Somatic and Dendritic Encoding of Spatial Variables in Retrosplenial Cortex Differs during 2D Navigation

Flexible gating between subspaces in a neural network model of internally guided task switching

Behavioral flexibility relies on the brain's ability to switch rapidly between multiple tasks, even when the task rule is not explicitly cued but must be inferred through trial and error. The underlying neural circuit mechanism remains poorly understood. We investigated recurrent neural networks (RNNs) trained to perform an analog of the classic Wisconsin Card Sorting Test. The networks consist of two modules responsible for rule representation and sensorimotor mapping, respectively, where each module is comprised of a circuit with excitatory neurons and three major types of inhibitory neurons. We found that rule representation by self-sustained persistent activity across trials, error monitoring and gated sensorimotor mapping emerged from training. Systematic dissection of trained RNNs revealed a detailed circuit mechanism that is consistent across networks trained with different hyperparameters. The networks' dynamical trajectories for different rules resided in separate subspaces of population activity; the subspaces collapsed and performance was reduced to chance level when dendrite-targeting somatostatin-expressing interneurons were silenced, illustrating how a phenomenological description of representational subspaces is explained by a specific circuit mechanism.

Engram mechanisms of memory linking and identity

Memories are thought to be stored in neuronal ensembles referred to as engrams. Studies have suggested that when two memories occur in quick succession, a proportion of their engrams overlap and the memories become linked (in a process known as prospective linking) while maintaining their individual identities. In this Review, we summarize the key principles of memory linking through engram overlap, as revealed by experimental and modelling studies. We describe evidence of the involvement of synaptic memory substrates, spine clustering and non-linear neuronal capacities in prospective linking, and suggest a dynamic somato-synaptic model, in which memories are shared between neurons yet remain separable through distinct dendritic and synaptic allocation patterns. We also bring into focus retrospective linking, in which memories become associated after encoding via offline reactivation, and discuss key temporal and mechanistic differences between prospective and retrospective linking, as well as the potential differences in their cognitive outcomes.

Coordinated head direction representations in mouse anterodorsal thalamic nucleus and retrosplenial cortex

The sense of direction is critical for survival in changing environments and relies on flexibly integrating self-motion signals with external sensory cues. While the anatomical substrates involved in head direction (HD) coding are well known, the mechanisms by which visual information updates HD representations remain poorly understood. Retrosplenial cortex (RSC) plays a key role in forming coherent representations of space in mammals and it encodes a variety of navigational variables, including HD. Here, we use simultaneous two-area tetrode recording to show that RSC HD representation is nearly synchronous with that of the anterodorsal nucleus of thalamus (ADn), the obligatory thalamic relay of HD to cortex, during rotation of a prominent visual cue. Moreover, coordination of HD representations in the two regions is maintained during darkness. We further show that anatomical and functional connectivity are consistent with a strong feedforward drive of HD information from ADn to RSC, with anatomically restricted corticothalamic feedback. Together, our results indicate a concerted global HD reference update across cortex and thalamus.

Full field-of-view virtual reality goggles for mice

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

Mesotrode chronic simultaneous mesoscale cortical imaging and subcortical or peripheral nerve spiking activity recording in mice

Brain function originates from hierarchical spatial-temporal neural dynamics distributed across cortical and subcortical networks. However, techniques available to assess large-scale brain network activity with single-neuron resolution in behaving animals remain limited. Here, we present Mesotrode that integrates chronic wide-field mesoscale cortical imaging and compact multi-site cortical/subcortical cellular electrophysiology in head-fixed mice that undergo self-initiated running or orofacial movements. Specifically, we harnessed the flexibility of chronic multi-site tetrode recordings to monitor single-neuron activity in multiple subcortical structures while simultaneously imaging the mesoscale activity of the entire dorsal cortex. A mesoscale spike-triggered averaging procedure allowed the identification of cortical activity motifs preferentially associated with single-neuron spiking. Using this approach, we were able to characterize chronic single-neuron-related functional connectivity maps for up to 60 days post-implantation. Neurons recorded from distinct subcortical structures display diverse but segregated cortical maps, suggesting that neurons of different origins participate in distinct cortico-subcortical pathways. We extended the capability of Mesotrode by implanting the micro-electrode at the facial motor nerve and found that facial nerve spiking is functionally associated with the PTA, RSP, and M2 network, and optogenetic inhibition of the PTA area significantly reduced the facial movement of the mice. These findings demonstrate that Mesotrode can be used to sample different combinations of cortico-subcortical networks over prolonged periods, generating multimodal and multi-scale network activity from a single implant, offering new insights into the neural mechanisms underlying specific behaviors.

