cognition and relevant to computations like sensorimotor integration and goal-directed task engagement. However, understanding direct causal links between population dynamics and behavior requires the ability to manipulate mesoscale activity and observe the effect of manipulation across multiple brain regions simultaneously. Here, we develop a novel system enabling simultaneous recording and manipulation of activity across the dorsal cortex of awake mice, compatible with large-scale electrophysiology from any region across the brain. Transgenic mice expressing the GCaMP calcium sensor are injected systemically with an adeno-associated virus driving expression of the ChrimsonR excitatory opsin. This strategy drives expression of the blue-excited calcium indicator, GCaMP, in excitatory neurons and red-excited Chrimson opsin in inhibitory neurons. We demonstrate widefield single-photon calcium imaging and simultaneous galvo-targeted laser stimulation over the entire dorsal cortical surface. The light channels of the imaging and the opsin do not interfere. We characterize the spatial and temporal resolution of the method, which is suitable for targeting specific cortical regions and specific time windows in behavioral tasks. The preparation is stable over many months and thus well-suited for long-term behavioral experiments. This technique allows for studying the effect of cortical perturbations on cortex-wide activity, on subcortical spiking activity, and on behavior, and for designing systems to control cortical activity in closed-loop.

Neural dynamics in the orbitofrontal cortex reveal cognitive strategies

Behavior is sloppy: a multitude of cognitive strategies can produce similar behavioral read-outs. An underutilized approach is to combine multifaceted behavioral analyses with neural recordings to resolve cognitive strategies. Here we show that rats performing a decision-making task exhibit distinct strategies over training, and these cognitive strategies are decipherable from orbitofrontal cortex (OFC) neural dynamics. We trained rats to perform a temporal wagering task with hidden reward states. While naive rats passively adapted to reward statistics, expert rats inferred reward states. Electrophysiological recordings and novel methods for characterizing population dynamics identified latent neural factors that reflected inferred states in expert but not naive rats. In experts, these factors showed abrupt changes following single trials that were informative of state transitions. These dynamics were driven by neurons whose firing rates reflected single trial inferences, and OFC inactivations showed they were causal to behavior. These results reveal the neural signatures of inference.

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.

Mental state decoders: game-changers or wishful thinking?

Decoding mental and perceptual states using fMRI has become increasingly popular over the past two decades, with numerous highly-cited studies published in high-profile journals. Nevertheless, what have we learned from these decoders? In this opinion, we argue that fMRI-based decoders are not neurophysiologically informative and are not, and likely cannot be, applicable to real-world decision-making. The former point stems from the fact that decoding models cannot disentangle neural mechanisms from their epiphenomena. The latter point stems from both logical and ethical constraints. Constructing decoders requires precious time and resources that should instead be directed toward scientific endeavors more likely to yield meaningful scientific progress.

Divergent subregional information processing in mouse prefrontal cortex during working memory

Working memory (WM) is a critical cognitive function allowing recent information to be temporarily held in mind to inform future action. This process depends on coordination between prefrontal cortex (PFC) subregions and other connected brain areas. However, few studies have examined the degree of functional specialization between these subregions throughout WM using electrophysiological recordings in freely-moving mice. Here we record single-units in three neighboring mouse medial PFC (mPFC) subregions-supplementary motor area (MOs), dorsomedial PFC (dmPFC), and ventromedial (vmPFC)-during a freely-behaving non-match-to-position WM task. The MOs is most active around task phase transitions, when it transiently represents the starting sample location. Dorsomedial PFC contains a stable population code, including persistent sample-location-specific firing during the delay period. Ventromedial PFC responds most strongly to reward-related information during choices. Our results reveal subregionally segregated WM computation in mPFC and motivate more precise consideration of the dynamic neural activity required for WM.

Toward a computational role for locus coeruleus/norepinephrine arousal systems

Brain and behavior undergo measurable changes in their underlying state and neuromodulators are thought to contribute to these fluctuations. Why do we undergo such changes, and what function could the underlying neuromodulatory systems perform? Here we examine theoretical answers to these questions with respect to the locus coeruleus/norepinephrine system focusing on peripheral markers for arousal, such as pupil diameter, that are thought to provide a window into brain wide noradrenergic signaling. We explore a computational role for arousal systems in facilitating internal state transitions that facilitate credit assignment and promote accurate perceptions in non-stationary environments. We summarize recent work that supports this idea and highlight open questions as well as alternative views of how arousal affects cognition.

A computational mechanism of cue-stimulus integration for pain in the brain

The brain integrates information from pain-predictive cues and noxious inputs to construct the pain experience. Although previous studies have identified neural encodings of individual pain components, how they are integrated remains elusive. Here, using a cue-induced pain task, we examined temporal functional magnetic resonance imaging activities within the state space, where axes represent individual voxel activities. By analyzing the features of these activities at the large-scale network level, we demonstrated that overall brain networks preserve both cue and stimulus information in their respective subspaces within the state space. However, only higher-order brain networks, including limbic and default mode networks, could reconstruct the pattern of participants' reported pain by linear summation of subspace activities, providing evidence for the integration of cue and stimulus information. These results suggest a hierarchical organization of the brain for processing pain components and elucidate the mechanism for their integration underlying our pain perception.

