Robust neuronal dynamics in premotor cortex during motor planning

Neural Dynamics in Extrastriate Cortex Underlying False Alarms

The unfolding of neural population activity can be described as a dynamical system. Stability in the latent dynamics that characterize neural population activity has been linked with consistency in animal behavior, such as motor control or value-based decision-making. However, whether such characteristics of neural dynamics can explain visual perceptual behavior is not well understood. To study this, we recorded V4 populations in two male monkeys engaged in a non-match-to-sample visual change-detection task that required sustained engagement. We measured how the stability in the latent dynamics in V4 might affect monkeys' perceptual behavior. Specifically, we reasoned that unstable sensory neural activity around dynamic attractor boundaries may make animals susceptible to taking incorrect actions when withholding action would have been correct ("false alarms"). We made three key discoveries: (1) greater stability was associated with longer trial sequences; (2) false alarm rate decreased (and response times slowed) when neural dynamics were more stable; and (3) low stability predicted false alarms on a single-trial level, and this relationship depended on the position of the neural activity within the state space, consistent with the latent neural state approaching an attractor boundary. Our results suggest the same outward false alarm behavior can be attributed to two different potential strategies that can be disambiguated by examining neural stability: (1) premeditated false alarms that might lead to greater stability in population dynamics and faster response time and (2) false alarms due to unstable sensory activity consistent with misperception.

Multiplexing of cognitive encoding by oculomotor networks leads to incidental gaze shifts

Humans and other animals are adept at learning to perform cognitively demanding behavioral tasks. Neurophysiological recordings in nonhuman primates during such tasks find that the requisite cognitive variables are encoded strongly in core oculomotor brain regions. Here, we assembled a large dataset-11 monkeys performing an abstract visual categorization task, surveyed across more than 1,000 neural recording sessions-to reveal that this produces a robust but uninstructed behavioral "tell," observed in all subjects and experiments: small, cognitively modulated eye movements. We find that these eye movements are causally linked to activity in SC but not LIP, and that they occur following transient alignment of cognitive and saccadic population coding subspaces in SC. This behavioral signature of oculomotor engagement is absent during a similar task that does not require rule-based categorization, suggesting that abstract task behaviors recruit primate oculomotor networks more strongly than previously understood.

Continuous sensorimotor transformation enhances robustness of neural dynamics to perturbation in macaque motor cortex

Neural activity in the motor cortex evolves dynamically to prepare and generate movement. Here, we investigate how motor cortical dynamics adapt to dynamic environments and whether these adaptations influence robustness against disruptions. We apply intracortical microstimulation (ICMS) in the motor cortex of monkeys performing delayed center-out reaches to either a static target (static) or a rotating target (moving) that required interception. While ICMS prolongs reaction times (RTs) in the static condition, it does not increase RTs in the moving condition, correlating with faster recovery of neural population activity post-perturbation. Neural dynamics suggests that the moving condition involves ongoing sensorimotor transformations during the delay period, whereas motor planning in the static condition is completed shortly. A neural network model shows that continuous feedback input rapidly corrects perturbation-induced errors in the moving condition. We conclude that continuous sensorimotor transformations enhance the motor cortex's resilience to perturbations, facilitating timely movement execution.

Chaotic recurrent neural networks for brain modelling: A review

Even in the absence of external stimuli, the brain is spontaneously active. Indeed, most cortical activity is internally generated by recurrence. Both theoretical and experimental studies suggest that chaotic dynamics characterize this spontaneous activity. While the precise function of brain chaotic activity is still puzzling, we know that chaos confers many advantages. From a computational perspective, chaos enhances the complexity of network dynamics. From a behavioural point of view, chaotic activity could generate the variability required for exploration. Furthermore, information storage and transfer are maximized at the critical border between order and chaos. Despite these benefits, many computational brain models avoid incorporating spontaneous chaotic activity due to the challenges it poses for learning algorithms. In recent years, however, multiple approaches have been proposed to overcome this limitation. As a result, many different algorithms have been developed, initially within the reservoir computing paradigm. Over time, the field has evolved to increase the biological plausibility and performance of the algorithms, sometimes going beyond the reservoir computing framework. In this review article, we examine the computational benefits of chaos and the unique properties of chaotic recurrent neural networks, with a particular focus on those typically utilized in reservoir computing. We also provide a detailed analysis of the algorithms designed to train chaotic RNNs, tracing their historical evolution and highlighting key milestones in their development. Finally, we explore the applications and limitations of chaotic RNNs for brain modelling, consider their potential broader impacts beyond neuroscience, and outline promising directions for future research.

Neural dynamics of reversal learning in the prefrontal cortex and recurrent neural networks

In probabilistic reversal learning, the choice option yielding reward with higher probability switches at a random trial. To perform optimally in this task, one has to accumulate evidence across trials to infer the probability that a reversal has occurred. We investigated how this reversal probability is represented in cortical neurons by analyzing the neural activity in the prefrontal cortex of monkeys and recurrent neural networks trained on the task. We found that in a neural subspace encoding reversal probability, its activity represented integration of reward outcomes as in a line attractor model. The reversal probability activity at the start of a trial was stationary, stable and consistent with the attractor dynamics. However, during the trial, the activity was associated with task-related behavior and became non-stationary, thus deviating from the line attractor. Fitting a predictive model to neural data showed that the stationary state at the trial start serves as an initial condition for launching the non-stationary activity. This suggested an extension of the line attractor model with behavior-induced non-stationary dynamics. The non-stationary trajectories were separable indicating that they can represent distinct probabilistic values. Perturbing the reversal probability activity in the recurrent neural networks biased choice outcomes demonstrating its functional significance. In sum, our results show that cortical networks encode reversal probability in stable stationary state at the start of a trial and utilize it to initiate non-stationary dynamics that accommodates task-related behavior while maintaining the reversal information.

Individual variability of neural computations underlying flexible decisions

The ability to flexibly switch our responses to external stimuli according to contextual information is critical for successful interactions with a complex world. Context-dependent computations are necessary across many domains1-3, yet their neural implementations remain poorly understood. Here we developed a novel behavioural task in rats to study context-dependent selection and accumulation of evidence for decision-making4-6. Under assumptions supported by both monkey and rat data, we first show mathematically that this computation can be supported by three dynamical solutions and that all networks performing the task implement a combination of these solutions. These solutions can be identified and tested directly with experimental data. We further show that existing electrophysiological and modelling data are compatible with the full variety of possible combinations of these solutions, suggesting that different individuals could use different combinations. To study variability across individual subjects, we developed automated, high-throughput methods to train rats on our task and trained many subjects using these methods. Consistent with theoretical predictions, neural and behavioural analyses revealed substantial heterogeneity across rats, despite uniformly good task performance. Our theory further predicts a specific link between behavioural and neural signatures, which was robustly supported in the data. In summary, our results provide an experimentally supported theoretical framework to analyse individual variability in biological and artificial systems that perform flexible decision-making tasks, open the door to cellular-resolution studies of individual variability in higher cognition, and provide insights into neural mechanisms of context-dependent computation more generally.