Brain-wide neural recordings in mice navigating physical spaces enabled by a cranial exoskeleton

Complex behaviors are mediated by neural computations occurring throughout the brain. In recent years, tremendous progress has been made in developing technologies that can record neural activity at cellular resolution at multiple spatial and temporal scales. However, these technologies are primarily designed for studying the mammalian brain during head fixation - wherein the behavior of the animal is highly constrained. Miniaturized devices for studying neural activity in freely behaving animals are largely confined to recording from small brain regions owing to performance limitations. We present a cranial exoskeleton that assists mice in maneuvering neural recording headstages that are orders of magnitude larger and heavier than the mice, while they navigate physical behavioral environments. Force sensors embedded within the headstage are used to detect the mouse's milli-Newton scale cranial forces which then control the x, y, and yaw motion of the exoskeleton via an admittance controller. We discovered optimal controller tuning parameters that enable mice to locomote at physiologically realistic velocities and accelerations while maintaining natural walking gait. Mice maneuvering headstages weighing up to 1.5 kg can make turns, navigate 2D arenas, and perform a navigational decision-making task with the same performance as when freely behaving. We designed an imaging headstage and an electrophysiology headstage for the cranial exoskeleton to record brain-wide neural activity in mice navigating 2D arenas. The imaging headstage enabled recordings of Ca2+ activity of 1000s of neurons distributed across the dorsal cortex. The electrophysiology headstage supported independent control of up to 4 silicon probes, enabling simultaneous recordings from 100s of neurons across multiple brain regions and multiple days. Cranial exoskeletons provide flexible platforms for largescale neural recording during the exploration of physical spaces, a critical new paradigm for unraveling the brain-wide neural mechanisms that control complex behavior.

Vectorized instructive signals in cortical dendrites during a brain-computer interface task

Backpropagation of error is the most widely used learning algorithm in artificial neural networks, forming the backbone of modern machine learning and artificial intelligence1,2. Backpropagation provides a solution to the credit assignment problem by vectorizing an error signal tailored to individual neurons. Recent theoretical models have suggested that neural circuits could implement backpropagation-like learning by semi-independently processing feedforward and feedback information streams in separate dendritic compartments3-7. This presents a compelling, but untested, hypothesis for how cortical circuits could solve credit assignment in the brain. We designed a neurofeedback brain-computer interface (BCI) task with an experimenter-defined reward function to evaluate the key requirements for dendrites to implement backpropagation-like learning. We trained mice to modulate the activity of two spatially intermingled populations (4 or 5 neurons each) of layer 5 pyramidal neurons in the retrosplenial cortex to rotate a visual grating towards a target orientation while we recorded GCaMP activity from somas and corresponding distal apical dendrites. We observed that the relative magnitudes of somatic versus dendritic signals could be predicted using the activity of the surrounding network and contained information about task-related variables that could serve as instructive signals, including reward and error. The signs of these putative teaching signals both depended on the causal role of individual neurons in the task and predicted changes in overall activity over the course of learning. These results provide the first biological evidence of a backpropagation-like solution to the credit assignment problem in the brain.

The dendritic engram

Accumulating evidence from a wide range of studies, including behavioral, cellular, molecular and computational findings, support a key role of dendrites in the encoding and recall of new memories. Dendrites can integrate synaptic inputs in non-linear ways, provide the substrate for local protein synthesis and facilitate the orchestration of signaling pathways that regulate local synaptic plasticity. These capabilities allow them to act as a second layer of computation within the neuron and serve as the fundamental unit of plasticity. As such, dendrites are integral parts of the memory engram, namely the physical representation of memories in the brain and are increasingly studied during learning tasks. Here, we review experimental and computational studies that support a novel, dendritic view of the memory engram that is centered on non-linear dendritic branches as elementary memory units. We highlight the potential implications of dendritic engrams for the learning and memory field and discuss future research directions.