Wide-field calcium imaging of cortical activation and functional connectivity in externally- and internally-driven locomotion

The role of the cerebral cortex in self-initiated versus sensory-driven movements is central to understanding volitional action. Whether the differences in these two movement classes are due to specific cortical areas versus more cortex-wide engagement is debated. Using wide-field Ca2+ imaging, we compared neural dynamics during spontaneous and motorized treadmill locomotion, determining the similarities and differences in cortex-wide activation and functional connectivity (FC). During motorized locomotion, the cortex exhibits greater activation globally prior to and during locomotion starting compared to spontaneous and less during steady-state walking, during stopping, and after termination. Both conditions are characterized by FC increases in anterior secondary motor cortex (M2) nodes and decreases in all other regions. There are also cortex-wide differences; most notably, M2 decreases in FC with all other nodes during motorized stopping and after termination. Therefore, both internally- and externally-generated movements widely engage the cortex, with differences represented in cortex-wide activation and FC patterns.

The Neural and Computational Architecture of Feedback Dynamics in Mouse Cortex during Stimulus Report

Conscious reportability of visual input is associated with a bimodal neural response in the primary visual cortex (V1): an early-latency response coupled to stimulus features and a late-latency response coupled to stimulus report or detection. This late wave of activity, central to major theories of consciousness, is thought to be driven by the prefrontal cortex (PFC), responsible for "igniting" it. Here we analyzed two electrophysiological studies in mice performing different stimulus detection tasks and characterized neural activity profiles in three key cortical regions: V1, posterior parietal cortex (PPC), and PFC. We then developed a minimal network model, constrained by known connectivity between these regions, reproducing the spatiotemporal propagation of visual- and report-related activity. Remarkably, while PFC was indeed necessary to generate report-related activity in V1, this occurred only through the mediation of PPC. PPC, and not PFC, had the final veto in enabling the report-related late wave of V1 activity.

Single neuron responses to perceptual difficulty in the mouse auditory cortex

Perceptual learning leads to improvement in behavioral performance, yet how the brain supports challenging perceptual demands is unknown. We used two photon imaging in the mouse primary auditory cortex during behavior in a Go-NoGo task designed to test perceptual difficulty. Using general linear model analysis, we found a subset of neurons that increased their responses during high perceptual demands. Single neurons increased their responses to both Go and NoGo sounds when mice were engaged in the more difficult perceptual discrimination. This increased responsiveness contributes to enhanced cortical network discriminability for the learned sounds. Under passive listening conditions, the same neurons responded weaker to the more similar sound pairs of the difficult task, and the training protocol by itself induced specific suppression to the learned sounds. Our findings identify how neuronal activity in auditory cortex is modulated during high perceptual demands, which is a fundamental feature associated with perceptual improvement.

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.

A multi-region recurrent circuit for evidence accumulation in rats

Decision-making based on noisy evidence requires accumulating evidence and categorizing it to form a choice. Here we evaluate a proposed feedforward and modular mapping of this process in rats: evidence accumulated in anterodorsal striatum (ADS) is categorized in prefrontal cortex (frontal orienting fields, FOF). Contrary to this, we show that both regions appear to be indistinguishable in their encoding/decoding of accumulator value and communicate this information bidirectionally. Consistent with a role for FOF in accumulation, silencing FOF to ADS projections impacted behavior throughout the accumulation period, even while nonselective FOF silencing did not. We synthesize these findings into a multi-region recurrent neural network trained with a novel approach. In-silico experiments reveal that multiple scales of recurrence in the cortico-striatal circuit rescue computation upon nonselective FOF perturbations. These results suggest that ADS and FOF accumulate evidence in a recurrent and distributed manner, yielding redundant representations and robustness to certain perturbations.

Widefield in vivo imaging system with two fluorescence and two reflectance channels, a single sCMOS detector, and shielded illumination

Significance

Widefield microscopy of the entire dorsal part of mouse cerebral cortex enables large-scale ("mesoscopic") imaging of different aspects of neuronal activity with spectrally compatible fluorescent indicators as well as hemodynamics via oxy- and deoxyhemoglobin absorption. Versatile and cost-effective imaging systems are needed for large-scale, color-multiplexed imaging of multiple fluorescent and intrinsic contrasts.

Aim

We aim to develop a system for mesoscopic imaging of two fluorescent and two reflectance channels.

Approach

Excitation of red and green fluorescence is achieved through epi-illumination. Hemoglobin absorption imaging is achieved using 525- and 625-nm light-emitting diodes positioned around the objective lens. An aluminum hemisphere placed between objective and cranial window provides diffuse illumination of the brain. Signals are recorded sequentially by a single sCMOS detector.