Separating cognitive and motor processes in the behaving mouse

The cognitive processes supporting complex animal behavior are closely associated with movements responsible for critical processes, such as facial expressions or the active sampling of our environments. These movements are strongly related to neural activity across much of the brain and are often highly correlated with ongoing cognitive processes. A fundamental issue for understanding the neural signatures of cognition and movements is whether cognitive processes are separable from related movements or if they are driven by common neural mechanisms. Here we demonstrate how the separability of cognitive and motor processes can be assessed and, when separable, how the neural dynamics associated with each component can be isolated. We designed a behavioral task in mice that involves multiple cognitive processes, and we show that dynamics commonly taken to support cognitive processes are strongly contaminated by movements. When cognitive and motor components are isolated using a novel approach for subspace decomposition, we find that they exhibit distinct dynamical trajectories and are encoded by largely separate populations of cells. Accurately isolating dynamics associated with particular cognitive and motor processes will be essential for developing conceptual and computational models of neural circuit function.

The information bottleneck as a principle underlying multi-area cortical representations during decision-making

Decision-making emerges from distributed computations across multiple brain areas, but it is unclear why the brain distributes the computation. In deep learning, artificial neural networks use multiple areas (or layers) and form optimal representations of task inputs. These optimal representations are sufficient to perform the task well, but minimal so they are invariant to other irrelevant variables. We recorded single neurons and multiunits in dorsolateral prefrontal cortex (DLPFC) and dorsal premotor cortex (PMd) in monkeys during a perceptual decision-making task. We found that while DLPFC represents task-related inputs required to compute the choice, the downstream PMd contains a minimal sufficient, or optimal, representation of the choice. To identify a mechanism for how cortex may form these optimal representations, we trained a multi-area recurrent neural network (RNN) to perform the task. Remarkably, DLPFC and PMd resembling representations emerged in the early and late areas of the multi-area RNN, respectively. The DLPFC-resembling area partially orthogonalized choice information and task inputs and this choice information was preferentially propagated to downstream areas through selective alignment with inter-area connections, while remaining task information was not. Our results suggest that cortex uses multi-area computation to form minimal sufficient representations by preferential propagation of relevant information between areas.

Ramping dynamics in the frontal cortex unfold over multiple timescales during motor planning

Plans are formulated and refined throughout the period leading up to their execution, ensuring that the appropriate behaviors are enacted at the appropriate times. Although existing evidence suggests that memory circuits convey the passage of time through diverse neuronal responses, it remains unclear whether the neural circuits involved in planning exhibit analogous temporal dynamics. Using publicly available data, we analyzed how activity in the mouse frontal motor cortex evolves during motor planning. Individual neurons exhibited diverse ramping activity throughout a delay interval that preceded a planned movement. The collective activity of these neurons was useful for making temporal predictions that became increasingly precise as the movement time approached. This temporal diversity gave rise to a spectrum of encoding patterns, ranging from stable to dynamic representations of the upcoming movement. Our results indicate that ramping activity unfolds over multiple timescales during motor planning, suggesting a shared mechanism in the brain for processing temporal information related to both memories from the past and plans for the future. NEW & NOTEWORTHY Neuronal responses in the cortex are diverse, but the nature and functional consequences of this diversity remain ambiguous. We identified a specific pattern of temporal heterogeneity in the mouse frontal motor cortex, whereby the firing of different neurons ramps up at varying speeds before the execution of a movement. Our decoding analyses reveal that this heterogeneity in ramping dynamics enables precise and reliable encoding of movement plans and time across various timescales.

Secondary motor cortex tracks decision value during the learning of a non-instructed task

Optimal decision-making depends on interconnected frontal brain regions, enabling animals to adapt decisions based on internal states, experiences, and contexts. The secondary motor cortex (M2) is key in adaptive behaviors in expert rodents, particularly in encoding decision values guiding complex probabilistic tasks. However, its role in deterministic tasks during initial learning remains uncertain. Here, we describe a self-initiated deterministic task requiring mice to use their forepaws to make choices without guiding cues. Our findings reveal that spontaneous decisions follow a "race" model between actions, which uncovers underlying decision values. We use in vivo microscopy and modeling to show that M2 neurons in male mice exhibit persistent activity-encoding decision values that predict action-selection probabilities. Optogenetic inhibition of the M2 reduces the reversal performance and alters the decision value. Additionally, updates in decision values determine the rate at which learning is reversed. These results highlight the use of decision values by the M2 to adapt choice during initial learning without instructive cues.

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.

Redundant, weakly connected prefrontal hemispheres balance precision and capacity in spatial working memory

How the prefrontal hemispheres coordinate to adapt to spatial working memory (WM) demands remains an open question. Recently, two models have been proposed: A specialized model, where each hemisphere governs contralateral behavior, and a redundant model, where both hemispheres equally guide behavior in the full visual space. To explore these alternatives, we analyzed simultaneous bilateral prefrontal cortex recordings from three macaque monkeys performing a visuo-spatial WM task. Each hemisphere represented targets across the full visual field and equally predicted behavioral imprecisions. Furthermore, memory errors were weakly correlated between hemispheres, suggesting that redundant, weakly coupled prefrontal hemispheres support spatial WM. Attractor model simulations showed that the hemispheric redundancy improved precision in simple tasks, whereas weak inter-hemispheric coupling allowed for specialized hemispheres in complex tasks. This interhemispheric architecture reconciles previous findings thought to support distinct models into a unified architecture, providing a versatile interhemispheric architecture that adapts to varying cognitive demands.