Brain-wide neural recordings in mice navigating physical spaces enabled by a cranial exoskeleton

Complex behaviors are mediated by neural computations occurring throughout the brain. In recent years, tremendous progress has been made in developing technologies that can record neural activity at cellular resolution at multiple spatial and temporal scales. However, these technologies are primarily designed for studying the mammalian brain during head fixation - wherein the behavior of the animal is highly constrained. Miniaturized devices for studying neural activity in freely behaving animals are largely confined to recording from small brain regions owing to performance limitations. We present a cranial exoskeleton that assists mice in maneuvering neural recording headstages that are orders of magnitude larger and heavier than the mice, while they navigate physical behavioral environments. Force sensors embedded within the headstage are used to detect the mouse's milli-Newton scale cranial forces which then control the x, y, and yaw motion of the exoskeleton via an admittance controller. We discovered optimal controller tuning parameters that enable mice to locomote at physiologically realistic velocities and accelerations while maintaining natural walking gait. Mice maneuvering headstages weighing up to 1.5 kg can make turns, navigate 2D arenas, and perform a navigational decision-making task with the same performance as when freely behaving. We designed an imaging headstage and an electrophysiology headstage for the cranial exoskeleton to record brain-wide neural activity in mice navigating 2D arenas. The imaging headstage enabled recordings of Ca2+ activity of 1000s of neurons distributed across the dorsal cortex. The electrophysiology headstage supported independent control of up to 4 silicon probes, enabling simultaneous recordings from 100s of neurons across multiple brain regions and multiple days. Cranial exoskeletons provide flexible platforms for largescale neural recording during the exploration of physical spaces, a critical new paradigm for unraveling the brain-wide neural mechanisms that control complex behavior.

Coregistration of heading to visual cues in retrosplenial cortex

Spatial cognition depends on an accurate representation of orientation within an environment. Head direction cells in distributed brain regions receive a range of sensory inputs, but visual input is particularly important for aligning their responses to environmental landmarks. To investigate how population-level heading responses are aligned to visual input, we recorded from retrosplenial cortex (RSC) of head-fixed mice in a moving environment using two-photon calcium imaging. We show that RSC neurons are tuned to the animal's relative orientation in the environment, even in the absence of head movement. Next, we found that RSC receives functionally distinct projections from visual and thalamic areas and contains several functional classes of neurons. While some functional classes mirror RSC inputs, a newly discovered class coregisters visual and thalamic signals. Finally, decoding analyses reveal unique contributions to heading from each class. Our results suggest an RSC circuit for anchoring heading representations to environmental visual landmarks.

Sharp cell-type-identity changes differentiate the retrosplenial cortex from the neocortex

The laminae of the neocortex are fundamental processing layers of the mammalian brain. Notably, such laminae are believed to be relatively stereotyped across short spatial scales such that shared laminae between nearby brain regions exhibit similar constituent cells. Here, we consider a potential exception to this rule by studying the retrosplenial cortex (RSC), a brain region known for sharp cytoarchitectonic differences across its granular-dysgranular border. Using a variety of transcriptomics techniques, we identify, spatially map, and interpret the excitatory cell-type landscape of the mouse RSC. In doing so, we uncover that RSC gene expression and cell types change sharply at the granular-dysgranular border. Additionally, supposedly homologous laminae between the RSC and the neocortex are effectively wholly distinct in their cell-type composition. In collection, the RSC exhibits a variety of intrinsic cell-type specializations and embodies an organizational principle wherein cell-type identities can vary sharply within and between brain regions.

Cortical glutamatergic projection neuron types contribute to distinct functional subnetworks

The cellular basis of cerebral cortex functional architecture remains not well understood. A major challenge is to monitor and decipher neural network dynamics across broad cortical areas yet with projection-neuron-type resolution in real time during behavior. Combining genetic targeting and wide-field imaging, we monitored activity dynamics of subcortical-projecting (PTFezf2) and intratelencephalic-projecting (ITPlxnD1) types across dorsal cortex of mice during different brain states and behaviors. ITPlxnD1 and PTFezf2 neurons showed distinct activation patterns during wakeful resting, during spontaneous movements and upon sensory stimulation. Distinct ITPlxnD1 and PTFezf2 subnetworks were dynamically tuned to different sensorimotor components of a naturalistic feeding behavior, and optogenetic inhibition of ITsPlxnD1 and PTsFezf2 in subnetwork nodes disrupted distinct components of this behavior. Lastly, ITPlxnD1 and PTFezf2 projection patterns are consistent with their subnetwork activation patterns. Our results show that, in addition to the concept of columnar organization, dynamic areal and projection-neuron-type specific subnetworks are a key feature of cortical functional architecture linking microcircuit components with global brain networks.

