Cortical neurons form specific circuits, but the functional structure of this microarchitecture and its relation to behaviour are poorly understood. Two-photon calcium imaging can monitor activity of spatially defined neuronal ensembles in the mammalian cortex. Here we applied this technique to the motor cortex of mice performing a choice behaviour. Head-fixed mice were trained to lick in response to one of two odours, and to withhold licking for the other odour. Mice routinely showed significant learning within the first behavioural session and across sessions. Microstimulation and trans-synaptic tracing identified two non-overlapping candidate tongue motor cortical areas. Inactivating either area impaired voluntary licking. Imaging in layer 2/3 showed neurons with diverse response types in both areas. Activity in approximately half of the imaged neurons distinguished trial types associated with different actions. Many neurons showed modulation coinciding with or preceding the action, consistent with their involvement in motor control. Neurons with different response types were spatially intermingled. Nearby neurons (within approximately 150 mum) showed pronounced coincident activity. These temporal correlations increased with learning within and across behavioural sessions, specifically for neuron pairs with similar response types. We propose that correlated activity in specific ensembles of functionally related neurons is a signature of learning-related circuit plasticity. Our findings reveal a fine-scale and dynamic organization of the frontal cortex that probably underlies flexible behaviour.

SUMMARY Animals generate movement by engaging spinal circuits that direct precise sequences of muscle contraction, but the identity and organizational logic of local interneurons that lie at the core of these circuits remain unresolved. Here we show that V1 interneurons, a major inhibitory population that controls motor output, fractionate into highly diverse subsets on the basis of the expression of nineteen transcription factors. Transcriptionally defined V1 subsets exhibit distinct physiological signatures and highly structured spatial distributions with mediolateral and dorsoventral positional biases. These positional distinctions constrain patterns of input from sensory and motor neurons, arguing that interneuron position is a determinant of microcircuit organization. Moreover, V1 diversity indicates that different inhibitory microcircuits exist for motor pools controlling hip, ankle, and foot muscles, revealing a variable circuit architecture for interneurons that control limb movement.

The taste system is one of our fundamental senses, responsible for detecting and responding to sweet, bitter, umami, salty and sour stimuli. In the tongue, the five basic tastes are mediated by separate classes of taste receptor cells each finely tuned to a single taste quality. Here, we explored the logic of taste coding in the brain by examining how sweet, bitter, umami and saltiness are represented in the primary taste cortex. Using in vivo two-photon calcium-imaging we demonstrated striking topographic segregation in the functional architecture of the gustatory cortex. Each taste quality is represented in its own separate cortical field, revealing the existence of a gustotopic map in the brain. These results expose the basic logic for the central representation of taste.

To the Editor -Methods for analyzing single-cell data 1-4 perform a core set of computational tasks. These tasks include dimensionality reduction, cell clustering, cell-state annotation, removal of unwanted variation, analysis of differential expression, identification of spatial patterns of gene expression, and joint analysis of multi-modal omics data. Many of these methods rely on likelihood-based models to represent variation in the data; we refer to these as 'probabilistic

The ability to jointly profile the transcriptional and chromatin land-scape of single-cells has emerged as a powerful technique to identify cellular populations and shed light on their regulation of gene expression. Current computational methods analyze jointly profiled (paired) or individual data modalities (unpaired), but do not offer a principled method to analyze both paired and unpaired samples jointly. Here we present MultiVI, a probabilistic framework that leverages deep neural networks to jointly analyze scRNA, scATAC and multiomic (scRNA + scATAC) data. MultiVI creates an informative low-dimensional latent space that accurately reflects both chromatin and transcriptional properties of the cells even when one of the modalities is missing. MultiVI accounts for technical effects in both scRNA and scATAC-seq while correcting for batch effects in both data modalities. We use public datasets to demonstrate that MultiVI is stable, easy to use, and outperforms current approaches for the joint analysis of paired and unpaired data. MultiVI is available as an open source package, implemented in the scvi-tools frame-work: https://docs.scvi-tools.org/.

Motor output varies along the rostro-caudal axis of the tetrapod spinal cord. At limb levels, ∼60 motor pools control the alternation of flexor and extensor muscles about each joint, whereas at thoracic levels as few as 10 motor pools supply muscle groups that support posture, inspiration, and expiration. Whether such differences in motor neuron identity and muscle number are associated with segmental distinctions in interneuron diversity has not been resolved. We show that select combinations of nineteen transcription factors that specify lumbar V1 inhibitory interneurons generate subpopulations enriched at limb and thoracic levels. Specification of limb and thoracic V1 interneurons involves the Hox gene Hoxc9 independently of motor neurons. Thus, early Hox patterning of the spinal cord determines the identity of V1 interneurons and motor neurons. These studies reveal a developmental program of V1 interneuron diversity, providing insight into the organization of inhibitory interneurons associated with differential motor output.

SUMMARY Documenting the extent of cellular diversity is a critical step in defining the functional organization of tissues and organs. To infer cell type diversity from partial or incomplete transcription factor expression data we devised a sparse Bayesian framework that is able to handle estimation uncertainty, and can incorporate diverse cellular characteristics to optimize experimental design. Focusing on spinal V1 inhibitory interneurons, for which the spatial expression of 19 transcription factors has been mapped, we infer the existence of approximately 50 candidate V1 neuronal types, many of which localize in compact spatial domains in the ventral spinal cord. We have validated the existence of inferred cell types by direct experimental measurement, establishing this Bayesian framework as an effective platform for cell type characterization in the nervous system and elsewhere.

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