Robustness of working memory to prefrontal cortex microstimulation

Delay period activity in the dorso-lateral prefrontal cortex (dlPFC) has been linked to the maintenance and control of sensory information in working memory. The stability of working memory related signals found in such delay period activity is believed to support robust memory-guided behavior during sensory perturbations, such as distractors. Here, we directly probed dlPFC's delay period activity with a diverse set of activity perturbations, and measured their consequences on neural activity and behavior. We applied patterned microstimulation to the dlPFC of monkeys implanted with multi-electrode arrays by electrically stimulating different electrodes in the array while the monkeys performed a memory-guided saccade task. We found that the microstimulation perturbations affected spatial working memory-related signals in individual dlPFC neurons. However, task performance remained largely unaffected. These apparently contradictory observations could be understood by examining different dimensions of the dlPFC population activity. In dimensions where working memory related signals naturally evolved over time, microstimulation impacted neural activity. In contrast, in dimensions containing working memory related signals that were stable over time, microstimulation minimally impacted neural activity. This dissociation explained how working memory-related information could be stably maintained in dlPFC despite the activity changes induced by microstimulation. Thus, working memory processes are robust to a variety of activity perturbations in the dlPFC.

A combinatorial neural code for long-term motor memory

Motor skill repertoire can be stably retained over long periods, but the neural mechanism that underlies stable memory storage remains poorly understood1-8. Moreover, it is unknown how existing motor memories are maintained as new motor skills are continuously acquired. Here we tracked neural representation of learned actions throughout a significant portion of the lifespan of a mouse and show that learned actions are stably retained in combination with context, which protects existing memories from erasure during new motor learning. We established a continual learning paradigm in which mice learned to perform directional licking in different task contexts while we tracked motor cortex activity for up to six months using two-photon imaging. Within the same task context, activity driving directional licking was stable over time with little representational drift. When learning new task contexts, new preparatory activity emerged to drive the same licking actions. Learning created parallel new motor memories instead of modifying existing representations. Re-learning to make the same actions in the previous task context re-activated the previous preparatory activity, even months later. Continual learning of new task contexts kept creating new preparatory activity patterns. Context-specific memories, as we observed in the motor system, may provide a solution for stable memory storage throughout continual learning.

Intermittent rate coding and cue-specific ensembles support working memory

Persistent, memorandum-specific neuronal spiking activity has long been hypothesized to underlie working memory1,2. However, emerging evidence suggests a potential role for 'activity-silent' synaptic mechanisms3-5. This issue remains controversial because evidence for either view has largely relied either on datasets that fail to capture single-trial population dynamics or on indirect measures of neuronal spiking. We addressed this controversy by examining the dynamics of mnemonic information on single trials obtained from large, local populations of lateral prefrontal neurons recorded simultaneously in monkeys performing a working memory task. Here we show that mnemonic information does not persist in the spiking activity of neuronal populations during memory delays, but instead alternates between coordinated 'On' and 'Off' states. At the level of single neurons, Off states are driven by both a loss of selectivity for memoranda and a return of firing rates to spontaneous levels. Further exploiting the large-scale recordings used here, we show that mnemonic information is available in the patterns of functional connections among neuronal ensembles during Off states. Our results suggest that intermittent periods of memorandum-specific spiking coexist with synaptic mechanisms to support working memory.

Cortical neuroprosthesis-mediated functional ipsilateral control of locomotion in rats with spinal cord hemisection

Control of voluntary limb movement is predominantly attributed to the contralateral motor cortex. However, increasing evidence suggests the involvement of ipsilateral cortical networks in this process, especially in motor tasks requiring bilateral coordination, such as locomotion. In this study, we combined a unilateral thoracic spinal cord injury (SCI) with a cortical neuroprosthetic approach to investigate the functional role of the ipsilateral motor cortex in rat movement through spared contralesional pathways. Our findings reveal that in all SCI rats, stimulation of the ipsilesional motor cortex promoted a bilateral synergy. This synergy involved the elevation of the contralateral foot along with ipsilateral hindlimb extension. Additionally, in two out of seven animals, stimulation of a sub-region of the hindlimb motor cortex modulated ipsilateral hindlimb flexion. Importantly, ipsilateral cortical stimulation delivered after SCI immediately alleviated multiple locomotor and postural deficits, and this effect persisted after ablation of the homologous motor cortex. These results provide strong evidence of a causal link between cortical activation and precise ipsilateral control of hindlimb movement. This study has significant implications for the development of future neuroprosthetic technology and our understanding of motor control in the context of SCI.

Dopamine builds and reveals reward-associated latent behavioral attractors

Phasic variations in dopamine levels are interpreted as a teaching signal reinforcing rewarded behaviors. However, behavior also depends on the motivational, neuromodulatory effect of phasic dopamine. In this study, we reveal a neurodynamical principle that unifies these roles in a recurrent network-based decision architecture embodied through an action-perception loop with the task space, the MAGNet model. Dopamine optogenetic conditioning in mice was accounted for by an embodied network model in which attractors encode internal goals. Dopamine-dependent synaptic plasticity created "latent" attractors, to which dynamics converged, but only locally. Attractor basins were widened by dopamine-modulated synaptic excitability, rendering goals accessible globally, i.e. from distal positions. We validated these predictions optogenetically in mice: dopamine neuromodulation suddenly and specifically attracted animals toward rewarded locations, without off-target motor effects. We thus propose that motivational dopamine reveals dopamine-built attractors representing potential goals in a behavioral landscape.

Orbitofrontal Cortex Mediates Sustained Basolateral Amygdala Encoding of Cued Reward-Seeking States

Basolateral amygdala (BLA) neurons are engaged by emotionally salient stimuli. An area of increasing interest is how BLA dynamics relate to evolving reward-seeking behavior, especially under situations of uncertainty or ambiguity. Here, we recorded the activity of individual BLA neurons in male rats across the acquisition and extinction of conditioned reward seeking. We assessed ongoing neural dynamics in a task where long reward cue presentations preceded an unpredictable, variably time reward delivery. We found that, with training, BLA neurons discriminated the CS+ and CS- cues with sustained cue-evoked activity that correlated with behavior and terminated only after reward receipt. BLA neurons were bidirectionally modulated, with a majority showing prolonged inhibition during cued reward seeking. Strikingly, population-level analyses revealed that neurons showing cue-evoked inhibitions and those showing excitations similarly represented the CS+ and behavioral state. This sustained population code rapidly extinguished in parallel with conditioned behavior. We next assessed the contribution of the orbitofrontal cortex (OFC), a major reciprocal partner to the BLA. Inactivation of the OFC while simultaneously recording in the BLA revealed a blunting of sustained cue-evoked activity in the BLA that accompanied reduced reward seeking. Optogenetic disruption of BLA activity and OFC terminals in the BLA also reduced reward seeking. Our data indicate that the BLA represents reward-seeking states via sustained, bidirectional cue-driven neural encoding. This code is regulated by cortical input and is important for the maintenance of vigilant reward-seeking behavior.

