Barcoding of episodic memories in the hippocampus of a food-caching bird.

The hippocampus is critical for episodic memory. Although hippocampal activity represents place and other behaviorally relevant variables, it is unclear how it encodes numerous memories of specific events in life. To study episodic coding, we leveraged the specialized behavior of chickadees-food-caching birds that form memories at well-defined moments in time whenever they cache food for subsequent retrieval. Our recordings during caching revealed very sparse, transient barcode-like patterns of firing across hippocampal neurons. Each "barcode" uniquely represented a caching event and transiently reactivated during the retrieval of that specific cache. Barcodes co-occurred with the conventional activity of place cells but were uncorrelated even for nearby cache locations that had similar place codes. We propose that animals recall episodic memories by reactivating hippocampal barcodes. Similarly to computer hash codes, these patterns assign unique identifiers to different events and could be a mechanism for rapid formation and storage of many non-interfering memories.

Dynamic Reorganization of Neuronal Activity Patterns in Parietal Cortex.

Neuronal representations change as associations are learned between sensory stimuli and behavioral actions. However, it is poorly understood whether representations for learned associations stabilize in cortical association areas or continue to change following learning. We tracked the activity of posterior parietal cortex neurons for a month as mice stably performed a virtual-navigation task. The relationship between cells' activity and task features was mostly stable on single days but underwent major reorganization over weeks. The neurons informative about task features (trial type and maze locations) changed across days. Despite changes in individual cells, the population activity had statistically similar properties each day and stable information for over a week. As mice learned additional associations, new activity patterns emerged in the neurons used for existing representations without greatly affecting the rate of change of these representations. We propose that dynamic neuronal activity patterns could balance plasticity for learning and stability for memory.

Single-neuron perturbations reveal feature-specific competition in V1.

The computations performed by local neural populations, such as a cortical layer, are typically inferred from anatomical connectivity and observations of neural activity. Here we describe a method-influence mapping-that uses single-neuron perturbations to directly measure how cortical neurons reshape sensory representations. In layer 2/3 of the primary visual cortex (V1), we use two-photon optogenetics to trigger action potentials in a targeted neuron and calcium imaging to measure the effect on spiking in neighbouring neurons in awake mice viewing visual stimuli. Excitatory neurons on average suppressed other neurons and had a centre-surround influence profile over anatomical space. A neuron's influence on its neighbour depended on their similarity in activity. Notably, neurons suppressed activity in similarly tuned neurons more than in dissimilarly tuned neurons. In addition, photostimulation reduced the population response, specifically to the targeted neuron's preferred stimulus, by around 2%. Therefore, V1 layer 2/3 performed feature competition, in which a like-suppresses-like motif reduces redundancy in population activity and may assist with inference of the features that underlie sensory input. We anticipate that influence mapping can be extended to investigate computations in other neural populations.

Geometric deep learning enables 3D kinematic profiling across species and environments.

Comprehensive descriptions of animal behavior require precise three-dimensional (3D) measurements of whole-body movements. Although two-dimensional approaches can track visible landmarks in restrictive environments, performance drops in freely moving animals, due to occlusions and appearance changes. Therefore, we designed DANNCE to robustly track anatomical landmarks in 3D across species and behaviors. DANNCE uses projective geometry to construct inputs to a convolutional neural network that leverages learned 3D geometric reasoning. We trained and benchmarked DANNCE using a dataset of nearly seven million frames that relates color videos and rodent 3D poses. In rats and mice, DANNCE robustly tracked dozens of landmarks on the head, trunk, and limbs of freely moving animals in naturalistic settings. We extended DANNCE to datasets from rat pups, marmosets, and chickadees, and demonstrate quantitative profiling of behavioral lineage during development.

Cerebellar-dependent expression of motor learning during eyeblink conditioning in head-fixed mice.

Eyeblink conditioning in restrained rabbits has served as an excellent model of cerebellar-dependent motor learning for many decades. In mice, the role of the cerebellum in eyeblink conditioning is less clear and remains controversial, partly because learning appears to engage fear-related circuits and lesions of the cerebellum do not abolish the learned behavior completely. Furthermore, experiments in mice are performed using freely moving systems, which lack the stability necessary for mapping out the essential neural circuitry with electrophysiological approaches. We have developed a novel apparatus for eyeblink conditioning in head-fixed mice. Here, we show that the performance of mice in our apparatus is excellent and that the learned behavior displays two hallmark features of cerebellar-dependent eyeblink conditioning in rabbits: (1) gradual acquisition; and (2) adaptive timing of conditioned movements. Furthermore, we use a combination of pharmacological inactivation, electrical stimulation, single-unit recordings, and targeted microlesions to demonstrate that the learned behavior is completely dependent on the cerebellum and to pinpoint the exact location in the deep cerebellar nuclei that is necessary. Our results pave the way for using eyeblink conditioning in head-fixed mice as a platform for applying next-generation genetic tools to address molecular and circuit-level questions about cerebellar function in health and disease.