Rethinking retrosplenial cortex: Perspectives and predictions

The last decade has produced exciting new ideas about retrosplenial cortex (RSC) and its role in integrating diverse inputs. Here, we review the diversity in forms of spatial and directional tuning of RSC activity, temporal organization of RSC activity, and features of RSC interconnectivity with other brain structures. We find that RSC anatomy and dynamics are more consistent with roles in multiple sensorimotor and cognitive processes than with any isolated function. However, two more generalized categories of function may best characterize roles for RSC in complex cognitive processes: (1) shifting and relating perspectives for spatial cognition and (2) prediction and error correction for current sensory states with internal representations of the environment. Both functions likely take advantage of RSC's capacity to encode conjunctions among sensory, motor, and spatial mapping information streams. Together, these functions provide the scaffold for intelligent actions, such as navigation, perspective taking, interaction with others, and error detection.

Refinements to rodent head fixation and fluid/food control for neuroscience

The use of head fixation in mice is increasingly common in research, its use having initially been restricted to the field of sensory neuroscience. Head restraint has often been combined with fluid control, rather than food restriction, to motivate behaviour, but this too is now in use for both restrained and non-restrained animals. Despite this, there is little guidance on how best to employ these techniques to optimise both scientific outcomes and animal welfare. This article summarises current practices and provides recommendations to improve animal wellbeing and data quality, based on a survey of the community, literature reviews, and the expert opinion and practical experience of an international working group convened by the UK's National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs). Topics covered include head fixation surgery and post-operative care, habituation to restraint, and the use of fluid/food control to motivate performance. We also discuss some recent developments that may offer alternative ways to collect data from large numbers of behavioural trials without the need for restraint. The aim is to provide support for researchers at all levels, animal care staff, and ethics committees to refine procedures and practices in line with the refinement principle of the 3Rs.

Review of data processing of functional optical microscopy for neuroscience

Functional optical imaging in neuroscience is rapidly growing with the development of optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. We cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges.

Implantable graded-index fibers for neural-dynamics-resolving brain imaging in awake mice on an air-lifted platform

We demonstrate a versatile framework for cellular brain imaging in awake mice based on suitably tailored segments of graded-index (GRIN) fiber. Closed-form solutions to ray-path equations for graded-index waveguides are shown to offer important insights into image-transmission properties of GRIN fibers, suggesting useful recipes for optimized GRIN-fiber-based deep-brain imaging. We show that the lengths of GRIN imaging components intended for deep-brain studies in freely moving rodents need to be chosen as a tradeoff among the spatial resolution, the targeted imaging depth and the degree of fiber-probe invasiveness. In the experimental setting that we present in this paper, the head of an awake mouse with a GRIN-fiber implant is fixed under a microscope objective, but the mouse is free to move around an in-house-built flat-floored air-lifted platform, exploring a predesigned environment, configured as an arena for one of standard cognitive tests. We show that cellular-resolution deep-brain imaging can be integrated in this setting with robust cell-specific optical neural recording to enable in vivo studies with minimal physical restraints on animal models. The enhancement of the information capacity of the fluorescence signal, achieved via a suitable filtering of the GRIN-fiber readout, is shown to open routes toward practical imaging modalities whereby the deep-brain neuronal dynamics and axonal connections underpinning the integrative functions of essential brain structures can be studied in awake rodent models.

Neural circuit mechanisms of hierarchical sequence learning tested on large-scale recording data

The brain performs various cognitive functions by learning the spatiotemporal salient features of the environment. This learning requires unsupervised segmentation of hierarchically organized spike sequences, but the underlying neural mechanism is only poorly understood. Here, we show that a recurrent gated network of neurons with dendrites can efficiently solve difficult segmentation tasks. In this model, multiplicative recurrent connections learn a context-dependent gating of dendro-somatic information transfers to minimize error in the prediction of somatic responses by the dendrites. Consequently, these connections filter the redundant input features represented by the dendrites but unnecessary in the given context. The model was tested on both synthetic and real neural data. In particular, the model was successful for segmenting multiple cell assemblies repeating in large-scale calcium imaging data containing thousands of cortical neurons. Our results suggest that recurrent gating of dendro-somatic signal transfers is crucial for cortical learning of context-dependent segmentation tasks.

