Reconstruction of 1,000 Projection Neurons Reveals New Cell Types and Organization of Long-Range Connectivity in the Mouse Brain.

Neuronal cell types are the nodes of neural circuits that determine the flow of information within the brain. Neuronal morphology, especially the shape of the axonal arbor, provides an essential descriptor of cell type and reveals how individual neurons route their output across the brain. Despite the importance of morphology, few projection neurons in the mouse brain have been reconstructed in their entirety. Here we present a robust and efficient platform for imaging and reconstructing complete neuronal morphologies, including axonal arbors that span substantial portions of the brain. We used this platform to reconstruct more than 1,000 projection neurons in the motor cortex, thalamus, subiculum, and hypothalamus. Together, the reconstructed neurons constitute more than 85 meters of axonal length and are available in a searchable online database. Axonal shapes revealed previously unknown subtypes of projection neurons and suggest organizational principles of long-range connectivity.

Expanding the Optogenetics Toolkit by Topological Inversion of Rhodopsins.

Targeted manipulation of activity in specific populations of neurons is important for investigating the neural circuit basis of behavior. Optogenetic approaches using light-sensitive microbial rhodopsins have permitted manipulations to reach a level of temporal precision that is enabling functional circuit dissection. As demand for more precise perturbations to serve specific experimental goals increases, a palette of opsins with diverse selectivity, kinetics, and spectral properties will be needed. Here, we introduce a novel approach of "topological engineering"-inversion of opsins in the plasma membrane-and demonstrate that it can produce variants with unique functional properties of interest for circuit neuroscience. In one striking example, inversion of a Channelrhodopsin variant converted it from a potent activator into a fast-acting inhibitor that operates as a cation pump. Our findings argue that membrane topology provides a useful orthogonal dimension of protein engineering that immediately permits as much as a doubling of the available toolkit.

Dopamine Is Required for the Neural Representation and Control of Movement Vigor.

Progressive depletion of midbrain dopamine neurons (PDD) is associated with deficits in the initiation, speed, and fluidity of voluntary movement. Models of basal ganglia function focus on initiation deficits; however, it is unclear how they account for deficits in the speed or amplitude of movement (vigor). Using an effort-based operant conditioning task for head-fixed mice, we discovered distinct functional classes of neurons in the dorsal striatum that represent movement vigor. Mice with PDD exhibited a progressive reduction in vigor, along with a selective impairment of its neural representation in striatum. Restoration of dopaminergic tone with a synthetic precursor ameliorated deficits in movement vigor and its neural representation, while suppression of striatal activity during movement was sufficient to reduce vigor. Thus, dopaminergic input to the dorsal striatum is indispensable for the emergence of striatal activity that mediates adaptive changes in movement vigor. These results suggest refined intervention strategies for Parkinson's disease.

Mesolimbic dopamine adapts the rate of learning from action.

Recent success in training artificial agents and robots derives from a combination of direct learning of behavioural policies and indirect learning through value functions1-3. Policy learning and value learning use distinct algorithms that optimize behavioural performance and reward prediction, respectively. In animals, behavioural learning and the role of mesolimbic dopamine signalling have been extensively evaluated with respect to reward prediction4; however, so far there has been little consideration of how direct policy learning might inform our understanding5. Here we used a comprehensive dataset of orofacial and body movements to understand how behavioural policies evolved as naive, head-restrained mice learned a trace conditioning paradigm. Individual differences in initial dopaminergic reward responses correlated with the emergence of learned behavioural policy, but not the emergence of putative value encoding for a predictive cue. Likewise, physiologically calibrated manipulations of mesolimbic dopamine produced several effects inconsistent with value learning but predicted by a neural-network-based model that used dopamine signals to set an adaptive rate, not an error signal, for behavioural policy learning. This work provides strong evidence that phasic dopamine activity can regulate direct learning of behavioural policies, expanding the explanatory power of reinforcement learning models for animal learning6.

Preserved neural dynamics across animals performing similar behaviour.

