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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., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Recently developed probes for extracellular electrophysiological recordings have large numbers of electrodes on long linear shanks. Linear electrode arrays, such as Neuropixels probes, have hundreds of recording electrodes distributed over linear shanks that span several millimeters. Because of the length of the probes, linear probe recordings in rodents usually cover multiple brain areas. Typical studies collate recordings across several recording sessions and animals. Neurons recorded in different sessions and animals thus have to be aligned to each other and to a standardized brain coordinate system. Here, we evaluate two typical workflows for localization of individual electrodes in standardized coordinates. These workflows rely on imaging brains with fluorescent probe tracks and warping 3D image stacks to standardized brain atlases. One workflow is based on tissue clearing and selective plane illumination microscopy (SPIM), whereas the other workflow is based on serial block-face two-photon (SBF2P) microscopy. In both cases electrophysiological features are then used to anchor particular electrodes along the reconstructed tracks to specific locations in the brain atlas and therefore to specific brain structures. We performed groundtruth experiments, in which motor cortex outputs are labeled with ChR2 and a fluorescence protein. Light-evoked electrical activity and fluorescence can be independently localized. Recordings from brain regions targeted by the motor cortex reveal better than 0.1-mm accuracy for electrode localization, independent of workflow used., (Copyright © 2021 Liu et al.)

Animals are not simple input-output machines. Their responses to even very similar stimuli are variable. A key, long-standing question in neuroscience is to understand the neural correlates of such behavioral variability. To reveal these correlates, behavior and neural population activity must be related to one another on single trials. Such analysis is challenging due to the dynamical nature of brain function (e.g., in decision making), heterogeneity across neurons and limited sampling of the relevant neural population. By analyzing population recordings from mouse frontal cortex in perceptual decision-making tasks, we show that an analysis approach tailored to the coarse grain features of the dynamics is able to reveal previously unrecognized structure in the organization of population activity. This structure is similar on error and correct trials, suggesting dynamics that may be constrained by the underlying circuitry, is able to predict multiple aspects of behavioral variability and reveals long time-scale modulation of population activity.

Neurons in motor cortex and connected brain regions fire in anticipation of specific movements, long before movement occurs. This neural activity reflects internal processes by which the brain plans and executes volitional movements. The study of motor planning offers an opportunity to understand how the structure and dynamics of neural circuits support persistent internal states and how these states influence behavior. Recent advances in large-scale neural recordings are beginning to decipher the relationship of the dynamics of populations of neurons during motor planning and movements. New behavioral tasks in rodents, together with quantified perturbations, link dynamics in specific nodes of neural circuits to behavior. These studies reveal a neural network distributed across multiple brain regions that collectively supports motor planning. We review recent advances and highlight areas where further work is needed to achieve a deeper understanding of the mechanisms underlying motor planning and related cognitive processes., (Copyright © 2017. Published by Elsevier Ltd.)

Activity in the mouse anterior lateral motor cortex (ALM) instructs directional movements, often seconds before movement initiation. It is unknown whether this preparatory activity is localized to ALM or widely distributed within motor cortex. Here we imaged activity across motor cortex while mice performed a whisker-based object localization task with a delayed, directional licking response. During tactile sensation and the delay epoch, object location was represented in motor cortex areas that are medial and posterior relative to ALM, including vibrissal motor cortex. Preparatory activity appeared first in deep layers of ALM, seconds before the behavioral response, and remained localized to ALM until the behavioral response. Later, widely distributed neurons represented the outcome of the trial. Cortical area was more predictive of neuronal selectivity than laminar location or axonal projection target. Motor cortex therefore represents sensory, motor, and outcome information in a spatially organized manner., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Neural activity maintains representations that bridge past and future events, often over many seconds. Network models can produce persistent and ramping activity, but the positive feedback that is critical for these slow dynamics can cause sensitivity to perturbations. Here we use electrophysiology and optogenetic perturbations in the mouse premotor cortex to probe the robustness of persistent neural representations during motor planning. We show that preparatory activity is remarkably robust to large-scale unilateral silencing: detailed neural dynamics that drive specific future movements were quickly and selectively restored by the network. Selectivity did not recover after bilateral silencing of the premotor cortex. Perturbations to one hemisphere are thus corrected by information from the other hemisphere. Corpus callosum bisections demonstrated that premotor cortex hemispheres can maintain preparatory activity independently. Redundancy across selectively coupled modules, as we observed in the premotor cortex, is a hallmark of robust control systems. Network models incorporating these principles show robustness that is consistent with data.

Active sensation requires the convergence of external stimuli with representations of body movements. We used mouse behavior, electrophysiology and optogenetics to dissect the temporal interactions among whisker movement, neural activity and sensation of touch. We photostimulated layer 4 activity in single barrels in a closed loop with whisking. Mimicking touch-related neural activity caused illusory perception of an object at a particular location, but scrambling the timing of the spikes over one whisking cycle (tens of milliseconds) did not abolish the illusion, indicating that knowledge of instantaneous whisker position is unnecessary for discriminating object locations. The illusions were induced only during bouts of directed whisking, when mice expected touch, and in the relevant barrel. Reducing activity biased behavior, consistent with a spike count code for object detection at a particular location. Our results show that mice integrate coding of touch with movement over timescales of a whisking bout to produce perception of active touch.