The influence of cortical activity on perception depends on behavioral state and sensory context.

The mechanistic link between neural circuit activity and behavior remains unclear. While manipulating cortical activity can bias certain behaviors and elicit artificial percepts, some tasks can still be solved when cortex is silenced or removed. Here, mice were trained to perform a visual detection task during which we selectively targeted groups of visually responsive and co-tuned neurons in L2/3 of primary visual cortex (V1) for two-photon photostimulation. The influence of photostimulation was conditional on two key factors: the behavioral state of the animal and the contrast of the visual stimulus. The detection of low-contrast stimuli was enhanced by photostimulation, while the detection of high-contrast stimuli was suppressed, but crucially, only when mice were highly engaged in the task. When mice were less engaged, our manipulations of cortical activity had no effect on behavior. The behavioral changes were linked to specific changes in neuronal activity. The responses of non-photostimulated neurons in the local network were also conditional on two factors: their functional similarity to the photostimulated neurons and the contrast of the visual stimulus. Functionally similar neurons were increasingly suppressed by photostimulation with increasing visual stimulus contrast, correlating with the change in behavior. Our results show that the influence of cortical activity on perception is not fixed, but dynamically and contextually modulated by behavioral state, ongoing activity and the routing of information through specific circuits.

Barcoding of episodic memories in the hippocampus of a food-caching bird.

Episodic memory, or memory of experienced events, is a critical function of the hippocampus1-3. It is therefore important to understand how hippocampal activity represents specific events in an animal's life. We addressed this question in chickadees - specialist food-caching birds that hide food at scattered locations and use memory to find their caches later in time4,5. We performed high-density neural recordings in the hippocampus of chickadees as they cached and retrieved seeds in a laboratory arena. We found that each caching event was represented by a burst of firing in a unique set of hippocampal neurons. These 'barcode-like' patterns of activity were sparse (<10% of neurons active), uncorrelated even for immediately adjacent caches, and different even for separate caches at the same location. The barcode representing a specific caching event was transiently reactivated whenever a bird later interacted with the same cache - for example, to retrieve food. Barcodes co-occurred with conventional place cell activity6,7, as well as location-independent responses to cached seeds. We propose that barcodes are signatures of episodic memories evoked during memory recall. These patterns assign a unique identifier to each event and may be a mechanism for rapid formation and storage of many non-interfering memories.

Shared and specialized coding across posterior cortical areas for dynamic navigation decisions.

Animals adaptively integrate sensation, planning, and action to navigate toward goal locations in ever-changing environments, but the functional organization of cortex supporting these processes remains unclear. We characterized encoding in approximately 90,000 neurons across the mouse posterior cortex during a virtual navigation task with rule switching. The encoding of task and behavioral variables was highly distributed across cortical areas but differed in magnitude, resulting in three spatial gradients for visual cue, spatial position plus dynamics of choice formation, and locomotion, with peaks respectively in visual, retrosplenial, and parietal cortices. Surprisingly, the conjunctive encoding of these variables in single neurons was similar throughout the posterior cortex, creating high-dimensional representations in all areas instead of revealing computations specialized for each area. We propose that, for guiding navigation decisions, the posterior cortex operates in parallel rather than hierarchically, and collectively generates a state representation of the behavior and environment, with each area specialized in handling distinct information modalities.

Cognitive experience alters cortical involvement in goal-directed navigation.