Animals of the same species exhibit similar behaviours that are advantageously adapted to their body and environment. These behaviours are shaped at the species level by selection pressures over evolutionary timescales. Yet, it remains unclear how these common behavioural adaptations emerge from the idiosyncratic neural circuitry of each individual. The overall organization of neural circuits is preserved across individuals1 because of their common evolutionarily specified developmental programme2-4. Such organization at the circuit level may constrain neural activity5-8, leading to low-dimensional latent dynamics across the neural population9-11. Accordingly, here we suggested that the shared circuit-level constraints within a species would lead to suitably preserved latent dynamics across individuals. We analysed recordings of neural populations from monkey and mouse motor cortex to demonstrate that neural dynamics in individuals from the same species are surprisingly preserved when they perform similar behaviour. Neural population dynamics were also preserved when animals consciously planned future movements without overt behaviour12 and enabled the decoding of planned and ongoing movement across different individuals. Furthermore, we found that preserved neural dynamics extend beyond cortical regions to the dorsal striatum, an evolutionarily older structure13,14. Finally, we used neural network models to demonstrate that behavioural similarity is necessary but not sufficient for this preservation. We posit that these emergent dynamics result from evolutionary constraints on brain development and thus reflect fundamental properties of the neural basis of behaviour.

Basal Ganglia Circuits for Action Specification.

Behavior is readily classified into patterns of movements with inferred common goals-actions. Goals may be discrete; movements are continuous. Through the careful study of isolated movements in laboratory settings, or via introspection, it has become clear that animals can exhibit exquisite graded specification to their movements. Moreover, graded control can be as fundamental to success as the selection of which action to perform under many naturalistic scenarios: a predator adjusting its speed to intercept moving prey, or a tool-user exerting the perfect amount of force to complete a delicate task. The basal ganglia are a collection of nuclei in vertebrates that extend from the forebrain (telencephalon) to the midbrain (mesencephalon), constituting a major descending extrapyramidal pathway for control over midbrain and brainstem premotor structures. Here we discuss how this pathway contributes to the continuous specification of movements that endows our voluntary actions with vigor and grace.

Opponent and bidirectional control of movement velocity in the basal ganglia.

For goal-directed behaviour it is critical that we can both select the appropriate action and learn to modify the underlying movements (for example, the pitch of a note or velocity of a reach) to improve outcomes. The basal ganglia are a critical nexus where circuits necessary for the production of behaviour, such as the neocortex and thalamus, are integrated with reward signalling to reinforce successful, purposive actions. The dorsal striatum, a major input structure of basal ganglia, is composed of two opponent pathways, direct and indirect, thought to select actions that elicit positive outcomes and suppress actions that do not, respectively. Activity-dependent plasticity modulated by reward is thought to be sufficient for selecting actions in the striatum. Although perturbations of basal ganglia function produce profound changes in movement, it remains unknown whether activity-dependent plasticity is sufficient to produce learned changes in movement kinematics, such as velocity. Here we use cell-type-specific stimulation in mice delivered in closed loop during movement to demonstrate that activity in either the direct or indirect pathway is sufficient to produce specific and sustained increases or decreases in velocity, without affecting action selection or motivation. These behavioural changes were a form of learning that accumulated over trials, persisted after the cessation of stimulation, and were abolished in the presence of dopamine antagonists. Our results reveal that the direct and indirect pathways can each bidirectionally control movement velocity, demonstrating unprecedented specificity and flexibility in the control of volition by the basal ganglia.

A Specific Component of the Evoked Potential Mirrors Phasic Dopamine Neuron Activity during Conditioning.