Results

We demonstrate the performance of our imaging system by recording large-scale spontaneous and stimulus-evoked neuronal, cholinergic, and hemodynamic activity in awake, head-fixed mice with a curved "crystal skull" window expressing the red calcium indicator jRGECO1a and the green acetylcholine sensorGRAB ACh 3.0 . Shielding of illumination light through the aluminum hemisphere enables concurrent recording of pupil diameter changes.

Conclusions

Our widefield microscope design with a single camera can be used to acquire multiple aspects of brain physiology and is compatible with behavioral readouts of pupil diameter.

Wide-field imaging in behaving mice as a tool to study cognitive function

Cognitive functions are mediated through coordinated and dynamic neuronal responses that involve many different areas across the brain. Therefore, it is of high interest to simultaneously record neuronal activity from as many brain areas as possible while the subject performs a cognitive behavioral task. One of the emerging tools to achieve a mesoscopic field of view is wide-field imaging of cortex-wide dynamics in mice. Wide-field imaging is cost-effective, user-friendly, and enables obtaining cortex-wide signals from mice performing complex and demanding cognitive tasks. Importantly, wide-field imaging offers an unbiased cortex-wide observation that sheds light on overlooked cortical regions and highlights parallel processing circuits. Recent wide-field imaging studies have shown that multi-area cortex-wide patterns, rather than just a single area, are involved in encoding cognitive functions. The optical properties of wide-field imaging enable imaging of different brain signals, such as layer-specific, inhibitory subtypes, or neuromodulation signals. Here, I review the main advantages of wide-field imaging in mice, review the recent literature, and discuss future directions of the field. It is expected that wide-field imaging in behaving mice will continue to gain popularity and aid in understanding the mesoscale dynamics underlying cognitive function.

Flexible multitask computation in recurrent networks utilizes shared dynamical motifs

Flexible computation is a hallmark of intelligent behavior. However, little is known about how neural networks contextually reconfigure for different computations. In the present work, we identified an algorithmic neural substrate for modular computation through the study of multitasking artificial recurrent neural networks. Dynamical systems analyses revealed learned computational strategies mirroring the modular subtask structure of the training task set. Dynamical motifs, which are recurring patterns of neural activity that implement specific computations through dynamics, such as attractors, decision boundaries and rotations, were reused across tasks. For example, tasks requiring memory of a continuous circular variable repurposed the same ring attractor. We showed that dynamical motifs were implemented by clusters of units when the unit activation function was restricted to be positive. Cluster lesions caused modular performance deficits. Motifs were reconfigured for fast transfer learning after an initial phase of learning. This work establishes dynamical motifs as a fundamental unit of compositional computation, intermediate between neuron and network. As whole-brain studies simultaneously record activity from multiple specialized systems, the dynamical motif framework will guide questions about specialization and generalization.

Behavioral strategy shapes activation of the Vip-Sst disinhibitory circuit in visual cortex

In complex environments, animals can adopt diverse strategies to find rewards. How distinct strategies differentially engage brain circuits is not well understood. Here, we investigate this question, focusing on the cortical Vip-Sst disinhibitory circuit between vasoactive intestinal peptide-postive (Vip) interneurons and somatostatin-positive (Sst) interneurons. We characterize the behavioral strategies used by mice during a visual change detection task. Using a dynamic logistic regression model, we find that individual mice use mixtures of a visual comparison strategy and a statistical timing strategy. Separately, mice also have periods of task engagement and disengagement. Two-photon calcium imaging shows large strategy-dependent differences in neural activity in excitatory, Sst inhibitory, and Vip inhibitory cells in response to both image changes and image omissions. In contrast, task engagement has limited effects on neural population activity. We find that the diversity of neural correlates of strategy can be understood parsimoniously as the increased activation of the Vip-Sst disinhibitory circuit during the visual comparison strategy, which facilitates task-appropriate responses.

The mouse motor system contains multiple premotor areas and partially follows human organizational principles

While humans are known to have several premotor cortical areas, secondary motor cortex (M2) is often considered to be the only higher-order motor area of the mouse brain and is thought to combine properties of various human premotor cortices. Here, we show that axonal tracer, functional connectivity, myelin mapping, gene expression, and optogenetics data contradict this notion. Our analyses reveal three premotor areas in the mouse, anterior-lateral motor cortex (ALM), anterior-lateral M2 (aM2), and posterior-medial M2 (pM2), with distinct structural, functional, and behavioral properties. By using the same techniques across mice and humans, we show that ALM has strikingly similar functional and microstructural properties to human anterior ventral premotor areas and that aM2 and pM2 amalgamate properties of human pre-SMA and cingulate cortex. These results provide evidence for the existence of multiple premotor areas in the mouse and chart a comparative map between the motor systems of humans and mice.