Neural activity in the mammalian cortex has been studied extensively during decision tasks, and recent work aims to identify under what conditions cortex is actually necessary for these tasks. We discovered that mice with distinct cognitive experiences, beyond sensory and motor learning, use different cortical areas and neural activity patterns to solve the same navigation decision task, revealing past learning as a critical determinant of whether cortex is necessary for goal-directed navigation. We used optogenetics and calcium imaging to study the necessity and neural activity of multiple cortical areas in mice with different training histories. Posterior parietal cortex and retrosplenial cortex were mostly dispensable for accurate performance of a simple navigation task. In contrast, these areas were essential for the same simple task when mice were previously trained on complex tasks with delay periods or association switches. Multiarea calcium imaging showed that, in mice with complex-task experience, single-neuron activity had higher selectivity and neuron-neuron correlations were weaker, leading to codes with higher task information. Therefore, past experience is a key factor in determining whether cortical areas have a causal role in goal-directed navigation.

Scaling of sensory information in large neural populations shows signatures of information-limiting correlations.

How is information distributed across large neuronal populations within a given brain area? Information may be distributed roughly evenly across neuronal populations, so that total information scales linearly with the number of recorded neurons. Alternatively, the neural code might be highly redundant, meaning that total information saturates. Here we investigate how sensory information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex. We show that information scales sublinearly due to correlated noise in these populations. We compartmentalized noise correlations into information-limiting and nonlimiting components, then extrapolate to predict how information grows with even larger neural populations. We predict that tens of thousands of neurons encode 95% of the information about visual stimulus direction, much less than the number of neurons in primary visual cortex. These findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most sensory information from smaller subpopulations.

HNCcorr: A Novel Combinatorial Approach for Cell Identification in Calcium-Imaging Movies.

Calcium imaging is a key method in neuroscience for investigating patterns of neuronal activity in vivo. Still, existing algorithms to detect and extract activity signals from calcium-imaging movies have major shortcomings. We introduce the HNCcorr algorithm for cell identification in calcium-imaging datasets that addresses these shortcomings. HNCcorr relies on the combinatorial clustering problem HNC (Hochbaum's Normalized Cut), which is similar to the Normalized Cut problem of Shi and Malik, a well known problem in image segmentation. HNC identifies cells as coherent clusters of pixels that are highly distinct from the remaining pixels. HNCcorr guarantees a globally optimal solution to the underlying optimization problem as well as minimal dependence on initialization techniques. HNCcorr also uses a new method, called "similarity squared", for measuring similarity between pixels in calcium-imaging movies. The effectiveness of HNCcorr is demonstrated by its top performance on the Neurofinder cell identification benchmark. We believe HNCcorr is an important addition to the toolbox for analysis of calcium-imaging movies.

Silk Fibroin Films Facilitate Single-Step Targeted Expression of Optogenetic Proteins.

Optical methods of interrogating neural circuits have emerged as powerful tools for understanding how the brain drives behaviors. Optogenetic proteins are widely used to control neuronal activity, while genetically encoded fluorescent reporters are used to monitor activity. These proteins are often expressed by injecting viruses, which frequently leads to inconsistent experiments due to misalignment of expression and optical components. Here, we describe how silk fibroin films simplify optogenetic experiments by providing targeted delivery of viruses. Films composed of silk fibroin and virus are applied to the surface of implantable optical components. After surgery, silk releases the virus to transduce nearby cells and provide localized expression around optical fibers and endoscopes. Silk films can also be used to express genetically encoded sensors in large cortical regions by using cranial windows coated with a silk/virus mixture. The ease of use and improved performance provided by silk make this a promising approach for optogenetic studies.

Adaptive timing of motor output in the mouse: the role of movement oscillations in eyelid conditioning.

To survive, animals must learn to control their movements with millisecond-level precision, and adjust the kinematics if conditions, or task requirements, change. Here, we examine adaptive timing of motor output in mice, using a simple eyelid conditioning task. Mice were trained to blink in response to a light stimulus that was always followed by a corneal air-puff at a constant time interval. Different mice were trained with different intervals of time separating the onset of the light and the air-puff. As in previous work in other animal species, mice learned to control the speed of the blink, such that the time of maximum eyelid closure matched the interval used during training. However, we found that the time of maximum eyelid speed was always in the first 100 ms after movement onset and did not scale with the training interval, indicating that adaptive timing is not accomplished by slowing down (or speeding up) the eyelid movement uniformly throughout the duration of the blink. A new analysis, specifically designed to examine the kinematics of blinks in single trials, revealed that the underlying control signal responsible for the eyelid movement is made up of oscillatory bursts that are time-locked to the light stimulus at the beginning of the blink, becoming desynchronized later on. Furthermore, mice learn to blink at different speeds and time the movement appropriately by adjusting the amplitude, but not the frequency of the bursts in the eyelid oscillation.