Insects have evolved sophisticated reflexes to right themselves in mid-air. Their recovery mechanisms involve complex interactions among the physical senses, muscles, body, and wings, and they must obey the laws of flight. We sought to understand the key mechanisms involved in dragonfly righting reflexes and to develop physics-based models for understanding the control strategies of flight maneuvers. Using kinematic analyses, physical modeling, and three-dimensional flight simulations, we found that a dragonfly uses left-right wing pitch asymmetry to roll its body 180 degrees to recover from falling upside down in ~200 milliseconds. Experiments of dragonflies with blocked vision further revealed that this rolling maneuver is initiated by their ocelli and compound eyes. These results suggest a pathway from the dragonfly's visual system to the muscles regulating wing pitch that underly the recovery. The methods developed here offer quantitative tools for inferring insects' internal actions from their acrobatics, and are applicable to a broad class of natural and robotic flying systems.

Neural computation involves diverse types of GABAergic inhibitory interneurons that are integrated with excitatory (E) neurons into precisely structured circuits. To understand how each neuron type shapes sensory representations, we measured firing patterns of defined types of neurons in the barrel cortex while mice performed an active, whisker-dependent object localization task. Touch excited fast-spiking (FS) interneurons at short latency, followed by activation of E neurons and somatostatin-expressing (SST) interneurons. Touch only weakly modulated vasoactive intestinal polypeptide-expressing (VIP) interneurons. Voluntary whisker movement activated FS neurons in the ventral posteromedial nucleus (VPM) target layers, a subset of SST neurons and a majority of VIP neurons. Together, FS neurons track thalamic input, mediating feedforward inhibition. SST neurons monitor local excitation, providing feedback inhibition. VIP neurons are activated by non-sensory inputs, disinhibiting E and FS neurons. Our data reveal rules of recruitment for interneuron types during behavior, providing foundations for understanding computation in cortical microcircuits.

Dopaminergic neurons (DANs) drive learning across the animal kingdom, but the upstream circuits that regulate their activity and thereby learning remain poorly understood. We provide a synaptic-resolution connectome of the circuitry upstream of all DANs in a learning center, the mushroom body of Drosophila larva. We discover afferent sensory pathways and a large population of neurons that provide feedback from mushroom body output neurons and link distinct memory systems (aversive and appetitive). We combine this with functional studies of DANs and their presynaptic partners and with comprehensive circuit modeling. We find that DANs compare convergent feedback from aversive and appetitive systems, which enables the computation of integrated predictions that may improve future learning. Computational modeling reveals that the discovered feedback motifs increase model flexibility and performance on learning tasks. Our study provides the most detailed view to date of biological circuit motifs that support associative learning.

Most cortical synapses are local and excitatory. Local recurrent circuits could implement amplification, allowing pattern completion and other computations. Cortical circuits contain subnetworks that consist of neurons with similar receptive fields and increased connectivity relative to the network average. Cortical neurons that encode different types of information are spatially intermingled and distributed over large brain volumes, and this complexity has hindered attempts to probe the function of these subnetworks by perturbing them individually. Here we use computational modelling, optical recordings and manipulations to probe the function of recurrent coupling in layer 2/3 of the mouse vibrissal somatosensory cortex during active tactile discrimination. A neural circuit model of layer 2/3 revealed that recurrent excitation enhances sensory signals by amplification, but only for subnetworks with increased connectivity. Model networks with high amplification were sensitive to damage: loss of a few members of the subnetwork degraded stimulus encoding. We tested this prediction by mapping neuronal selectivity and photoablating neurons with specific selectivity. Ablation of a small proportion of layer 2/3 neurons (10-20, less than 5% of the total) representing touch markedly reduced responses in the spared touch representation, but not in other representations. Ablations most strongly affected neurons with stimulus responses that were similar to those of the ablated population, which is also consistent with network models. Recurrence among cortical neurons with similar selectivity therefore drives input-specific amplification during behaviour.

Population neural recordings with long-range temporal structure are often best understood in terms of a shared underlying low-dimensional dynamical process. Advances in recording technology provide access to an ever larger fraction of the population, but the standard computational approaches available to identify the collective dynamics scale poorly with the size of the dataset. Here we describe a new, scalable approach to discovering the low-dimensional dynamics that underlie simultaneously recorded spike trains from a neural population. Our method is based on recurrent linear models (RLMs), and relates closely to timeseries models based on recurrent neural networks. We formulate RLMs for neural data by generalising the Kalman-filter-based likelihood calculation for latent linear dynamical systems (LDS) models to incorporate a generalised-linear observation process. We show that RLMs describe motor-cortical population data better than either directly-coupled generalised-linear models or latent linear dynamical system models with generalised-linear observations. We also introduce the cascaded linear model (CLM) to capture low-dimensional instantaneous correlations in neural populations. The CLM describes the cortical recordings better than either Ising or Gaussian models and, like the RLM, can be fit exactly and quickly. The CLM can also be seen as a generalization of a low-rank Gaussian model, in this case factor analysis. The computational tractability of the RLM and CLM allow both to scale to very high-dimensional neural data.

Traditional approaches for increasing the affinity of protein-protein complexes focus on constructing highly complementary binding surfaces. Recent theoretical simulations and experimental results suggest that electrostatic steering forces can also be manipulated to increase association rates while leaving dissociation rates unchanged, thus increasing affinity. Here we demonstrate that electrostatic attraction can be enhanced between an antibody fragment and its cognate antigen through application of a few simple rules to identify potential on-rate amplification sites that lie at the periphery of the antigen-antibody interface.