Simultaneous, cortex-wide dynamics of up to 1 million neurons reveal unbounded scaling of dimensionality with neuron number

The brain's remarkable properties arise from the collective activity of millions of neurons. Widespread application of dimensionality reduction to multi-neuron recordings implies that neural dynamics can be approximated by low-dimensional "latent" signals reflecting neural computations. However, can such low-dimensional representations truly explain the vast range of brain activity, and if not, what is the appropriate resolution and scale of recording to capture them? Imaging neural activity at cellular resolution and near-simultaneously across the mouse cortex, we demonstrate an unbounded scaling of dimensionality with neuron number in populations up to 1 million neurons. Although half of the neural variance is contained within sixteen dimensions correlated with behavior, our discovered scaling of dimensionality corresponds to an ever-increasing number of neuronal ensembles without immediate behavioral or sensory correlates. The activity patterns underlying these higher dimensions are fine grained and cortex wide, highlighting that large-scale, cellular-resolution recording is required to uncover the full substrates of neuronal computations.

Divergent Subregional Information Processing in Mouse Prefrontal Cortex During Working Memory

Working memory (WM) is a critical cognitive function allowing recent information to be temporarily held in mind to inform future action. This process depends on coordination between key subregions in prefrontal cortex (PFC) and other connected brain areas. However, few studies have examined the degree of functional specialization between these subregions throughout the phases of WM using electrophysiological recordings in freely-moving animals, particularly mice. To this end, we recorded single-units in three neighboring medial PFC (mPFC) subregions in mouse - supplementary motor area (MOs), dorsomedial PFC (dmPFC), and ventromedial (vmPFC) - during a freely-behaving non-match-to-position WM task. We found divergent patterns of task-related activity across the phases of WM. The MOs is most active around task phase transitions and encodes the starting sample location most selectively. Dorsomedial PFC contains a more stable population code, including persistent sample-location-specific firing during a five second delay period. Finally, the vmPFC responds most strongly to reward-related information during the choice phase. Our results reveal anatomically and temporally segregated computation of WM task information in mPFC and motivate more precise consideration of the dynamic neural activity required for WM.

Targeted micro-fiber arrays for measuring and manipulating localized multi-scale neural dynamics over large, deep brain volumes during behavior

Neural population dynamics relevant to behavior vary over multiple spatial and temporal scales across three-dimensional volumes. Current optical approaches lack the spatial coverage and resolution necessary to measure and manipulate naturally occurring patterns of large-scale, distributed dynamics within and across deep brain regions such as the striatum. We designed a new micro-fiber array approach capable of chronically measuring and optogenetically manipulating local dynamics across over 100 targeted locations simultaneously in head-fixed and freely moving mice, enabling the investigation of cell-type- and neurotransmitter-specific signals over arbitrary 3D volumes at a spatial resolution and coverage previously inaccessible. We applied this method to resolve rapid dopamine release dynamics across the striatum, revealing distinct, modality-specific spatiotemporal patterns in response to salient sensory stimuli extending over millimeters of tissue. Targeted optogenetics enabled flexible control of neural signaling on multiple spatial scales, better matching endogenous signaling patterns, and the spatial localization of behavioral function across large circuits.

Chronic brain functional ultrasound imaging in freely moving rodents performing cognitive tasks

BACKGROUND

Functional ultrasound imaging (fUS) is an emerging imaging technique that indirectly measures neural activity via changes in blood volume. Chronic fUS imaging during cognitive tasks in freely moving animals faces multiple exceptional challenges: performing large durable craniotomies with chronic implants, designing behavioral experiments matching the hemodynamic timescale, stabilizing the ultrasound probe during freely moving behavior, accurately assessing motion artifacts, and validating that the animal can perform cognitive tasks while tethered.

NEW METHOD

We provide validated solutions for those technical challenges. In addition, we present standardized step-by-step reproducible protocols, procedures, and data processing pipelines. Finally, we present proof-of-concept analysis of brain dynamics during a decision making task.

RESULTS

We obtain stable recordings from which we can robustly decode task variables from fUS data over multiple months. Moreover, we find that brain wide imaging through hemodynamic response is nonlinearly related to cognitive variables, such as task difficulty, as compared to sensory responses previously explored.

COMPARISON WITH EXISTING METHODS

Computational pipelines in fUS are nascent and we present an initial development of a full processing pathway to correct and segment fUS data.

CONCLUSIONS

Our methods provide stable imaging and analysis of behavior with fUS that will enable new experimental paradigms in understanding brain-wide dynamics in naturalistic behaviors.

Distributed and specific encoding of sensory, motor, and decision information in the mouse neocortex during goal-directed behavior

Goal-directed behaviors involve coordinated activity in many cortical areas, but whether the encoding of task variables is distributed across areas or is more specifically represented in distinct areas remains unclear. Here, we compared representations of sensory, motor, and decision information in the whisker primary somatosensory cortex, medial prefrontal cortex, and tongue-jaw primary motor cortex in mice trained to lick in response to a whisker stimulus with mice that were not taught this association. Irrespective of learning, properties of the sensory stimulus were best encoded in the sensory cortex, whereas fine movement kinematics were best represented in the motor cortex. However, movement initiation and the decision to lick in response to the whisker stimulus were represented in all three areas, with decision neurons in the medial prefrontal cortex being more selective, showing minimal sensory responses in miss trials and motor responses during spontaneous licks. Our results reconcile previous studies indicating highly specific vs. highly distributed sensorimotor processing.