Analysis methods for large-scale neuronal recordings

Simultaneous recordings from hundreds or thousands of neurons are becoming routine because of innovations in instrumentation, molecular tools, and data processing software. Such recordings can be analyzed with data science methods, but it is not immediately clear what methods to use or how to adapt them for neuroscience applications. We review, categorize, and illustrate diverse analysis methods for neural population recordings and describe how these methods have been used to make progress on longstanding questions in neuroscience. We review a variety of approaches, ranging from the mathematically simple to the complex, from exploratory to hypothesis-driven, and from recently developed to more established methods. We also illustrate some of the common statistical pitfalls in analyzing large-scale neural data.

Disentangling the Roles of Distinct Cell Classes with Cell-Type Dynamical Systems

Latent dynamical systems have been widely used to characterize the dynamics of neural population activity in the brain. However, these models typically ignore the fact that the brain contains multiple cell types. This limits their ability to capture the functional roles of distinct cell classes, and to predict the effects of cell-specific perturbations on neural activity or behavior. To overcome these limitations, we introduce the "cell-type dynamical systems" (CTDS) model. This model extends latent linear dynamical systems to contain distinct latent variables for each cell class, with biologically inspired constraints on both dynamics and emissions. To illustrate our approach, we consider neural recordings with distinct excitatory (E) and inhibitory (I) populations. The CTDS model defines separate latents for both cell types, and constrains the dynamics so that E (I) latents have a strictly positive (negative) effects on other latents. We applied CTDS to recordings from rat frontal orienting fields (FOF) and anterior dorsal striatum (ADS) during an auditory decision-making task. The model achieved higher accuracy than a standard linear dynamical system (LDS), and revealed that the animal's choice can be decoded from both E and I latents and thus is not restricted to a single cell-class. We also performed in-silico optogenetic perturbation experiments in the FOF and ADS, and found that CTDS was able to replicate the experimentally observed effects of different perturbations on behavior, whereas a standard LDS model-which does not differentiate between cell types-did not. Crucially, our model allowed us to understand the effects of these perturbations by revealing the dynamics of different cell-specific latents. Finally, CTDS can also be used to identify cell types for neurons whose class labels are unknown in electrophysiological recordings. These results illustrate the power of the CTDS model to provide more accurate and more biologically interpretable descriptions of neural population dynamics and their relationship to behavior.

Unraveling the Complexity of Parkinson's Disease: Insights into Pathogenesis and Precision Interventions

Parkinson's disease (PD) is a neurodegenerative disorder characterized by dopaminergic neuron loss, leading to motor and non-motor symptoms. Early detection before symptom onset is crucial but challenging. This study presents a framework integrating circuit modeling, non-equilibrium dynamics, and optimization to understand PD pathogenesis and enable precision interventions. Neuronal firing patterns, particularly oscillatory activity, play a critical role in PD pathology. The basal ganglia network, specifically the subthalamic nucleus-external globus pallidus (STN-GPe) circuitry, exhibits abnormal activity associated with motor dysfunction. The framework leverages the non-equilibrium landscape and flux theory to identify key connections generating pathological activity, providing insights into disease progression and potential intervention points. The intricate STN-GPe interplay is highlighted, shedding light on compensatory mechanisms within this circuitry may initially counteract changes but later contribute to pathological alterations as disease progresses. The framework addresses the need for comprehensive evaluation methods to assess intervention outcomes. Cross-correlations between state variables provide superior early warning signals compared to traditional indicators relying on critical slowing down. By elucidating compensatory mechanisms and circuit dynamics, the framework contributes to improved management, early detection, risk assessment, and potential prevention/delay of PD development. This pioneering research paves the way for precision medicine in neurodegenerative disorders.

Thyroid hormone remodels cortex to coordinate body-wide metabolism and exploration

Animals adapt to environmental conditions by modifying the function of their internal organs, including the brain. To be adaptive, alterations in behavior must be coordinated with the functional state of organs throughout the body. Here, we find that thyroid hormone-a regulator of metabolism in many peripheral organs-directly activates cell-type-specific transcriptional programs in the frontal cortex of adult male mice. These programs are enriched for axon-guidance genes in glutamatergic projection neurons, synaptic regulatory genes in both astrocytes and neurons, and pro-myelination factors in oligodendrocytes, suggesting widespread plasticity of cortical circuits. Indeed, whole-cell electrophysiology revealed that thyroid hormone alters excitatory and inhibitory synaptic transmission, an effect that requires thyroid hormone-induced gene regulatory programs in presynaptic neurons. Furthermore, thyroid hormone action in the frontal cortex regulates innate exploratory behaviors and causally promotes exploratory decision-making. Thus, thyroid hormone acts directly on the cerebral cortex in males to coordinate exploratory behaviors with whole-body metabolic state.

Cortical actions of thyroid hormone: An exploration and metabolism crossroad

Classically, the central actions of thyroid hormones (THs) on metabolism occur within the hypothalamus. A recent article published in Cell by Sabatini and colleagues demonstrates that TH modulates cerebral cortical circuits of male mice, which might integrate exploratory behavior and whole-body metabolism.

Neural Dynamics Underlying False Alarms in Extrastriate Cortex

The unfolding of neural population activity can be approximated as a dynamical system. Stability in the latent dynamics that characterize neural population activity has been linked with consistency in animal behavior, such as motor control or value-based decision-making. However, whether similar dynamics characterize perceptual activity and decision-making in the visual cortex is not well understood. To test this, we recorded V4 populations in monkeys engaged in a non-match-to-sample visual change-detection task that required sustained engagement. We measured how the stability in the latent dynamics in V4 might affect monkeys' perceptual behavior. Specifically, we reasoned that unstable sensory neural activity around dynamic attractor boundaries may make animals susceptible to taking incorrect actions when withholding action would have been correct ("false alarms"). We made three key discoveries: 1) greater stability was associated with longer trial sequences; 2) false alarm rate decreased (and reaction times slowed) when neural dynamics were more stable; and, 3) low stability predicted false alarms on a single-trial level, and this relationship depended on the elapsed time during the trial, consistent with the latent neural state approaching an attractor boundary. Our results suggest the same outward false alarm behavior can be attributed to two different potential strategies that can be disambiguated by examining neural stability: 1) premeditated false alarms that might lead to greater stability in population dynamics and faster reaction time and 2) false alarms due to unstable sensory activity consistent with misperception.