Midbrain dopamine (DA) neurons are thought to be a critical node in the circuitry that mediates reward learning. DA neurons receive diverse inputs from regions distributed throughout the neuraxis from frontal neocortex to the mesencephalon. While a great deal is known about changes in the activity of individual DA neurons during learning, much less is known about the functional changes in the microcircuits in which DA neurons are embedded. Here we used local field potentials recorded from the midbrain of behaving mice to show that the midbrain evoked potential (mEP) faithfully reflects the temporal and spatial structure of the phasic response of midbrain neuron populations during conditioning. By comparing the mEP to simultaneously recorded single units, we identified specific components of the mEP that corresponded to phasic DA and non-DA responses to salient stimuli. The DA component of the mEP emerged with the acquisition of a conditioned stimulus, was extinguished following changes in reinforcement contingency, and could be inhibited by pharmacological manipulations that attenuate the phasic responses of DA neurons. In contrast to single-unit recordings, the mEP permitted relatively dense sampling of the midbrain circuit during conditioning and thus could be used to reveal the spatiotemporal structure of multiple intermingled midbrain circuits. Finally, the mEP response was stable for months and thus provides a new approach to study long-term changes in the organization of ventral midbrain microcircuits during learning. Significance statement: Neurons that synthesize and release the neurotransmitter dopamine play a critical role in voluntary reward-seeking behavior. Much of our insight into the function of dopamine neurons comes from recordings of individual cells in behaving animals; however, it is notoriously difficult to record from dopamine neurons due to their sparsity and depth, as well as the presence of intermingled non-dopaminergic neurons. Here we show that much of the information that can be learned from recordings of individual dopamine and non-dopamine neurons is also revealed by changes in specific components of the local field potential. This technique provides an accessible measurement that could prove critical to our burgeoning understanding of the molecular, functional, and anatomical diversity of neuron populations in the midbrain.

Precise spatial coding is preserved along the longitudinal hippocampal axis.

Compared with the dorsal hippocampus, relatively few studies have characterized neuronal responses in the ventral hippocampus. In particular, it is unclear whether and how cells in the ventral region represent space and/or respond to contextual changes. We recorded from dorsal and ventral CA1 neurons in freely moving mice exposed to manipulations of visuospatial and olfactory contexts. We found that ventral cells respond to alterations of the visuospatial environment such as exposure to novel local cues, cue rotations, and contextual expansion in similar ways to dorsal cells, with the exception of cue rotations. Furthermore, we found that ventral cells responded to odors much more strongly than dorsal cells, particularly to odors of high valence. Similar to earlier studies recording from the ventral hippocampus in CA3, we also found increased scaling of place cell field size along the longitudinal hippocampal axis. Although the increase in place field size observed toward the ventral pole has previously been taken to suggest a decrease in spatial information coded by ventral place cells, we hypothesized that a change in spatial scaling could instead signal a shift in representational coding that preserves the resolution of spatial information. To explore this possibility, we examined population activity using principal component analysis (PCA) and neural location reconstruction techniques. Our results suggest that ventral populations encode a distributed representation of space, and that the resolution of spatial information at the population level is comparable to that of dorsal populations of similar size. Finally, through the use of neural network modeling, we suggest that the redundancy in spatial representation along the longitudinal hippocampal axis may allow the hippocampus to overcome the conflict between memory interference and generalization inherent in neural network memory. Our results indicate that ventral population activity is well suited for generalization across locations and contexts.

Integrating across behaviors and timescales to understand the neural control of movement.

The nervous system evolved to enable navigation throughout the environment in the pursuit of resources. Evolutionarily newer structures allowed increasingly complex adaptations but necessarily added redundancy. A dominant view of movement neuroscientists is that there is a one-to-one mapping between brain region and function. However, recent experimental data is hard to reconcile with the most conservative interpretation of this framework, suggesting a degree of functional redundancy during the performance of well-learned, constrained behaviors. This apparent redundancy likely stems from the bidirectional interactions between the various cortical and subcortical structures involved in motor control. We posit that these bidirectional connections enable flexible interactions across structures that change depending upon behavioral demands, such as during acquisition, execution or adaptation of a skill. Observing the system across both multiple actions and behavioral timescales can help isolate the functional contributions of individual structures, leading to an integrated understanding of the neural control of movement.

Nonlinear manifolds underlie neural population activity during behaviour.

There is rich variety in the activity of single neurons recorded during behaviour. Yet, these diverse single neuron responses can be well described by relatively few patterns of neural co-modulation. The study of such low-dimensional structure of neural population activity has provided important insights into how the brain generates behaviour. Virtually all of these studies have used linear dimensionality reduction techniques to estimate these population-wide co-modulation patterns, constraining them to a flat "neural manifold". Here, we hypothesised that since neurons have nonlinear responses and make thousands of distributed and recurrent connections that likely amplify such nonlinearities, neural manifolds should be intrinsically nonlinear. Combining neural population recordings from monkey motor cortex, mouse motor cortex, mouse striatum, and human motor cortex, we show that: 1) neural manifolds are intrinsically nonlinear; 2) the degree of their nonlinearity varies across architecturally distinct brain regions; and 3) manifold nonlinearity becomes more evident during complex tasks that require more varied activity patterns. Simulations using recurrent neural network models confirmed the proposed relationship between circuit connectivity and manifold nonlinearity, including the differences across architecturally distinct regions. Thus, neural manifolds underlying the generation of behaviour are inherently nonlinear, and properly accounting for such nonlinearities will be critical as neuroscientists move towards studying numerous brain regions involved in increasingly complex and naturalistic behaviours.