Mice Recognise Mice in Neighbouring Rearing Cages and Change Their Social Behaviour

Mice are social animals that change their behaviour primarily in response to visual, olfactory, and auditory information from conspecifics. Rearing conditions such as cage size and colour are important factors influencing mouse behaviour. In recent years, transparent plastic cages have become standard breeding cages. The advantage of using a transparent cage is that the experimenter can observe the mouse from outside the cage without touching the cage. However, mice may recognise the environment outside the cage and change their behaviour. We speculated that mice housed in transparent cages might recognise mice in neighbouring cages. We used only male mice in this experiment. C57BL/6 mice were kept in transparent rearing cages with open lids, and the cage positions were maintained for 3 weeks. Subsequently, we examined how mice behaved toward cagemate mice, mice from neighbouring cages, and mice from distant cages. We compared the level of interest in mice using a social preference test. Similar to previous reports, subject mice showed a high degree of interest in unfamiliar mice from distant cages. By contrast, subject mice reacted to mice from neighbouring cages as familiar mice, similar to cagemate mice. This suggests that mice housed in transparent cages with open lids perceive the external environment and identify mice in neighbouring cages. Researchers should pay attention to the environment outside the mouse cage, especially for the social preference test.

Joint encoding of stimulus and decision in monkey primary visual cortex

We investigated whether neurons in monkey primary visual cortex (V1) exhibit mixed selectivity for sensory input and behavioral choice. Parallel multisite spiking activity was recorded from area V1 of awake monkeys performing a delayed match-to-sample task. The monkeys had to make a forced choice decision of whether the test stimulus matched the preceding sample stimulus. The population responses evoked by the test stimulus contained information about both the identity of the stimulus and with some delay but before the onset of the motor response the forthcoming choice. The results of subspace identification analysis indicate that stimulus-specific and decision-related information coexists in separate subspaces of the high-dimensional population activity, and latency considerations suggest that the decision-related information is conveyed by top-down projections.

Cell type-specific connectome predicts distributed working memory activity in the mouse brain

Recent advances in connectomics and neurophysiology make it possible to probe whole-brain mechanisms of cognition and behavior. We developed a large-scale model of the multiregional mouse brain for a cardinal cognitive function called working memory, the brain's ability to internally hold and process information without sensory input. The model is built on mesoscopic connectome data for interareal cortical connections and endowed with a macroscopic gradient of measured parvalbumin-expressing interneuron density. We found that working memory coding is distributed yet exhibits modularity; the spatial pattern of mnemonic representation is determined by long-range cell type-specific targeting and density of cell classes. Cell type-specific graph measures predict the activity patterns and a core subnetwork for memory maintenance. The model shows numerous attractor states, which are self-sustained internal states (each engaging a distinct subset of areas). This work provides a framework to interpret large-scale recordings of brain activity during cognition, while highlighting the need for cell type-specific connectomics.

Modelling and Controlling System Dynamics of the Brain: An Intersection of Machine Learning and Control Theory

The human brain, as a complex system, has long captivated multidisciplinary researchers aiming to decode its intricate structure and function. This intricate network has driven scientific pursuits to advance our understanding of cognition, behavior, and neurological disorders by delving into the complex mechanisms underlying brain function and dysfunction. Modelling brain dynamics using machine learning techniques deepens our comprehension of brain dynamics from a computational perspective. These computational models allow researchers to simulate and analyze neural interactions, facilitating the identification of dysfunctions in connectivity or activity patterns. Additionally, the trained dynamical system, serving as a surrogate model, optimizes neurostimulation strategies under the guidelines of control theory. In this chapter, we discuss the recent studies on modelling and controlling brain dynamics at the intersection of machine learning and control theory, providing a framework to understand and improve cognitive function, and treat neurological and psychiatric disorders.

Full field-of-view virtual reality goggles for mice

Visual virtual reality (VR) systems for head-fixed mice offer advantages over real-world studies for investigating the neural circuitry underlying behavior. However, current VR approaches do not fully cover the visual field of view of mice, do not stereoscopically illuminate the binocular zone, and leave the lab frame visible. To overcome these limitations, we developed iMRSIV (Miniature Rodent Stereo Illumination VR)-VR goggles for mice. Our system is compact, separately illuminates each eye for stereo vision, and provides each eye with an ∼180° field of view, thus excluding the lab frame while accommodating saccades. Mice using iMRSIV while navigating engaged in virtual behaviors more quickly than in a current monitor-based system and displayed freezing and fleeing reactions to overhead looming stimulation. Using iMRSIV with two-photon functional imaging, we found large populations of hippocampal place cells during virtual navigation, global remapping during environment changes, and unique responses of place cell ensembles to overhead looming stimulation.