The brain learns about the environment from continuous streams of information to generate adequate behavior. This is not easy when sensory and motor sequences are hierarchically organized. Some cortical regions jointly represent multiple levels of sequence hierarchy, but how local cortical circuits learn hierarchical sequences remains largely unknown. Evidence shows that the dendrites of cortical neurons learn redundant representations of sensory information compared to the soma, suggesting a filtering process within a neuron. Our model proposes that recurrent synaptic inputs multiplicatively regulate this intracellular process by gating dendrite-to-soma information transfers depending on the context of sequence learning. Furthermore, our model provides a powerful tool to analyze the spatiotemporal patterns of neural activity in large-scale recording data.

Compartmentalized dendritic plasticity during associative learning

Experience-dependent changes in behavior are mediated by long-term functional modifications in brain circuits. Activity-dependent plasticity of synaptic input is a major underlying cellular process. Although we have a detailed understanding of synaptic and dendritic plasticity in vitro, little is known about the functional and plastic properties of active dendrites in behaving animals. Using deep brain two-photon Ca²⁺ imaging, we investigated how sensory responses in amygdala principal neurons develop upon classical fear conditioning, a form of associative learning. Fear conditioning induced differential plasticity in dendrites and somas regulated by compartment-specific inhibition. Our results indicate that learning-induced plasticity can be uncoupled between soma and dendrites, reflecting distinct synaptic and microcircuit-level mechanisms that increase the computational capacity of amygdala circuits.

Dynamic compartmental computations in tuft dendrites of layer 5 neurons during motor behavior

Tuft dendrites of layer 5 pyramidal neurons form specialized compartments important for motor learning and performance, yet their computational capabilities remain unclear. Structural-functional mapping of the tuft tree from the motor cortex during motor tasks revealed two morphologically distinct populations of layer 5 pyramidal tract neurons (PTNs) that exhibit specific tuft computational properties. Early bifurcating and large nexus PTNs showed marked tuft functional compartmentalization, representing different motor variable combinations within and between their two tuft hemi-trees. By contrast, late bifurcating and smaller nexus PTNs showed synchronous tuft activation. Dendritic structure and dynamic recruitment of the N-methyl-d-aspartate (NMDA)-spiking mechanism explained the differential compartmentalization patterns. Our findings support a morphologically dependent framework for motor computations, in which independent amplification units can be combinatorically recruited to represent different motor sequences within the same tree.

Learning cortical representations through perturbed and adversarial dreaming

Humans and other animals learn to extract general concepts from sensory experience without extensive teaching. This ability is thought to be facilitated by offline states like sleep where previous experiences are systemically replayed. However, the characteristic creative nature of dreams suggests that learning semantic representations may go beyond merely replaying previous experiences. We support this hypothesis by implementing a cortical architecture inspired by generative adversarial networks (GANs). Learning in our model is organized across three different global brain states mimicking wakefulness, non-rapid eye movement (NREM), and REM sleep, optimizing different, but complementary, objective functions. We train the model on standard datasets of natural images and evaluate the quality of the learned representations. Our results suggest that generating new, virtual sensory inputs via adversarial dreaming during REM sleep is essential for extracting semantic concepts, while replaying episodic memories via perturbed dreaming during NREM sleep improves the robustness of latent representations. The model provides a new computational perspective on sleep states, memory replay, and dreams, and suggests a cortical implementation of GANs.

Emergence of synaptic organization and computation in dendrites

Single neurons in the brain exhibit astounding computational capabilities, which gradually emerge throughout development and enable them to become integrated into complex neural circuits. These capabilities derive in part from the precise arrangement of synaptic inputs on the neurons' dendrites. While the full computational benefits of this arrangement are still unknown, a picture emerges in which synapses organize according to their functional properties across multiple spatial scales. In particular, on the local scale (tens of microns), excitatory synaptic inputs tend to form clusters according to their functional similarity, whereas on the scale of individual dendrites or the entire tree, synaptic inputs exhibit dendritic maps where excitatory synapse function varies smoothly with location on the tree. The development of this organization is supported by inhibitory synapses, which are carefully interleaved with excitatory synapses and can flexibly modulate activity and plasticity of excitatory synapses. Here, we summarize recent experimental and theoretical research on the developmental emergence of this synaptic organization and its impact on neural computations.