Hippocampal representations of foraging trajectories depend upon spatial context.

Animals learn trajectories to rewards in both spatial, navigational contexts and relational, non-navigational contexts. Synchronous reactivation of hippocampal activity is thought to be critical for recall and evaluation of trajectories for learning. Do hippocampal representations differentially contribute to experience-dependent learning of trajectories across spatial and relational contexts? In this study, we trained mice to navigate to a hidden target in a physical arena or manipulate a joystick to a virtual target to collect delayed rewards. In a navigational context, calcium imaging in freely moving mice revealed that synchronous CA1 reactivation was retrospective and important for evaluation of prior navigational trajectories. In a non-navigational context, reactivation was prospective and important for initiation of joystick trajectories, even in the same animals trained in both contexts. Adaptation of trajectories to a new target was well-explained by a common learning algorithm in which hippocampal activity makes dissociable contributions to reinforcement learning computations depending upon spatial context.

Motor cortical output for skilled forelimb movement is selectively distributed across projection neuron classes.

The interaction of descending neocortical outputs and subcortical premotor circuits is critical for shaping skilled movements. Two broad classes of motor cortical output projection neurons provide input to many subcortical motor areas: pyramidal tract (PT) neurons, which project throughout the neuraxis, and intratelencephalic (IT) neurons, which project within the cortex and subcortical striatum. It is unclear whether these classes are functionally in series or whether each class carries distinct components of descending motor control signals. Here, we combine large-scale neural recordings across all layers of motor cortex with cell type-specific perturbations to study cortically dependent mouse motor behaviors: kinematically variable manipulation of a joystick and a kinematically precise reach-to-grasp. We find that striatum-projecting IT neuron activity preferentially represents amplitude, whereas pons-projecting PT neurons preferentially represent the variable direction of forelimb movements. Thus, separable components of descending motor cortical commands are distributed across motor cortical projection cell classes.

A role for synaptic inputs at distal dendrites: instructive signals for hippocampal long-term plasticity.

Synaptic potentials originating at distal dendritic locations are severely attenuated when they reach the soma and, thus, are poor at driving somatic spikes. Nonetheless, distal inputs convey essential information, suggesting that such inputs may be important for compartmentalized dendritic signaling. Here we report a new plasticity rule in which stimulation of distal perforant path inputs to hippocampal CA1 pyramidal neurons induces long-term potentiation at the CA1 proximal Schaffer collateral synapses when the two inputs are paired at a precise interval. This subthreshold form of heterosynaptic plasticity occurs in the absence of somatic spiking but requires activation of both NMDA receptors and IP(3) receptor-dependent release of Ca(2+) from internal stores. Our results suggest that direct sensory information arriving at distal CA1 synapses through the perforant path provide compartmentalized, instructive signals that assess the saliency of mnemonic information propagated through the hippocampal circuit to proximal synapses.

HCN1 channels constrain synaptically evoked Ca2+ spikes in distal dendrites of CA1 pyramidal neurons.

HCN1 hyperpolarization-activated cation channels act as an inhibitory constraint of both spatial learning and synaptic integration and long-term plasticity in the distal dendrites of hippocampal CA1 pyramidal neurons. However, as HCN1 channels provide an excitatory current, the mechanism of their inhibitory action remains unclear. Here we report that HCN1 channels also constrain CA1 distal dendritic Ca2+ spikes, which have been implicated in the induction of LTP at distal excitatory synapses. Our experimental and computational results indicate that HCN1 channels provide both an active shunt conductance that decreases the temporal integration of distal EPSPs and a tonic depolarizing current that increases resting inactivation of T-type and N-type voltage-gated Ca2+ channels, which contribute to the Ca2+ spikes. This dual mechanism may provide a general means by which HCN channels regulate dendritic excitability.