Meso-Py: Dual Brain Cortical Calcium Imaging in Mice during Head-Fixed Social Stimulus Presentation

We present a cost-effective, compact foot-print, and open-source Raspberry Pi-based widefield imaging system. The compact nature allows the system to be used for close-proximity dual-brain cortical mesoscale functional-imaging to simultaneously observe activity in two head-fixed animals in a staged social touch-like interaction. We provide all schematics, code, and protocols for a rail system where head-fixed mice are brought together to a distance where the macrovibrissae of each mouse make contact. Cortical neuronal functional signals (GCaMP6s; genetically encoded Ca2+ sensor) were recorded from both mice simultaneously before, during, and after the social contact period. When the mice were together, we observed bouts of mutual whisking and cross-mouse correlated cortical activity across the cortex. Correlations were not observed in trial-shuffled mouse pairs, suggesting that correlated activity was specific to individual interactions. Whisking-related cortical signals were observed during the period where mice were together (closest contact). The effects of social stimulus presentation extend outside of regions associated with mutual touch and have global synchronizing effects on cortical activity.

Exposure to sounds during sleep impairs hippocampal sharp wave ripples and memory consolidation

Sleep is critical for the consolidation of recent experiences into long-term memories. As a key underlying neuronal mechanism, hippocampal sharp-wave ripples (SWRs) occurring during sleep define periods of hippocampal reactivation of recent experiences and have been causally linked with memory consolidation. Hippocampal SWR-dependent memory consolidation during sleep is often referred to as occurring during an "offline" state, dedicated to processing internally generated neural activity patterns rather than external stimuli. However, the brain is not fully disconnected from the environment during sleep. In particular, sounds heard during sleep are processed by a highly active auditory system which projects to brain regions in the medial temporal lobe, reflecting an anatomical pathway for sound modulation of hippocampal activity. While neural processing of salient sounds during sleep, such as those of a predator or an offspring, is evolutionarily adaptive, whether ongoing processing of environmental sounds during sleep interferes with SWR-dependent memory consolidation remains unknown. To address this question, we used a closed-loop system to deliver non-waking sound stimuli during or following SWRs in sleeping rats. We found that exposure to sounds during sleep suppressed the ripple power and reduced the rate of SWRs. Furthermore, sounds delivered during SWRs (On-SWR) suppressed ripple power significantly more than sounds delivered 2 seconds after SWRs (Off-SWR). Next, we tested the influence of sound presentation during sleep on memory consolidation. To this end, SWR-triggered sounds were applied during sleep sessions following learning of a conditioned place preference paradigm, in which rats learned a place-reward association. We found that On-SWR sound pairing during post-learning sleep induced a complete abolishment of memory retention 24 h following learning, while leaving memory retention immediately following sleep intact. In contrast, Off-SWR pairing weakened memory 24 h following learning as well as immediately following learning. Notably, On-SWR pairing induced a significantly larger impairment in memory 24 h after learning as compared to Off-SWR pairing. Together, these findings suggest that sounds heard during sleep suppress SWRs and memory consolidation, and that the magnitude of these effects are dependent on sound-SWR timing. These results suggest that exposure to environmental sounds during sleep may pose a risk for memory consolidation processes.

Functional alterations of the prefrontal circuit underlying cognitive aging in mice

Executive function is susceptible to aging. How aging impacts the circuit-level computations underlying executive function remains unclear. Using calcium imaging and optogenetic manipulation during memory-guided behavior, we show that working-memory coding and the relevant recurrent connectivity in the mouse medial prefrontal cortex (mPFC) are altered as early as middle age. Population activity in the young adult mPFC exhibits dissociable yet overlapping patterns between tactile and auditory modalities, enabling crossmodal memory coding concurrent with modality-dependent coding. In middle age, however, crossmodal coding remarkably diminishes while modality-dependent coding persists, and both types of coding decay in advanced age. Resting-state functional connectivity, especially among memory-coding neurons, decreases already in middle age, suggesting deteriorated recurrent circuits for memory maintenance. Optogenetic inactivation reveals that the middle-aged mPFC exhibits heightened vulnerability to perturbations. These findings elucidate functional alterations of the prefrontal circuit that unfold in middle age and deteriorate further as a hallmark of cognitive aging.

Widefield imaging of rapid pan-cortical voltage dynamics with an indicator evolved for one-photon microscopy

Widefield imaging with genetically encoded voltage indicators (GEVIs) is a promising approach for understanding the role of large cortical networks in the neural coding of behavior. However, the limited performance of current GEVIs restricts their deployment for single-trial imaging of rapid neuronal voltage dynamics. Here, we developed a high-throughput platform to screen for GEVIs that combine fast kinetics with high brightness, sensitivity, and photostability under widefield one-photon illumination. Rounds of directed evolution produced JEDI-1P, a green-emitting fluorescent indicator with enhanced performance across all metrics. Next, we optimized a neonatal intracerebroventricular delivery method to achieve cost-effective and wide-spread JEDI-1P expression in mice. We also developed an approach to correct optical measurements from hemodynamic and motion artifacts effectively. Finally, we achieved stable brain-wide voltage imaging and successfully tracked gamma-frequency whisker and visual stimulations in awake mice in single trials, opening the door to investigating the role of high-frequency signals in brain computations.