The guide to dendritic spikes of the mammalian cortex in vitro and in vivo

Half a century since their discovery by Llinás and colleagues, dendritic spikes have been observed in various neurons in different brain regions, from the neocortex and cerebellum to the basal ganglia. Dendrites exhibit a terrifically diverse but stereotypical repertoire of spikes, sometimes specific to subregions of the dendrite. Despite their prevalence, we only have a glimpse into their role in the behaving animal. This article aims to survey the full range of dendritic spikes found in excitatory and inhibitory neurons, compare them in vivo versus in vitro, and discuss new studies describing dendritic spikes in the human cortex. We focus on dendritic spikes in neocortical and hippocampal neurons and present a roadmap to identify and understand the broader role of dendritic spikes in single-cell computation.

Differential dendritic integration of long-range inputs in association cortex via subcellular changes in synaptic AMPA-to-NMDA receptor ratio

Synaptic NMDA receptors can produce powerful dendritic supralinearities that expand the computational repertoire of single neurons and their respective circuits. This form of supralinearity may represent a general principle for synaptic integration in thin dendrites. However, individual cortical neurons receive many diverse classes of input that may require distinct postsynaptic decoding schemes. Here, we show that sensory, motor, and thalamic inputs preferentially target basal, apical oblique, and distal tuft dendrites, respectively, in layer 5b pyramidal neurons of the mouse retrosplenial cortex, a visuospatial association area. These dendritic compartments exhibited differential expression of NMDA receptor-mediated supralinearity due to systematic changes in the AMPA-to-NMDA receptor ratio. Our results reveal a new schema for integration in cortical pyramidal neurons, in which dendrite-specific changes in synaptic receptors support input-localized decoding. This coexistence of multiple modes of dendritic integration in single neurons has important implications for synaptic plasticity and cortical computation.

Mapping vestibular and visual contributions to angular head velocity tuning in the cortex

Neurons that signal the angular velocity of head movements (AHV cells) are important for processing visual and spatial information. However, it has been challenging to isolate the sensory modality that drives them and to map their cortical distribution. To address this, we develop a method that enables rotating awake, head-fixed mice under a two-photon microscope in a visual environment. Starting in layer 2/3 of the retrosplenial cortex, a key area for vision and navigation, we find that 10% of neurons report angular head velocity (AHV). Their tuning properties depend on vestibular input with a smaller contribution of vision at lower speeds. Mapping the spatial extent, we find AHV cells in all cortical areas that we explored, including motor, somatosensory, visual, and posterior parietal cortex. Notably, the vestibular and visual contributions to AHV are area dependent. Thus, many cortical circuits have access to AHV, enabling a diverse integration with sensorimotor and cognitive information.

Context value updating and multidimensional neuronal encoding in the retrosplenial cortex

The retrosplenial cortex (RSC) has diverse functional inputs and is engaged by various sensory, spatial, and associative learning tasks. We examine how multiple functional aspects are integrated on the single-cell level in the RSC and how the encoding of task-related parameters changes across learning. Using a visuospatial context discrimination paradigm and two-photon calcium imaging in behaving mice, a large proportion of dysgranular RSC neurons was found to encode multiple task-related dimensions while forming context-value associations across learning. During reversal learning requiring increased cognitive flexibility, we revealed an increased proportion of multidimensional encoding neurons that showed higher decoding accuracy for behaviorally relevant context-value associations. Chemogenetic inactivation of RSC led to decreased behavioral context discrimination during learning phases in which context-value associations were formed, while recall of previously formed associations remained intact. RSC inactivation resulted in a persistent positive behavioral bias in valuing contexts, indicating a role for the RSC in context-value updating.