Neurons at the top of primate ventral visual stream [inferior temporal cortex (IT)] have selectivity for objects that is highly tolerant to variation in the object's appearance on the retina. Previous nonhuman primate (Macaca mulatta) studies suggest that this neuronal tolerance is at least partly supported by the natural temporal contiguity of visual experience, because altering that temporal contiguity can robustly alter adult IT position and size tolerance. According to that work, it is the statistics of the subject's visual experience, not the subject's reward, that instruct the specific images that IT treats as equivalent. But is reward necessary for gating this type of learning in the ventral stream? Here we show that this is not the case--temporal tolerance learning proceeds at the same rate, regardless of reward magnitude and regardless of the temporal co-occurrence of reward, even in a behavioral task that does not require the subject to engage the object images. This suggests that the ventral visual stream uses autonomous, fully unsupervised mechanisms to constantly leverage all visual experience to help build its invariant object representation.

Primates can easily identify visual objects over large changes in retinal position--a property commonly referred to as position "invariance." This ability is widely assumed to depend on neurons in inferior temporal cortex (IT) that can respond selectively to isolated visual objects over similarly large ranges of retinal position. However, in the real world, objects rarely appear in isolation, and the interplay between position invariance and the representation of multiple objects (i.e., clutter) remains unresolved. At the heart of this issue is the intuition that the representations of nearby objects can interfere with one another and that the large receptive fields needed for position invariance can exacerbate this problem by increasing the range over which interference acts. Indeed, most IT neurons' responses are strongly affected by the presence of clutter. While external mechanisms (such as attention) are often invoked as a way out of the problem, we show (using recorded neuronal data and simulations) that the intrinsic properties of IT population responses, by themselves, can support object recognition in the face of limited clutter. Furthermore, we carried out extensive simulations of hypothetical neuronal populations to identify the essential individual-neuron ingredients of a good population representation. These simulations show that the crucial neuronal property to support recognition in clutter is not preservation of response magnitude, but preservation of each neuron's rank-order object preference under identity-preserving image transformations (e.g., clutter). Because IT neuronal responses often exhibit that response property, while neurons in earlier visual areas (e.g., V1) do not, we suggest that preserving the rank-order object preference regardless of clutter, rather than the response magnitude, more precisely describes the goal of individual neurons at the top of the ventral visual stream.

Object recognition is challenging because each object produces myriad retinal images. Responses of neurons from the inferior temporal cortex (IT) are selective to different objects, yet tolerant ("invariant") to changes in object position, scale, and pose. How does the brain construct this neuronal tolerance? We report a form of neuronal learning that suggests the underlying solution. Targeted alteration of the natural temporal contiguity of visual experience caused specific changes in IT position tolerance. This unsupervised temporal slowness learning (UTL) was substantial, increased with experience, and was significant in single IT neurons after just 1 hour. Together with previous theoretical work and human object perception experiments, we speculate that UTL may reflect the mechanism by which the visual stream builds and maintains tolerant object representations.

Action selection occurs through competition between potential choice options. Neural correlates of choice competition are observed across frontal cortex and downstream superior colliculus (SC) during decision-making, yet how these regions interact to mediate choice competition remains unresolved. Here we report that SC can bidirectionally modulate choice competition and drive choice activity in frontal cortex. In the mouse, topographically matched regions of frontal cortex and SC formed a descending motor pathway for directional licking and a re-entrant loop via the thalamus. During decision-making, distinct neuronal populations in both frontal cortex and SC encoded opposing lick directions and exhibited competitive interactions. SC GABAergic neurons encoded ipsilateral choice and locally inhibited glutamatergic neurons that encoded contralateral choice. Activating or suppressing these cell types could bidirectionally drive choice activity in frontal cortex. These results thus identify SC as a major locus to modulate choice competition within the broader action selection network. Multiple cortical and subcortical brain regions are implicated in decision-making, yet their causal interactions remain unclear. Here, the authors identified cellular and circuit interactions that bias cortical decision-making dynamics and behavior. [ABSTRACT FROM AUTHOR]

Motor behaviors are often planned long before execution but only released after specific sensory events. Planning and execution are each associated with distinct patterns of motor cortex activity. Key questions are how these dynamic activity patterns are generated and how they relate to behavior. Here, we investigate the multi-regional neural circuits that link an auditory "Go cue" and the transition from planning to execution of directional licking. Ascending glutamatergic neurons in the midbrain reticular and pedunculopontine nuclei show short latency and phasic changes in spike rate that are selective for the Go cue. This signal is transmitted via the thalamus to the motor cortex, where it triggers a rapid reorganization of motor cortex state from planning-related activity to a motor command, which in turn drives appropriate movement. Our studies show how midbrain can control cortical dynamics via the thalamus for rapid and precise motor behavior. [Display omitted] • Distinct motor cortex activity patterns underlie planning and movement initiation • A contextual cue reorganizes motor cortex activity to initiate planned movements • Midbrain neurons transmit information to cortex via thalamus to release movements • Midbrain activity is causal for reorganizing cortical activity and releasing movements Ascending midbrain neurons trigger rapid reorganization of motor cortex activity via thalamus to switch from planning-related activity to a motor command that initiates movement in mice [ABSTRACT FROM AUTHOR]