How 'visual' is the visual cortex? The interactions between the visual cortex and other sensory, motivational and motor systems as enabling factors for visual perception

The definition of the visual cortex is primarily based on the evidence that lesions of this area impair visual perception. However, this does not exclude that the visual cortex may process more information than of retinal origin alone, or that other brain structures contribute to vision. Indeed, research across the past decades has shown that non-visual information, such as neural activity related to reward expectation and value, locomotion, working memory and other sensory modalities, can modulate primary visual cortical responses to retinal inputs. Nevertheless, the function of this non-visual information is poorly understood. Here we review recent evidence, coming primarily from studies in rodents, arguing that non-visual and motor effects in visual cortex play a role in visual processing itself, for instance disentangling direct auditory effects on visual cortex from effects of sound-evoked orofacial movement. These findings are placed in a broader framework casting vision in terms of predictive processing under control of frontal, reward- and motor-related systems. In contrast to the prevalent notion that vision is exclusively constructed by the visual cortical system, we propose that visual percepts are generated by a larger network-the extended visual system-spanning other sensory cortices, supramodal areas and frontal systems. This article is part of the theme issue 'Decision and control processes in multisensory perception'.

Distinct Features of Interictal Activity Predict Seizure Localization and Burden in a Mouse Model of Childhood Epilepsy

The epileptic brain is distinguished by spontaneous seizures and interictal epileptiform discharges (IEDs). Basic patterns of mesoscale brain activity outside of seizures and IEDs are also frequently disrupted in the epileptic brain and likely influence disease symptoms, but are poorly understood. We aimed to quantify how interictal brain activity differs from that in healthy individuals, and identify what features of interictal activity influence seizure occurrence in a genetic mouse model of childhood epilepsy. Neural activity across the majority of the dorsal cortex was monitored with widefield Ca2+ imaging in mice of both sexes expressing a human Kcnt1 variant (Kcnt1m/m ) and wild-type controls (WT). Ca2+ signals during seizures and interictal periods were classified according to their spatiotemporal features. We identified 52 spontaneous seizures, which emerged and propagated within a consistent set of susceptible cortical areas, and were predicted by a concentration of total cortical activity within the emergence zone. Outside of seizures and IEDs, similar events were detected in Kcnt1m/m and WT mice, suggesting that the spatial structure of interictal activity is similar. However, the rate of events whose spatial profile overlapped with where seizures and IEDs emerged was increased, and the characteristic global intensity of cortical activity in individual Kcnt1m/m mice predicted their epileptic activity burden. This suggests that cortical areas with excessive interictal activity are vulnerable to seizures, but epilepsy is not an inevitable outcome. Global scaling of the intensity of cortical activity below levels found in the healthy brain may provide a natural mechanism of seizure protection.SIGNIFICANCE STATEMENT Defining the scope and structure of an epilepsy-causing gene variant's effects on mesoscale brain activity constitutes a major contribution to our understanding of how epileptic brains differ from healthy brains, and informs the development of precision epilepsy therapies. We provide a clear roadmap for measuring how severely brain activity deviates from normal, not only in pathologically active areas, but across large portions of the brain and outside of epileptic activity. This will indicate where and how activity needs to be modulated to holistically restore normal function. It also has the potential to reveal unintended off-target treatment effects and facilitate therapy optimization to deliver maximal benefit with minimal side-effect potential.

Why is everyone talking about brain state?

The rapid and coordinated propagation of neural activity across the brain provides the foundation for complex behavior and cognition. Technical advances across neuroscience subfields have advanced understanding of these dynamics, but points of convergence are often obscured by semantic differences, creating silos of subfield-specific findings. In this review we describe how a parsimonious conceptualization of brain state as the fundamental building block of whole-brain activity offers a common framework to relate findings across scales and species. We present examples of the diverse techniques commonly used to study brain states associated with physiology and higher-order cognitive processes, and discuss how integration across them will enable a more comprehensive and mechanistic characterization of the neural dynamics that are crucial to survival but are disrupted in disease.