In Vivo Optogenetics with Stimulus Calibration.

Optogenetic reagents allow for depolarization and hyperpolarization of cells with light. This provides unprecedented spatial and temporal resolution to the control of neuronal activity both in vitro and in vivo. In the intact animal this requires strategies to deliver light deep into the highly scattering tissue of the brain. A general approach that we describe here is to implant optical fibers just above brain regions targeted for light delivery. In part due to the fact that expression of optogenetic proteins is accomplished by techniques with inherent variability (e.g., viral expression levels), it also requires strategies to measure and calibrate the effect of stimulation. Here we describe general procedures that allow one to simultaneously stimulate neurons and use photometry with genetically encoded activity indicators to precisely calibrate stimulation.

Dissociable contributions of phasic dopamine activity to reward and prediction.

Sensory cues that precede reward acquire predictive (expected value) and incentive (drive reward-seeking action) properties. Mesolimbic dopamine neurons' responses to sensory cues correlate with both expected value and reward-seeking action. This has led to the proposal that phasic dopamine responses may be sufficient to inform value-based decisions, elicit actions, and/or induce motivational states; however, causal tests are incomplete. Here, we show that direct dopamine neuron stimulation, both calibrated to physiological and greater intensities, at the time of reward can be sufficient to induce and maintain reward seeking (reinforcing) although replacement of a cue with stimulation is insufficient to induce reward seeking or act as an informative cue. Stimulation of descending cortical inputs, one synapse upstream, are sufficient for reinforcement and cues to future reward. Thus, physiological activation of mesolimbic dopamine neurons can be sufficient for reinforcing properties of reward without being sufficient for the predictive and incentive properties of cues.

Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings.

Measuring the dynamics of neural processing across time scales requires following the spiking of thousands of individual neurons over milliseconds and months. To address this need, we introduce the Neuropixels 2.0 probe together with newly designed analysis algorithms. The probe has more than 5000 sites and is miniaturized to facilitate chronic implants in small mammals and recording during unrestrained behavior. High-quality recordings over long time scales were reliably obtained in mice and rats in six laboratories. Improved site density and arrangement combined with newly created data processing methods enable automatic post hoc correction for brain movements, allowing recording from the same neurons for more than 2 months. These probes and algorithms enable stable recordings from thousands of sites during free behavior, even in small animals such as mice.

Making the grade with models of persistent activity.

Persistent neural activity that outlasts an initial stimulus is thought to provide a mechanism for the transient storage of memory. In this issue of Neuron, Fransén et al. identify important principles for a cell-autonomous mechanism of graded persistent firing using an elegant combination of experimental and computational approaches.

Stochastically gating ion channels enable patterned spike firing through activity-dependent modulation of spike probability.

The transformation of synaptic input into patterns of spike output is a fundamental operation that is determined by the particular complement of ion channels that a neuron expresses. Although it is well established that individual ion channel proteins make stochastic transitions between conducting and non-conducting states, most models of synaptic integration are deterministic, and relatively little is known about the functional consequences of interactions between stochastically gating ion channels. Here, we show that a model of stellate neurons from layer II of the medial entorhinal cortex implemented with either stochastic or deterministically gating ion channels can reproduce the resting membrane properties of stellate neurons, but only the stochastic version of the model can fully account for perithreshold membrane potential fluctuations and clustered patterns of spike output that are recorded from stellate neurons during depolarized states. We demonstrate that the stochastic model implements an example of a general mechanism for patterning of neuronal output through activity-dependent changes in the probability of spike firing. Unlike deterministic mechanisms that generate spike patterns through slow changes in the state of model parameters, this general stochastic mechanism does not require retention of information beyond the duration of a single spike and its associated afterhyperpolarization. Instead, clustered patterns of spikes emerge in the stochastic model of stellate neurons as a result of a transient increase in firing probability driven by activation of HCN channels during recovery from the spike afterhyperpolarization. Using this model, we infer conditions in which stochastic ion channel gating may influence firing patterns in vivo and predict consequences of modifications of HCN channel function for in vivo firing patterns.