Encoding of 2D Self-Centered Plans and World-Centered Positions in the Rat Frontal Orienting Field

The neural mechanisms of motor planning have been extensively studied in rodents. Preparatory activity in the frontal cortex predicts upcoming choice, but limitations of typical tasks have made it challenging to determine whether the spatial information is in a self-centered direction reference frame or a world-centered position reference frame. Here, we trained male rats to make delayed visually guided orienting movements to six different directions, with four different target positions for each direction, which allowed us to disentangle direction versus position tuning in neural activity. We recorded single unit activity from the rat frontal orienting field (FOF) in the secondary motor cortex, a region involved in planning orienting movements. Population analyses revealed that the FOF encodes two separate 2D maps of space. First, a 2D map of the planned and ongoing movement in a self-centered direction reference frame. Second, a 2D map of the animal's current position on the port wall in a world-centered reference frame. Thus, preparatory activity in the FOF represents self-centered upcoming movement directions, but FOF neurons multiplex both self- and world-reference frame variables at the level of single neurons. Neural network model comparison supports the view that despite the presence of world-centered representations, the FOF receives the target information as self-centered input and generates self-centered planning signals.

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.

Dynamic control of sequential retrieval speed in networks with heterogeneous learning rules

Temporal rescaling of sequential neural activity has been observed in multiple brain areas during behaviors involving time estimation and motor execution at variable speeds. Temporally asymmetric Hebbian rules have been used in network models to learn and retrieve sequential activity, with characteristics that are qualitatively consistent with experimental observations. However, in these models sequential activity is retrieved at a fixed speed. Here, we investigate the effects of a heterogeneity of plasticity rules on network dynamics. In a model in which neurons differ by the degree of temporal symmetry of their plasticity rule, we find that retrieval speed can be controlled by varying external inputs to the network. Neurons with temporally symmetric plasticity rules act as brakes and tend to slow down the dynamics, while neurons with temporally asymmetric rules act as accelerators of the dynamics. We also find that such networks can naturally generate separate 'preparatory' and 'execution' activity patterns with appropriate external inputs.

Adaptive robustness through incoherent signaling mechanisms in a regenerative brain

Animal behavior emerges from collective dynamics of neurons, making it vulnerable to damage. Paradoxically, many organisms exhibit a remarkable ability to maintain significant behavior even after large-scale neural injury. Molecular underpinnings of this extreme robustness remain largely unknown. Here, we develop a quantitative pipeline to measure long-lasting latent states in planarian flatworm behaviors during whole-brain regeneration. By combining >20,000 animal trials with neural network modeling, we show that long-range volumetric peptidergic signals allow the planarian to rapidly restore coarse behavior output after large perturbations to the nervous system, while slow restoration of small-molecule neuromodulator functions refines precision. This relies on the different time and length scales of neuropeptide and small-molecule transmission to generate incoherent patterns of neural activity that competitively regulate behavior. Controlling behavior through opposing communication mechanisms creates a more robust system than either alone and may serve as a generalizable approach for constructing robust neural networks.

An output-null signature of inertial load in motor cortex

Coordinated movement requires the nervous system to continuously compensate for changes in mechanical load across different conditions. For voluntary movements like reaching, the motor cortex is a critical hub that generates commands to move the limbs and counteract loads. How does cortex contribute to load compensation when rhythmic movements are sequenced by a spinal pattern generator? Here, we address this question by manipulating the mass of the forelimb in unrestrained mice during locomotion. While load produces changes in motor output that are robust to inactivation of motor cortex, it also induces a profound shift in cortical dynamics. This shift is minimally affected by cerebellar perturbation and significantly larger than the load response in the spinal motoneuron population. This latent representation may enable motor cortex to generate appropriate commands when a voluntary movement must be integrated with an ongoing, spinally-generated rhythm.

Symmetry in Frontal But Not Motor and Somatosensory Cortical Projections

The neocortex and striatum are topographically organized for sensory and motor functions. While sensory and motor areas are lateralized for touch and motor control, respectively, frontal areas are involved in decision-making, where lateralization of function may be less important. This study contrasted the topographic precision of cell-type-specific ipsilateral and contralateral cortical projections while varying the injection site location in transgenic mice of both sexes. While sensory cortical areas had strongly topographic outputs to the ipsilateral cortex and striatum, they were weaker and not as topographically precise to contralateral targets. The motor cortex had somewhat stronger projections but still relatively weak contralateral topography. In contrast, frontal cortical areas had high degrees of topographic similarity for both ipsilateral and contralateral projections to the cortex and striatum. Corticothalamic organization is mainly ipsilateral, with weaker, more medial contralateral projections. Corticostriatal computations might integrate input outside closed basal ganglia loops using contralateral projections, enabling the two hemispheres to act as a unit to converge on one result in motor planning and decision-making.

Constructing neural networks with pre-specified dynamics

A main goal in neuroscience is to understand the computations carried out by neural populations that give animals their cognitive skills. Neural network models allow to formulate explicit hypotheses regarding the algorithms instantiated in the dynamics of a neural population, its firing statistics, and the underlying connectivity. Neural networks can be defined by a small set of parameters, carefully chosen to procure specific capabilities, or by a large set of free parameters, fitted with optimization algorithms that minimize a given loss function. In this work we alternatively propose a method to make a detailed adjustment of the network dynamics and firing statistic to better answer questions that link dynamics, structure, and function. Our algorithm-termed generalised Firing-to-Parameter (gFTP)-provides a way to construct binary recurrent neural networks whose dynamics strictly follows a user pre-specified transition graph that details the transitions between population firing states triggered by stimulus presentations. Our main contribution is a procedure that detects when a transition graph is not realisable in terms of a neural network, and makes the necessary modifications in order to obtain a new transition graph that is realisable and preserves all the information encoded in the transitions of the original graph. With a realisable transition graph, gFTP assigns values to the network firing states associated with each node in the graph, and finds the synaptic weight matrices by solving a set of linear separation problems. We test gFTP performance by constructing networks with random dynamics, continuous attractor-like dynamics that encode position in 2-dimensional space, and discrete attractor dynamics. We then show how gFTP can be employed as a tool to explore the link between structure, function, and the algorithms instantiated in the network dynamics.

