Although anatomical, lesion, and imaging studies of the hippocampus indicate qualitatively different information processing along its septo-temporal axis, physiological mechanisms supporting such distinction are missing. We found fundamental differences between the dorsal (dCA3) and the ventral-most parts (vCA3) of the hippocampus in both environmental representation and temporal dynamics. Discrete place fields of dCA3 neurons evenly covered all parts of the testing environments. In contrast, vCA3 neurons (1) rarely showed continuous two-dimensional place fields, (2) differentiated open and closed arms of a radial maze, and (3) discharged similar firing patterns with respect to the goals, both on multiple arms of a radial maze and during opposite journeys in a zigzag maze. In addition, theta power and the fraction of theta-rhythmic neurons were substantially reduced in the ventral compared with dorsal hippocampus. We hypothesize that the spatial representation in the septo-temporal axis of the hippocampus is progressively decreased. This change is paralleled with a reduction of theta rhythm and an increased representation of nonspatial information.

Representation learning in neural networks may be implemented with supervised or unsupervised algorithms, distinguished by the availability of feedback. In sensory cortex, perceptual learning drives neural plasticity, but it is not known if this is due to supervised or unsupervised learning. Here we recorded populations of up to 90,000 neurons simultaneously from the primary visual cortex (V1) and higher visual areas (HVA), while mice learned multiple tasks as well as during unrewarded exposure to the same stimuli. Similar to previous studies, we found that neural changes in task mice were correlated with their behavioral learning. However, the neural changes were mostly replicated in mice with unrewarded exposure, suggesting that the changes were in fact due to unsupervised learning. The neural plasticity was concentrated in the medial HVAs and obeyed visual, rather than spatial, learning rules. In task mice only, we found a ramping reward prediction signal in anterior HVAs, potentially involved in supervised learning. Our neural results predict that unsupervised learning may accelerate subsequent task learning, a prediction which we validated with behavioral experiments.

Renal peritubular interstitial fibroblast-like cells are critical for adult erythropoiesis, as they are the main source of erythropoietin (EPO). Hypoxia-inducible factor 2 (HIF-2) controls EPO synthesis in the kidney and liver and is regulated by prolyl-4-hydroxylase domain (PHD) dioxygenases PHD1, PHD2, and PHD3, which function as cellular oxygen sensors. Renal interstitial cells with EPO-producing capacity are poorly characterized, and the role of the PHD/HIF-2 axis in renal EPO-producing cell (REPC) plasticity is unclear. Here we targeted the PHD/HIF-2/EPO axis in FOXD1 stroma-derived renal interstitial cells and examined the role of individual PHDs in REPC pool size regulation and renal EPO output. Renal interstitial cells with EPO-producing capacity were entirely derived from FOXD1-expressing stroma, and Phd2 inactivation alone induced renal Epo in a limited number of renal interstitial cells. EPO induction was submaximal, as hypoxia or pharmacologic PHD inhibition further increased the REPC fraction among Phd2-/- renal interstitial cells. Moreover, Phd1 and Phd3 were differentially expressed in renal interstitium, and heterozygous deficiency for Phd1 and Phd3 increased REPC numbers in Phd2-/- mice. We propose that FOXD1 lineage renal interstitial cells consist of distinct subpopulations that differ in their responsiveness to Phd2 inactivation and thus regulation of HIF-2 activity and EPO production under hypoxia or conditions of pharmacologic or genetic PHD inactivation.

The transition between dormant and active Mycobacterium tuberculosis infection requires reorganization of its lipid metabolism and activation of a battery of serine hydrolase enzymes. Among these serine hydrolases, Rv0045c is a mycobacterial-specific serine hydrolase with limited sequence homology outside mycobacteria but structural homology to divergent bacterial hydrolase families. Herein, we determined the global substrate specificity of Rv0045c against a library of fluorogenic hydrolase substrates, constructed a combined experimental and computational model for its binding pocket, and performed comprehensive substitutional analysis to develop a structural map of its binding pocket. Rv0045c showed strong substrate selectivity toward short, straight chain alkyl esters with the highest activity toward four atom chains. This strong substrate preference was maintained through the combined action of residues in a flexible loop connecting the cap and α/β hydrolase domains and in residues close to the catalytic triad. Two residues bracketing the substrate-binding pocket (Gly90 and His187) were essential to maintaining the narrow substrate selectivity of Rv0045c toward various acyl ester substituents, as independent conversion of these residues significantly increased its catalytic activity and broadened its substrate specificity. Focused saturation mutagenesis of position 187 implicated this residue, as the differentiation point between the substrate specificity of Rv0045c and the structurally homologous ybfF hydrolase family. Insertion of the analogous tyrosine residue from ybfF hydrolases into Rv0045c increased the catalytic activity of Rv0045 by over 20-fold toward diverse ester substrates. The unique binding pocket structure and selectivity of Rv0045c provide molecular indications of its biological role and evidence for expanded substrate diversity in serine hydrolases from M. tuberculosis.