We developed a series of statistical potentials to recognize the native protein from decoys, particularly when using only a reduced representation in which each side chain is treated as a single C(beta) atom. Beginning with a highly successful all-atom statistical potential, the Discrete Optimized Protein Energy function (DOPE), we considered the implications of including additional information in the all-atom statistical potential and subsequently reducing to the C(beta) representation. One of the potentials includes interaction energies conditional on backbone geometries. A second potential separates sequence local from sequence nonlocal interactions and introduces a novel reference state for the sequence local interactions. The resultant potentials perform better than the original DOPE statistical potential in decoy identification. Moreover, even upon passing to a reduced C(beta) representation, these statistical potentials outscore the original (all-atom) DOPE potential in identifying native states for sets of decoys. Interestingly, the backbone-dependent statistical potential is shown to retain nearly all of the information content of the all-atom representation in the C(beta) representation. In addition, these new statistical potentials are combined with existing potentials to model hydrogen bonding, torsion energies, and solvation energies to produce even better performing potentials. The ability of the C(beta) statistical potentials to accurately represent protein interactions bodes well for computational efficiency in protein folding calculations using reduced backbone representations, while the extensions to DOPE illustrate general principles for improving knowledge-based potentials.

Endocytic recycling of synaptic vesicles after exocytosis is critical for nervous system function. At synapses of cultured neurons that lack the two "neuronal" dynamins, dynamin 1 and 3, smaller excitatory postsynaptic currents are observed due to an impairment of the fission reaction of endocytosis that results in an accumulation of arrested clathrin-coated pits and a greatly reduced synaptic vesicle number. Surprisingly, despite a smaller readily releasable vesicle pool and fewer docked vesicles, a strong facilitation, which correlated with lower vesicle release probability, was observed upon action potential stimulation at such synapses. Furthermore, although network activity in mutant cultures was lower, Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) activity was unexpectedly increased, consistent with the previous report of an enhanced state of synapsin 1 phosphorylation at CaMKII-dependent sites in such neurons. These changes were partially reversed by overnight silencing of synaptic activity with tetrodotoxin, a treatment that allows progression of arrested endocytic pits to synaptic vesicles. Facilitation was also counteracted by CaMKII inhibition. These findings reveal a mechanism aimed at preventing synaptic transmission failure due to vesicle depletion when recycling vesicle traffic is backed up by a defect in dynamin-dependent endocytosis and provide new insight into the coupling between endocytosis and exocytosis.

The Ssn6-Tup1 corepressor complex regulates many genes in Saccharomyces cerevisiae. Three mechanisms have been proposed to explain its repression functions: 1) nucleosome positioning by binding histone tails; 2) recruitment of histone deacetylases; and 3) direct interference with the general transcription machinery or activators. It is unclear if Ssn6-Tup1 utilizes each of these mechanisms at a single gene in a redundant manner or each individually at different loci. A systematic analysis of the contribution of each mechanism at a native promoter has not been reported. Here we employed a genetic strategy to analyze the contributions of nucleosome positioning, histone deacetylation, and Mediator interference in the repression of chromosomal Tup1 target genes in vivo. We exploited the fact that Ssn6-Tup1 requires the ISW2 chromatin remodeling complex to establish nucleosome positioning in vivo to disrupt chromatin structure without affecting other Tup1 repression functions. Deleting ISW2, the histone deacetylase gene HDA1, or genes encoding Mediator subunits individually caused slight or no derepression of RNR3 and HUG1. However, when Mediator mutations were combined with Deltaisw2 or Deltahda1 mutations, enhanced transcription was observed, and the strongest level of derepression was observed in triple Deltaisw2/Deltahda1/Mediator mutants. The increased transcription in the mutants was not due to the loss of Tup1 at the promoter and correlated with increased TBP cross-linking to promoters. Thus, Tup1 utilizes multiple redundant mechanisms to repress transcription of native genes, which may be important for it to act as a global corepressor at a wide variety of promoters.

Regulated gene expression is a complex process achieved through the function of multiple protein factors acting in concert at a given promoter. The transcription factor TFIID is a central component of the machinery regulating mRNA synthesis by RNA polymerase II. This large multiprotein complex is composed of the TATA box binding protein (TBP) and several TBP-associated factors (TAF(II)s). The recent discovery of multiple TBP-related factors and tissue-specific TAF(II)s suggests the existence of specialized TFIID complexes that likely play a critical role in regulating transcription in a gene- and tissue-specific manner. The tissue-selective factor TAF(II)105 was originally identified as a component of TFIID derived from a human B-cell line. In this report we demonstrate the specific induction of TAF(II)105 in cultured B cells in response to bacterial lipopolysaccharide (LPS). To examine the in vivo role of TAF(II)105, we have generated TAF(II)105-null mice by homologous recombination. Here we show that B-lymphocyte development is largely unaffected by the absence of TAF(II)105. TAF(II)105-null B cells can proliferate in response to LPS, produce relatively normal levels of resting antibodies, and can mount an immune response by producing antigen-specific antibodies in response to immunization. Taken together, we conclude that the function of TAF(II)105 in B cells is likely redundant with the function of other TAF(II)105-related cellular proteins.