Assessing Cross-Contamination in Spike-Sorted Electrophysiology Data

Recent advances in extracellular electrophysiology now facilitate the recording of spikes from hundreds or thousands of neurons simultaneously. This has necessitated both the development of new computational methods for spike sorting and better methods to determine spike-sorting accuracy. One long-standing method of assessing the false discovery rate (FDR) of spike sorting-the rate at which spikes are assigned to the wrong cluster-has been the rate of interspike interval (ISI) violations. Despite their near ubiquitous usage in spike sorting, our understanding of how exactly ISI violations relate to FDR, as well as best practices for using ISI violations as a quality metric, remains limited. Here, we describe an analytical solution that can be used to predict FDR from the ISI violation rate (ISIv). We test this model in silico through Monte Carlo simulation and apply it to publicly available spike-sorted electrophysiology datasets. We find that the relationship between ISIv and FDR is highly nonlinear, with additional dependencies on firing frequency, the correlation in activity between neurons, and contaminant neuron count. Predicted median FDRs in public datasets recorded in mice were found to range from 3.1 to 50.0%. We found that stochasticity in the occurrence of ISI violations as well as uncertainty in cluster-specific parameters make it difficult to predict FDR for single clusters with high confidence but that FDR can be estimated accurately across a population of clusters. Our findings will help the growing community of researchers using extracellular electrophysiology assess spike-sorting accuracy in a principled manner.

Goal-directed learning is multidimensional and accompanied by diverse and widespread changes in neocortical signaling

New tasks are often learned in stages with each stage reflecting a different learning challenge. Accordingly, each learning stage is likely mediated by distinct neuronal processes. And yet, most rodent studies of the neuronal correlates of goal-directed learning focus on individual outcome measures and individual brain regions. Here, we longitudinally studied mice from naïve to expert performance in a head-fixed, operant conditioning whisker discrimination task. In addition to tracking the primary behavioral outcome of stimulus discrimination, we tracked and compared an array of object-based and temporal-based behavioral measures. These behavioral analyses identify multiple, partially overlapping learning stages in this task, consistent with initial response implementation, early stimulus-response generalization, and late response inhibition. To begin to understand the neuronal foundations of these learning processes, we performed widefield Ca2+ imaging of dorsal neocortex throughout learning and correlated behavioral measures with neuronal activity. We found distinct and widespread correlations between neocortical activation patterns and various behavioral measures. For example, improvements in sensory discrimination correlated with target stimulus evoked activations of response-related cortices along with distractor stimulus evoked global cortical suppression. Our study reveals multidimensional learning for a simple goal-directed learning task and generates hypotheses for the neuronal modulations underlying these various learning processes.

Integrator dynamics in the cortico-basal ganglia loop underlie flexible motor timing

Flexible control of motor timing is crucial for behavior. Before volitional movement begins, the frontal cortex and striatum exhibit ramping spiking activity, with variable ramp slopes anticipating movement onsets. This activity in the cortico-basal ganglia loop may function as an adjustable 'timer,' triggering actions at the desired timing. However, because the frontal cortex and striatum share similar ramping dynamics and are both necessary for timing behaviors, distinguishing their individual roles in this timer function remains challenging. To address this, we conducted perturbation experiments combined with multi-regional electrophysiology in mice performing a flexible lick-timing task. Following transient silencing of the frontal cortex, cortical and striatal activity swiftly returned to pre-silencing levels and resumed ramping, leading to a shift in lick timing close to the silencing duration. Conversely, briefly inhibiting the striatum caused a gradual decrease in ramping activity in both regions, with ramping resuming from post-inhibition levels, shifting lick timing beyond the inhibition duration. Thus, inhibiting the frontal cortex and striatum effectively paused and rewound the timer, respectively. These findings suggest the striatum is a part of the network that temporally integrates input from the frontal cortex and generates ramping activity that regulates motor timing.

A latent pool of neurons silenced by sensory-evoked inhibition can be recruited to enhance perception

To investigate which activity patterns in sensory cortex are relevant for perceptual decision-making, we combined two-photon calcium imaging and targeted two-photon optogenetics to interrogate barrel cortex activity during perceptual discrimination. We trained mice to discriminate bilateral whisker deflections and report decisions by licking left or right. Two-photon calcium imaging revealed sparse coding of contralateral and ipsilateral whisker input in layer 2/3, with most neurons remaining silent during the task. Activating pyramidal neurons using two-photon holographic photostimulation evoked a perceptual bias that scaled with the number of neurons photostimulated. This effect was dominated by optogenetic activation of non-coding neurons, which did not show sensory or motor-related activity during task performance. Photostimulation also revealed potent recruitment of cortical inhibition during sensory processing, which strongly and preferentially suppressed non-coding neurons. Our results suggest that a pool of non-coding neurons, selectively suppressed by network inhibition during sensory processing, can be recruited to enhance perception.

Chronic restraint stress induces depression-like behaviors and alterations in the afferent projections of medial prefrontal cortex from multiple brain regions in mice

INTRODUCTION

The medial prefrontal cortex (mPFC) forms output pathways through projection neurons, inversely receiving adjacent and long-range inputs from other brain regions. However, how afferent neurons of mPFC are affected by chronic stress needs to be clarified. In this study, the effects of chronic restraint stress (CRS) on the distribution density of mPFC dendrites/dendritic spines and the projections from the cortex and subcortical brain regions to the mPFC were investigated.

METHODS

In the present study, C57BL/6 J transgenic (Thy1-YFP-H) mice were subjected to CRS to establish an animal model of depression. The infralimbic (IL) of mPFC was selected as the injection site of retrograde AAV using stereotactic technique. The effects of CRS on dendrites/dendritic spines and afferent neurons of the mPFC IL were investigaed by quantitatively assessing the distribution density of green fluorescent (YFP) positive dendrites/dendritic spines and red fluorescent (retrograde AAV recombinant protein) positive neurons, respectively.

RESULTS

The results revealed that retrograde tracing virus labeled neurons were widely distributed in ipsilateral and contralateral cingulate cortex (Cg1), second cingulate cortex (Cg2), prelimbic cortex (PrL), infralimbic cortex, medial orbital cortex (MO), and dorsal peduncular cortex (DP). The effects of CRS on the distribution density of mPFC red fluorescence positive neurons exhibited regional differences, ranging from rostral to caudal or from top to bottom. Simultaneously, CRS resulted a decrease in the distribution density of basal, proximal and distal dendrites, as well as an increase in the loss of dendritic spines of the distal dendrites in the IL of mPFC. Furthermore, varying degrees of red retrograde tracing virus fluorescence signals were observed in other cortices, amygdala, hippocampus, septum/basal forebrain, hypothalamus, thalamus, mesencephalon, and brainstem in both ipsilateral and contralateral brain. CRS significantly reduced the distribution density of red fluorescence positive neurons in other cortices, hippocampus, septum/basal forebrain, hypothalamus, and thalamus. Conversely, CRS significantly increased the distribution density of red fluorescence positive neurons in amygdala.