A general method to recognize and track unmarked animals within a population will enable new studies of social behavior and individuality.

How do descending inputs from the brain control leg motor circuits to change how an animal walks? Conceptually, descending neurons are thought to function either as command-type neurons, in which a single type of descending neuron exerts a high-level control to elicit a coordinated change in motor output, or through a population coding mechanism, whereby a group of neurons, each with local effects, act in combination to elicit a global motor response. The Drosophila Moonwalker Descending Neurons (MDNs), which alter leg motor circuit dynamics so that the fly walks backwards, exemplify the command-type mechanism. Here, we identify several dozen MDN target neurons within the leg motor circuits, and show that two of them mediate distinct and highly-specific changes in leg muscle activity during backward walking: LBL40 neurons provide the hindleg power stroke during stance phase; LUL130 neurons lift the legs at the end of stance to initiate swing. Through these two effector neurons, MDN directly controls both the stance and swing phases of the backward stepping cycle. These findings suggest that command-type descending neurons can also operate through the distributed control of local motor circuits.

The brain exhibits rich oscillatory dynamics that vary across tasks and states, such as the EEG oscillations that define sleep. These oscillations play critical roles in cognition and arousal, but the brainwide mechanisms underlying them are not yet described. Using simultaneous EEG and fast fMRI in subjects drifting between sleep and wakefulness, we developed a machine learning approach to investigate which brainwide fMRI dynamics predict alpha (8-12 Hz) and delta (1-4 Hz) rhythms. We predicted moment-by-moment EEG power from fMRI activity in held-out subjects, and found that information about alpha power was represented by a remarkably small set of regions, segregated in two distinct networks linked to arousal and visual systems. Conversely, delta rhythms were diffusely represented on a large spatial scale across the cortex. These results identify distributed networks that predict delta and alpha rhythms, and establish a computational framework for investigating fMRI brainwide dynamics underlying EEG oscillations.

Survival behaviors are orchestrated by hardwired circuits located in deep subcortical brain regions, most prominently the hypothalamus. Artificial activation of spatially localized, genetically defined hypothalamic cell populations is known to trigger distinct behaviors, suggesting a nucleus-centered organization of behavioral control. However, no study has investigated the hypothalamic representation of innate behaviors using unbiased, large-scale single neuron recordings. Here, using custom silicon probes, we performed recordings across the rostro-caudal extent of the medial hypothalamus in freely moving animals engaged in a diverse array of social and predator defense (“fear”) behaviors. Nucleus-averaged activity revealed spatially distributed generic “ignition signals” that occurred at the onset of each behavior, and did not identify sparse, nucleus-specific behavioral representations. Single-unit analysis revealed that social and fear behavior classes are encoded by activity in distinct sets of spatially distributed neuronal ensembles spanning the entire hypothalamic rostro-caudal axis. Individual ensemble membership, however, was drawn from neurons in 3-4 adjacent nuclei. Mixed selectivity was identified as the most prevalent mode of behavior representation by individual hypothalamic neurons. Encoding models indicated that a significant fraction of the variance in single neuron activity is explained by behavior. This work reveals that innate behaviors are encoded in the hypothalamus by activity in spatially distributed neural ensembles that each span multiple neighboring nuclei, complementing the prevailing view of hypothalamic behavioral control by single nucleus-restricted cell types derived from perturbational studies.

Task-related neural activity is widespread across populations of neurons during goal-directed behaviors. However, little is known about the synaptic reorganization and circuit mechanisms that lead to broad activity changes. Here we trained a limited subset of neurons in a spiking network with strong synaptic interactions to reproduce the activity of neurons in the motor cortex during a decision-making task. We found that task-related activity, resembling the neural data, emerged across the network, even in the untrained neurons. Analysis of trained networks showed that strong untrained synapses, which were independent of the task and determined the dynamical state of the network, mediated the spread of task-related activity. Optogenetic perturbations suggest that the motor cortex is strongly-coupled, supporting the applicability of the mechanism to cortical networks. Our results reveal a cortical mechanism that facilitates distributed representations of task-variables by spreading the activity from a subset of plastic neurons to the entire network through task-independent strong synapses.

Task-related neural activity is widespread across populations of neurons during goal-directed behaviors. However, little is known about the synaptic reorganization and circuit mechanisms that lead to broad activity changes. Here we trained a subset of neurons in a spiking network with strong synaptic interactions to reproduce the activity of neurons in the motor cortex during a decision-making task. Task-related activity, resembling the neural data, emerged across the network, even in the untrained neurons. Analysis of trained networks showed that strong untrained synapses, which were independent of the task and determined the dynamical state of the network, mediated the spread of task-related activity. Optogenetic perturbations suggest that the motor cortex is strongly-coupled, supporting the applicability of the mechanism to cortical networks. Our results reveal a cortical mechanism that facilitates distributed representations of task-variables by spreading the activity from a subset of plastic neurons to the entire network through task-independent strong synapses.