Probing mechanisms of visual spatial attention in mice

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

A distributed circuit for associating environmental context with motor choice in retrosplenial cortex

During navigation, animals often use recognition of familiar environmental contexts to guide motor action selection. The retrosplenial cortex (RSC) receives inputs from both visual cortex and subcortical regions required for spatial memory and projects to motor planning regions. However, it is not known whether RSC is important for associating familiar environmental contexts with specific motor actions. We test this possibility by developing a task in which motor trajectories are chosen based on the context. We find that mice exhibit differential predecision activity in RSC and that optogenetic suppression of RSC activity impairs task performance. Individual RSC neurons encode a range of task variables, often multiplexed with distinct temporal profiles. However, the responses are spatiotemporally organized, with task variables represented along a posterior-to-anterior gradient along RSC during the behavioral performance, consistent with histological characterization. These results reveal an anatomically organized retrosplenial cortical circuit for associating environmental contexts with appropriate motor outputs.

Active dendrites enable strong but sparse inputs to determine orientation selectivity

The dendrites of neocortical pyramidal neurons are excitable. However, it is unknown how synaptic inputs engage nonlinear dendritic mechanisms during sensory processing in vivo, and how they in turn influence action potential output. Here, we provide a quantitative account of the relationship between synaptic inputs, nonlinear dendritic events, and action potential output. We developed a detailed pyramidal neuron model constrained by in vivo dendritic recordings. We drive this model with realistic input patterns constrained by sensory responses measured in vivo and connectivity measured in vitro. We show mechanistically that under realistic conditions, dendritic Na+ and NMDA spikes are the major determinants of neuronal output in vivo. We demonstrate that these dendritic spikes can be triggered by a surprisingly small number of strong synaptic inputs, in some cases even by single synapses. We predict that dendritic excitability allows the 1% strongest synaptic inputs of a neuron to control the tuning of its output. Active dendrites therefore allow smaller subcircuits consisting of only a few strongly connected neurons to achieve selectivity for specific sensory features.

Local and Global Dynamics of Dendritic Activity in the Pyramidal Neuron

There has been increasing interest in the measurement and comparison of activity across compartments of the pyramidal neuron. Dendritic activity can occur both locally, on a single dendritic segment, or globally, involving multiple compartments of the single neuron. Little is known about how these dendritic dynamics shape and contribute to information processing and behavior. Although it has been difficult to characterize local and global activity in vivo due to the technical challenge of simultaneously recording from the entire dendritic arbor and soma, the rise of calcium imaging has driven the increased feasibility and interest of these experiments. However, the distinction between local and global activity made by calcium imaging requires careful consideration. In this review we describe local and global activity, discuss the difficulties and caveats of this distinction, and present the evidence of local and global activity in information processing and behavior.

Temporally multiplexed dual-plane imaging of neural activity with four-dimensional precision

Spatiotemporal patterns of neural activity generate brain functions, such as perception, memory, and behavior. Four-dimensional (4-D: x, y, z, t) analyses of such neural activity will facilitate understanding of brain functions. However, conventional two-photon microscope systems observe single-plane brain tissue alone at a time with cellular resolution. It faces a trade-off between the spatial resolution in the x-, y-, and z-axes and the temporal resolution by a limited point-by-point scan speed. To overcome this trade-off in 4-D imaging, we developed a holographic two-photon microscope for dual-plane imaging. A spatial light modulator (SLM) provided an additional focal plane at a different depth. Temporal multiplexing of split lasers with an optical chopper allowed fast imaging of two different focal planes. We simultaneously recorded the activities of neurons on layers 2/3 and 5 of the cerebral cortex in awake mice in vivo. The present study demonstrated the proof-of-concept of dual-plane two-photon imaging of neural circuits by using the temporally multiplexed SLM-based microscope. The temporally multiplexed holographic microscope, combined with in vivo labeling with genetically encoded probes, enabled 4-D imaging and analysis of neural activities at cellular resolution and physiological timescales. Large-scale 4-D imaging and analysis will facilitate studies of not only the nervous system but also of various biological systems.