CONCLUSION

Our results suggest a possible mechanism that CRS leads to disturbances in synaptic plasticity by affecting multiple inputs to the mPFC, which is characterized by a decrease in the distribution density of dendrites/dendritic spines in the IL of mPFC and a reduction in input neurons of multiple cortices to the IL of mPFC as well as an increase in input neurons of amygdala to the IL of mPFC, ultimately causing depression-like behaviors.

Stimulation of caudal inferior and middle frontal gyri disrupts planning during spoken interaction

Turn-taking is a central feature of conversation across languages and cultures.1,2,3,4 This key social behavior requires numerous sensorimotor and cognitive operations1,5,6 that can be organized into three general phases: comprehension of a partner's turn, preparation of a speaker's own turn, and execution of that turn. Using intracranial electrocorticography, we recently demonstrated that neural activity related to these phases is functionally distinct during turn-taking.7 In particular, networks active during the perceptual and articulatory stages of turn-taking consisted of structures known to be important for speech-related sensory and motor processing,8,9,10,11,12,13,14,15,16,17 while putative planning dynamics were most regularly observed in the caudal inferior frontal gyrus (cIFG) and the middle frontal gyrus (cMFG). To test if these structures are necessary for planning during spoken interaction, we used direct electrical stimulation (DES) to transiently perturb cortical function in neurosurgical patient-volunteers performing a question-answer task.7,18,19 We found that stimulating the cIFG and cMFG led to various response errors9,13,20,21 but not gross articulatory deficits, which instead resulted from DES of structures involved in motor control8,13,20,22 (e.g., the precentral gyrus). Furthermore, perturbation of the cIFG and cMFG delayed inter-speaker timing-consistent with slowed planning-while faster responses could result from stimulation of sites located in other areas. Taken together, our findings suggest that the cIFG and cMFG contain critical preparatory circuits that are relevant for interactive language use.

Rule-based modulation of a sensorimotor transformation across cortical areas

Flexible responses to sensory stimuli based on changing rules are critical for adapting to a dynamic environment. However, it remains unclear how the brain encodes and uses rule information to guide behavior. Here, we made single-unit recordings while head-fixed mice performed a cross-modal sensory selection task where they switched between two rules: licking in response to tactile stimuli while rejecting visual stimuli, or vice versa. Along a cortical sensorimotor processing stream including the primary (S1) and secondary (S2) somatosensory areas, and the medial (MM) and anterolateral (ALM) motor areas, single-neuron activity distinguished between the two rules both prior to and in response to the tactile stimulus. We hypothesized that neural populations in these areas would show rule-dependent preparatory states, which would shape the subsequent sensory processing and behavior. This hypothesis was supported for the motor cortical areas (MM and ALM) by findings that (1) the current task rule could be decoded from pre-stimulus population activity; (2) neural subspaces containing the population activity differed between the two rules; and (3) optogenetic disruption of pre-stimulus states impaired task performance. Our findings indicate that flexible action selection in response to sensory input can occur via configuration of preparatory states in the motor cortex.

A combinatorial neural code for long-term motor memory

Motor skill repertoire can be stably retained over long periods, but the neural mechanism underlying stable memory storage remains poorly understood. Moreover, it is unknown how existing motor memories are maintained as new motor skills are continuously acquired. Here we tracked neural representation of learned actions throughout a significant portion of a mouse's lifespan, and we show that learned actions are stably retained in motor memory in combination with context, which protects existing memories from erasure during new motor learning. We used automated home-cage training to establish a continual learning paradigm in which mice learned to perform directional licking in different task contexts. We combined this paradigm with chronic two-photon imaging of motor cortex activity for up to 6 months. Within the same task context, activity driving directional licking was stable over time with little representational drift. When learning new task contexts, new preparatory activity emerged to drive the same licking actions. Learning created parallel new motor memories while retaining the previous memories. Re-learning to make the same actions in the previous task context re-activated the previous preparatory activity, even months later. At the same time, continual learning of new task contexts kept creating new preparatory activity patterns. Context-specific memories, as we observed in the motor system, may provide a solution for stable memory storage throughout continual learning. Learning in new contexts produces parallel new representations instead of modifying existing representations, thus protecting existing motor repertoire from erasure.

Symmetry in frontal but not motor and somatosensory cortical projections

Neocortex and striatum are topographically organized for sensory and motor functions. While sensory and motor areas are lateralized for touch and motor control, respectively, frontal areas are involved in decision making, where lateralization of function may be less important. This study contrasted the topographic precision of cell type-specific ipsilateral and contralateral cortical projections while varying the injection site location in transgenic mice of both sexes. While sensory cortical areas had strongly topographic outputs to ipsilateral cortex and striatum, they were weaker and not as topographically precise to contralateral targets. Motor cortex had somewhat stronger projections, but still relatively weak contralateral topography. In contrast, frontal cortical areas had high degrees of topographic similarity for both ipsilateral and contralateral projections to cortex and striatum. Corticothalamic organization is mainly ipsilateral, with weaker, more medial contralateral projections. Corticostriatal computations might integrate input outside closed basal ganglia loops using contralateral projections, enabling the two hemispheres to act as a unit to converge on one result in motor planning and decision making.

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.

One nose but two nostrils: Learn to align with sparse connections between two olfactory cortices

The integration of neural representations in the two hemispheres is an important problem in neuroscience. Recent experiments revealed that odor responses in cortical neurons driven by separate stimulation of the two nostrils are highly correlated. This bilateral alignment points to structured inter-hemispheric connections, but detailed mechanism remains unclear. Here, we hypothesized that continuous exposure to environmental odors shapes these projections and modeled it as online learning with local Hebbian rule. We found that Hebbian learning with sparse connections achieves bilateral alignment, exhibiting a linear trade-off between speed and accuracy. We identified an inverse scaling relationship between the number of cortical neurons and the inter-hemispheric projection density required for desired alignment accuracy, i.e., more cortical neurons allow sparser inter-hemispheric projections. We next compared the alignment performance of local Hebbian rule and the global stochastic-gradient-descent (SGD) learning for artificial neural networks. We found that although SGD leads to the same alignment accuracy with modestly sparser connectivity, the same inverse scaling relation holds. We showed that their similar performance originates from the fact that the update vectors of the two learning rules align significantly throughout the learning process. This insight may inspire efficient sparse local learning algorithms for more complex problems.