Strategy dependent recruitment of distributed cortical circuits during spatial navigation

Spatial navigation is a complex cognitive process that involves neural computations in distributed regions of the brain. Little is known about how cortical regions are coordinated when animals navigate novel spatial environments or how that coordination changes as environments become familiar. We recorded mesoscale calcium (Ca2+) dynamics across large swathes of the dorsal cortex in mice solving the Barnes maze, a 2D spatial navigation task where mice used random, serial, and spatial search strategies to navigate to the goal. Cortical dynamics exhibited patterns of repeated calcium activity with rapid and abrupt shifts between cortical activation patterns at sub-second time scales. We used a clustering algorithm to decompose the spatial patterns of cortical calcium activity in a low dimensional state space, identifying 7 states, each corresponding to a distinct spatial pattern of cortical activation, sufficient to describe the cortical dynamics across all the mice. When mice used serial or spatial search strategies to navigate to the goal, the frontal regions of the cortex were reliably activated for prolonged durations of time (> 1s) shortly after trial initiation. These frontal cortex activation events coincided with mice approaching the edge of the maze from the center and were preceded by temporal sequences of cortical activation patterns that were distinct for serial and spatial search strategies. In serial search trials, frontal cortex activation events were preceded by activation of the posterior regions of the cortex followed by lateral activation of one hemisphere. In spatial search trials, frontal cortical events were preceded by activation of posterior regions of the cortex followed by broad activation of the lateral regions of the cortex. Our results delineated cortical components that differentiate goal- and non-goal oriented spatial navigation strategies.

Emerging Penetrating Neural Electrodes: In Pursuit of Large Scale and Longevity

Penetrating neural electrodes provide a powerful approach to decipher brain circuitry by allowing for time-resolved electrical detections of individual action potentials. This unique capability has contributed tremendously to basic and translational neuroscience, enabling both fundamental understandings of brain functions and applications of human prosthetic devices that restore crucial sensations and movements. However, conventional approaches are limited by the scarce number of available sensing channels and compromised efficacy over long-term implantations. Recording longevity and scalability have become the most sought-after improvements in emerging technologies. In this review, we discuss the technological advances in the past 5-10 years that have enabled larger-scale, more detailed, and longer-lasting recordings of neural circuits at work than ever before. We present snapshots of the latest advances in penetration electrode technology, showcase their applications in animal models and humans, and outline the underlying design principles and considerations to fuel future technological development.

Whole-brain mapping of effective connectivity by fMRI with cortex-wide patterned optogenetics

Functional magnetic resonance imaging (fMRI) with optogenetic neural manipulation is a powerful tool that enables brain-wide mapping of effective functional networks. To achieve flexible manipulation of neural excitation throughout the mouse cortex, we incorporated spatiotemporal programmable optogenetic stimuli generated by a digital micromirror device into an MRI scanner via an optical fiber bundle. This approach offered versatility in space and time in planning the photostimulation pattern, combined with in situ optical imaging and cell-type-specific or circuit-specific genetic targeting in individual mice. Brain-wide effective connectivity obtained by fMRI with optogenetic stimulation of atlas-based cortical regions is generally congruent with anatomically defined axonal tracing data but is affected by the types of anesthetics that act selectively on specific connections. fMRI combined with flexible optogenetics opens a new path to investigate dynamic changes in functional brain states in the same animal through high-throughput brain-wide effective connectivity mapping.

Influence of junction resistance on spatiotemporal dynamics and reservoir computing performance arising from an SWNT/POM 3D network formed via a scaffold template technique

For scientists in numerous fields, creating a physical device that can function like the human brain is an aspiration. It is believed that we may achieve brain-like spatiotemporal information processing by fabricating an in materio reservoir computing (RC) device because of a complex random network topology with nonlinear dynamics. One of the significant drawbacks of a two-dimensional physical reservoir system is the difficulty in controlling the network density. This work reports the use of a 3D porous template as a scaffold to fabricate a three-dimensional network of a single-walled carbon nanotube polyoxometalate nanocomposite. Although the three-dimensional system exhibits better nonlinear dynamics and spatiotemporal dynamics, and higher harmonics generation than a two-dimensional system, the results suggest a correlation between a higher number of resistive junctions and reservoir performance. We show that by increasing the spatial dimension of the device, the memory capacity improves, while the scale-free network exponent (γ) remains nearly unchanged. The three-dimensional device also displays improved performance in the well-known RC benchmark task of waveform generation. This study demonstrates the impact of an additional spatial dimension, network distribution and network density on in materio RC device performance and tries to shed some light on the reason behind such behavior.

A distributed and efficient population code of mixed selectivity neurons for flexible navigation decisions

Decision-making requires flexibility to rapidly switch one's actions in response to sensory stimuli depending on information stored in memory. We identified cortical areas and neural activity patterns underlying this flexibility during virtual navigation, where mice switched navigation toward or away from a visual cue depending on its match to a remembered cue. Optogenetics screening identified V1, posterior parietal cortex (PPC), and retrosplenial cortex (RSC) as necessary for accurate decisions. Calcium imaging revealed neurons that can mediate rapid navigation switches by encoding a mixture of a current and remembered visual cue. These mixed selectivity neurons emerged through task learning and predicted the mouse's choices by forming efficient population codes before correct, but not incorrect, choices. They were distributed across posterior cortex, even V1, and were densest in RSC and sparsest in PPC. We propose flexibility in navigation decisions arises from neurons that mix visual and memory information within a visual-parietal-retrosplenial network.