Systems Neuroscience of Natural Behaviors in Rodents

Animals evolved in complex environments, producing a wide range of behaviors, including navigation, foraging, prey capture, and conspecific interactions, which vary over timescales ranging from milliseconds to days. Historically, these behaviors have been the focus of study for ecology and ethology, while systems neuroscience has largely focused on short timescale behaviors that can be repeated thousands of times and occur in highly artificial environments. Thanks to recent advances in machine learning, miniaturization, and computation, it is newly possible to study freely moving animals in more natural conditions while applying systems techniques: performing temporally specific perturbations, modeling behavioral strategies, and recording from large numbers of neurons while animals are freely moving. The authors of this review are a group of scientists with deep appreciation for the common aims of systems neuroscience, ecology, and ethology. We believe it is an extremely exciting time to be a neuroscientist, as we have an opportunity to grow as a field, to embrace interdisciplinary, open, collaborative research to provide new insights and allow researchers to link knowledge across disciplines, species, and scales. Here we discuss the origins of ethology, ecology, and systems neuroscience in the context of our own work and highlight how combining approaches across these fields has provided fresh insights into our research. We hope this review facilitates some of these interactions and alliances and helps us all do even better science, together.

Movement-Related Signals in Sensory Areas: Roles in Natural Behavior

Recent studies have demonstrated prominent and widespread movement-related signals in the brain of head-fixed mice, even in primary sensory areas. However, it is still unknown what role these signals play in sensory processing. Why are these sensory areas 'contaminated' by movement signals? During natural behavior, animals actively acquire sensory information as they move through the environment and use this information to guide ongoing actions. In this context, movement-related signals could allow sensory systems to predict self-induced sensory changes and extract additional information about the environment. In this review we summarize recent findings on the presence of movement-related signals in sensory areas and discuss how their study, in the context of natural freely moving behaviors, could advance models of sensory processing.

Two Distinct Types of Eye-Head Coupling in Freely Moving Mice

Animals actively interact with their environment to gather sensory information. There is conflicting evidence about how mice use vision to sample their environment. During head restraint, mice make rapid eye movements coupled between the eyes, similar to conjugate saccadic eye movements in humans. However, when mice are free to move their heads, eye movements are more complex and often non-conjugate, with the eyes moving in opposite directions. We combined head and eye tracking in freely moving mice and found both observations are explained by two eye-head coupling types, associated with vestibular mechanisms. The first type comprised non-conjugate eye movements, which compensate for head tilt changes to maintain a similar visual field relative to the horizontal ground plane. The second type of eye movements was conjugate and coupled to head yaw rotation to produce a "saccade and fixate" gaze pattern. During head-initiated saccades, the eyes moved together in the head direction but during subsequent fixation moved in the opposite direction to the head to compensate for head rotation. This saccade and fixate pattern is similar to humans who use eye movements (with or without head movement) to rapidly shift gaze but in mice relies on combined head and eye movements. Both couplings were maintained during social interactions and visually guided object tracking. Even in head-restrained mice, eye movements were invariably associated with attempted head motion. Our results reveal that mice combine head and eye movements to sample their environment and highlight similarities and differences between eye movements in mice and humans.

Neurophysiological coding of space and time in the hippocampus, entorhinal cortex, and retrosplenial cortex

Neurophysiological recordings in behaving rodents demonstrate neuronal response properties that may code space and time for episodic memory and goal-directed behaviour. Here, we review recordings from hippocampus, entorhinal cortex, and retrosplenial cortex to address the problem of how neurons encode multiple overlapping spatiotemporal trajectories and disambiguate these for accurate memory-guided behaviour. The solution could involve neurons in the entorhinal cortex and hippocampus that show mixed selectivity, coding both time and location. Some grid cells and place cells that code space also respond selectively as time cells, allowing differentiation of time intervals when a rat runs in the same location during a delay period. Cells in these regions also develop new representations that differentially code the context of prior or future behaviour allowing disambiguation of overlapping trajectories. Spiking activity is also modulated by running speed and head direction, supporting the coding of episodic memory not as a series of snapshots but as a trajectory that can also be distinguished on the basis of speed and direction. Recent data also address the mechanisms by which sensory input could distinguish different spatial locations. Changes in firing rate reflect running speed on long but not short time intervals, and few cells code movement direction, arguing against path integration for coding location. Instead, new evidence for neural coding of environmental boundaries in egocentric coordinates fits with a modelling framework in which egocentric coding of barriers combined with head direction generates distinct allocentric coding of location. The egocentric input can be used both for coding the location of spatiotemporal trajectories and for retrieving specific viewpoints of the environment. Overall, these different patterns of neural activity can be used for encoding and disambiguation of prior episodic spatiotemporal trajectories or for planning of future goal-directed spatiotemporal trajectories.