Recurrent Neural Circuits Overcome Partial Inactivation by Compensation and Re-learning

Technical advances in artificial manipulation of neural activity have precipitated a surge in studying the causal contribution of brain circuits to cognition and behavior. However, complexities of neural circuits challenge interpretation of experimental results, necessitating new theoretical frameworks for reasoning about causal effects. Here, we take a step in this direction, through the lens of recurrent neural networks trained to perform perceptual decisions. We show that understanding the dynamical system structure that underlies network solutions provides a precise account for the magnitude of behavioral effects due to perturbations. Our framework explains past empirical observations by clarifying the most sensitive features of behavior, and how complex circuits compensate and adapt to perturbations. In the process, we also identify strategies that can improve the interpretability of inactivation experiments.

Cross-hemispheric communication: Insights on lateralized brain functions

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

Preparatory activity and the expansive null-space

The study of the cortical control of movement experienced a conceptual shift over recent decades, as the basic currency of understanding shifted from single-neuron tuning towards population-level factors and their dynamics. This transition was informed by a maturing understanding of recurrent networks, where mechanism is often characterized in terms of population-level factors. By estimating factors from data, experimenters could test network-inspired hypotheses. Central to such hypotheses are 'output-null' factors that do not directly drive motor outputs yet are essential to the overall computation. In this Review, we highlight how the hypothesis of output-null factors was motivated by the venerable observation that motor-cortex neurons are active during movement preparation, well before movement begins. We discuss how output-null factors then became similarly central to understanding neural activity during movement. We discuss how this conceptual framework provided key analysis tools, making it possible for experimenters to address long-standing questions regarding motor control. We highlight an intriguing trend: as experimental and theoretical discoveries accumulate, the range of computational roles hypothesized to be subserved by output-null factors continues to expand.

Separating cognitive and motor processes in the behaving mouse

The cognitive processes supporting complex animal behavior are closely associated with ubiquitous movements responsible for our posture, facial expressions, ability to actively sample our sensory environments, and other critical processes. These movements are strongly related to neural activity across much of the brain and are often highly correlated with ongoing cognitive processes, making it challenging to dissociate the neural dynamics that support cognitive processes from those supporting related movements. In such cases, a critical issue is whether cognitive processes are separable from related movements, or if they are driven by common neural mechanisms. Here, we demonstrate how the separability of cognitive and motor processes can be assessed, and, when separable, how the neural dynamics associated with each component can be isolated. We establish a novel two-context behavioral task in mice that involves multiple cognitive processes and show that commonly observed dynamics taken to support cognitive processes are strongly contaminated by movements. When cognitive and motor components are isolated using a novel approach for subspace decomposition, we find that they exhibit distinct dynamical trajectories. Further, properly accounting for movement revealed that largely separate populations of cells encode cognitive and motor variables, in contrast to the 'mixed selectivity' often reported. Accurately isolating the dynamics associated with particular cognitive and motor processes will be essential for developing conceptual and computational models of neural circuit function and evaluating the function of the cell types of which neural circuits are composed.

Rule-based modulation of a sensorimotor transformation across cortical areas

Flexible responses to sensory stimuli based on changing rules are critical for adapting to a dynamic environment. However, it remains unclear how the brain encodes rule information and uses this information to guide behavioral responses to sensory stimuli. Here, we made single-unit recordings while head-fixed mice performed a cross-modal sensory selection task in which they switched between two rules in different blocks of trials: licking in response to tactile stimuli applied to a whisker while rejecting visual stimuli, or licking to visual stimuli while rejecting the tactile stimuli. Along a cortical sensorimotor processing stream including the primary (S1) and secondary (S2) somatosensory areas, and the medial (MM) and anterolateral (ALM) motor areas, the single-trial activity of individual neurons distinguished between the two rules both prior to and in response to the tactile stimulus. Variable rule-dependent responses to identical stimuli could in principle occur via appropriate configuration of pre-stimulus preparatory states of a neural population, which would shape the subsequent response. We hypothesized that neural populations in S1, S2, MM and ALM would show preparatory activity states that were set in a rule-dependent manner to cause processing of sensory information according to the current rule. This hypothesis was supported for the motor cortical areas by findings that (1) the current task rule could be decoded from pre-stimulus population activity in ALM and MM; (2) neural subspaces containing the population activity differed between the two rules; and (3) optogenetic disruption of pre-stimulus states within ALM and MM impaired task performance. Our findings indicate that flexible selection of an appropriate action in response to a sensory input can occur via configuration of preparatory states in the motor cortex.

Differential Formation of Motor Cortical Dynamics during Movement Preparation According to the Predictability of Go Timing

The motor cortex not only executes but also prepares movement, as motor cortical neurons exhibit preparatory activity that predicts upcoming movements. In movement preparation, animals adopt different strategies in response to uncertainties existing in nature such as the unknown timing of when a predator will attack-an environmental cue informing "go." However, how motor cortical neurons cope with such uncertainties is less understood. In this study, we aim to investigate whether and how preparatory activity is altered depending on the predictability of "go" timing. We analyze firing activities of the anterior lateral motor cortex in male mice during two auditory delayed-response tasks each with predictable or unpredictable go timing. When go timing is unpredictable, preparatory activities immediately reach and stay in a neural state capable of producing movement anytime to a sudden go cue. When go timing is predictable, preparation activity reaches the movement-producible state more gradually, to secure more accurate decisions. Surprisingly, this preparation process entails a longer reaction time. We find that as preparatory activity increases in accuracy, it takes longer for a neural state to transition from the end of preparation to the start of movement. Our results suggest that the motor cortex fine-tunes preparatory activity for more accurate movement using the predictability of go timing.

Brain-wide neural activity underlying memory-guided movement

Behavior relies on activity in structured neural circuits that are distributed across the brain, but most experiments probe neurons in a single area at a time. Using multiple Neuropixels probes, we recorded from multi-regional loops connected to the anterior lateral motor cortex (ALM), a circuit node mediating memory-guided directional licking. Neurons encoding sensory stimuli, choices, and actions were distributed across the brain. However, choice coding was concentrated in the ALM and subcortical areas receiving input from the ALM in an ALM-dependent manner. Diverse orofacial movements were encoded in the hindbrain; midbrain; and, to a lesser extent, forebrain. Choice signals were first detected in the ALM and the midbrain, followed by the thalamus and other brain areas. At movement initiation, choice-selective activity collapsed across the brain, followed by new activity patterns driving specific actions. Our experiments provide the foundation for neural circuit models of decision-making and movement initiation.

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.