The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems.

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.

Olfactory memories can be very good-your mother's baking-or very bad-your father's cooking. We go through life forming these different associations with the smells we encounter. But what makes one association pleasant and another repulsive? Work in deep areas of the Drosophila brain has revealed the beginnings of an answer, as reported in this issue of Neuron by Owald et al. (2015).

Two simple models—vaulting over stiff legs and rebounding over compliant legs—are employed to describe the mechanics of legged locomotion. It is agreed that compliant legs are necessary for describing running, and that leg compliance is also present during walking. Stiff legs continue to be employed to model walking under the assumption that the compliance of the leg during walking is low enough to be considered stiff. Here we study gait choice and walk-to-run transition in a biped with compliance and show that the principles underlying gait choice and transition are completely different from stiff legs. Two findings underpin our conclusions: First, at the same speed, step length and stance duration, multiple gaits that differ in the number of times the leg expands and contracts during a single stance are possible. Among them, humans and other animals choose the (normal) gait with M-shaped vertical ground reaction forces (vGRF) not just because of energy considerations but also constraints from forces. Second, the transition from walking to running occurs because of three factors: vGRF minimum at mid-stance characteristic of normal walking, synchronization of horizontal and vertical motions during single support, and velocity redirection during the double support. The insight above required an analytical approximation of the double spring-loaded pendulum (DSLIP) model describing the intricate oscillatory dynamics that relate single and double support phases. Additionally, we also examined DSLIP as a quantitative model for locomotion and conclude that DSLIP speed range is limited. However, insights gleaned from the analytical treatment of DSLIP are general and will inform the construction of more accurate models of walking.

bioRxiv preprint: https://doi.org/10.1101/2024.09.23.612940

Leptin is an adipose tissue hormone that maintains homeostatic control of adipose tissue mass by regulating the activity of specific neural populations controlling appetite and metabolism1. Leptin regulates food intake by inhibiting orexigenic agouti-related protein (AGRP) neurons and activating anorexigenic pro-opiomelanocortin (POMC) neurons2. However, while AGRP neurons regulate food intake on a rapid time scale, acute activation of POMC neurons has only a minimal effect3–5. This has raised the possibility that there is a heretofore unidentified leptin-regulated neural population that suppresses appetite on a rapid time scale. Here, we report the discovery of a novel population of leptin-target neurons expressing basonuclin 2 (Bnc2) that acutely suppress appetite by directly inhibiting AGRP neurons. Opposite to the effect of AGRP activation, BNC2 neuronal activation elicited a place preference indicative of positive valence in hungry but not fed mice. The activity of BNC2 neurons is finely tuned by leptin, sensory food cues, and nutritional status. Finally, deleting leptin receptors in BNC2 neurons caused marked hyperphagia and obesity, similar to that observed in a leptin receptor knockout in AGRP neurons. These data indicate that BNC2-expressing neurons are a key component of the neural circuit that maintains energy balance, thus filling an important gap in our understanding of the regulation of food intake and leptin action.

Obesity is associated with increased blood pressure (BP), which in turn increases the risk of cardiovascular diseases. We found that the increase in leptin levels seen in diet-induced obesity (DIO) drives an increase in BP in rodents, an effect that was not seen in animals deficient in leptin or leptin receptors (LepR). Furthermore, humans with loss-of-function mutations in leptin and the LepR have low BP despite severe obesity. Leptin's effects on BP are mediated by neuronal circuits in the dorsomedial hypothalamus (DMH), as blocking leptin with a specific antibody, antagonist, or inhibition of the activity of LepR-expressing neurons in the DMH caused a rapid reduction of BP in DIO mice, independent of changes in weight. Re-expression of LepRs in the DMH of DIO LepR-deficient mice caused an increase in BP. These studies demonstrate that leptin couples changes in weight to changes in BP in mammalian species.

The central actions of leptin are essential for homeostatic control of adipose tissue mass, glucose metabolism, and many autonomic and neuroendocrine systems. In the brain, leptin acts on numerous different cell types via the long-form leptin receptor (LepRb) to elicit its effects. The precise identification of leptin’s cellular targets is fundamental to understanding the mechanism of its pleiotropic central actions. We have systematically characterized LepRb distribution in the mouse brain using in situ hybridization in wildtype mice as well as by EYFP immunoreactivity in a novel LepRb-IRES-Cre EYFP reporter mouse line showing high levels of LepRb mRNA/EYFP coexpression. We found substantial LepRb mRNA and EYFP expression in hypothalamic and extrahypothalamic sites described before, including the dorsomedial nucleus of the hypothalamus, ventral premammillary nucleus, ventral tegmental area, parabrachial nucleus, and the dorsal vagal complex. Expression in insular cortex, lateral septal nucleus, medial preoptic area, rostral linear nucleus, and in the Edinger-Westphal nucleus was also observed and had been previously unreported. The LepRb-IRES-Cre reporter line was used to chemically characterize a population of leptin receptor-expressing neurons in the midbrain. Tyrosine hydroxylase and Cre reporter were found to be coexpressed in the ventral tegmental area and in other midbrain dopaminergic neurons. Lastly, the LepRb-IRES-Cre reporter line was used to map the extent of peripheral leptin sensing by central nervous system (CNS) LepRb neurons. Thus, we provide data supporting the use of the LepRb-IRES-Cre line for the assessment of the anatomic and functional characteristics of neurons expressing leptin receptor.

Although the vinegar fly, Drosophila melanogaster, has been a biological model organism for over a century, its emergence as a model system for the study of neurophysiology is comparatively recent. The primary reason for this is that the vinegar fly and its neurons are tiny; up until 5 years ago, it was prohibitively difficult to record intracellularly from individual neurons in the intact Drosophila brain (Wilson et al., 2004). Today, fly electrophysiologists can genetically label neurons with GFP and reliably record from many (but not all) neurons in the fruit fly brain. Using genetic tools to drive expression of fluorescent calcium indicators, light-sensitive ion channels, or cell activity suppressors, we are beginning to understand how the external environment is represented with electrical potentials in Drosophila neurons (for review, see Olsen and Wilson, 2008).

Natural neural circuits, optimized by millions of years of evolution, are fast, low power, robust, and adapt in response to experience, all characteristics we would love to have in systems we ourselves design. Recently there have been enormous advances in understanding how neurons implement computations within the brain of living creatures. Can we use this new-found knowledge to create better artificial system? What lessons can we learn from the neurons themselves, that can help us create better neuromorphic circuits?

Upon inflammation, leukocytes extravasate through endothelial cells. When they extravasate, it is generally accepted that neighboring endothelial cells disconnect. Careful examination of endothelial junctions showed a partial membrane overlap beyond VE-cadherin distribution. These overlaps are regulated by actin polymerization and, although marked by, do not require PECAM-1, nor VE-cadherin. Neutrophils prefer wider membrane overlaps as exit sites. Detailed 3D analysis of neutrophil transmigration in real time at high spatiotemporal resolution revealed that overlapping endothelial membranes form a tunnel during neutrophil transmigration. These tunnels are formed by the neutrophil lifting the membrane of the upper endothelial cell while indenting and crawling over the membrane of the underlying endothelial cell. Our work shows that endothelial cells do not simply retract upon the passage of neutrophils but provide membrane tunnels, allowing neutrophils to extravasate. This discovery defines the 3D multicellular architecture in which the paracellular transmigration of neutrophils occurs.