Active dendrites provide neurons with powerful processing capabilities. However, little is known about the role of neuronal dendrites in behaviourally related circuit computations. Here we report that a novel global dendritic nonlinearity is involved in the integration of sensory and motor information within layer 5 pyramidal neurons during an active sensing behaviour. Layer 5 pyramidal neurons possess elaborate dendritic arborizations that receive functionally distinct inputs, each targeted to spatially separate regions. At the cellular level, coincident input from these segregated pathways initiates regenerative dendritic electrical events that produce bursts of action potential output and circuits featuring this powerful dendritic nonlinearity can implement computations based on input correlation. To examine this in vivo we recorded dendritic activity in layer 5 pyramidal neurons in the barrel cortex using two-photon calcium imaging in mice performing an object-localization task. Large-amplitude, global calcium signals were observed throughout the apical tuft dendrites when active touch occurred at particular object locations or whisker angles. Such global calcium signals are produced by dendritic plateau potentials that require both vibrissal sensory input and primary motor cortex activity. These data provide direct evidence of nonlinear dendritic processing of correlated sensory and motor information in the mammalian neocortex during active sensation.

A basic task faced by the visual system of many organisms is to accurately track the position of moving prey. The retina is the first stage in the processing of such stimuli; the nature of the transformation here, from photons to spike trains, constrains not only the ultimate fidelity of the tracking signal but also the ease with which it can be extracted by other brain regions. Here we demonstrate that a population of fast-OFF ganglion cells in the salamander retina, whose dynamics are governed by a nonlinear circuit, serve to compute the future position of the target over hundreds of milliseconds. The extrapolated position of the target is not found by stimulus reconstruction but is instead computed by a weighted sum of ganglion cell outputs, the population vector average (PVA). The magnitude of PVA extrapolation varies systematically with target size, speed, and acceleration, such that large targets are tracked most accurately at high speeds, and small targets at low speeds, just as is seen in the motion of real prey. Tracking precision reaches the resolution of single photoreceptors, and the PVA algorithm performs more robustly than several alternative algorithms. If the salamander brain uses the fast-OFF cell circuit for target extrapolation as we suggest, the circuit dynamics should leave a microstructure on the behavior that may be measured in future experiments. Our analysis highlights the utility of simple computations that, while not globally optimal, are efficiently implemented and have close to optimal performance over a limited but ethologically relevant range of stimuli.

Animals use information from multiple sensory organs to generate appropriate behavior. Exactly how these different sensory inputs are fused at the motor system is not well understood. Here we study how fly neck motor neurons integrate information from two well characterized sensory systems: visual information from the compound eye and gyroscopic information from the mechanosensory halteres. Extracellular recordings reveal that a subpopulation of neck motor neurons display "gating-like" behavior: they do not fire action potentials in response to visual stimuli alone but will do so if the halteres are coactivated. Intracellular recordings show that these motor neurons receive small, sustained subthreshold visual inputs in addition to larger inputs that are phase locked to haltere movements. Our results suggest that the nonlinear gating-like effect results from summation of these two inputs with the action potential threshold providing the nonlinearity. As a result of this summation, the sustained visual depolarization is transformed into a temporally structured train of action potentials synchronized to the haltere beating movements. This simple mechanism efficiently fuses two different sensory signals and may also explain the context-dependent effects of visual inputs on fly behavior.

There is rich variety in the activity of single neurons recorded during behaviour. Yet, these diverse single neuron responses can be well described by relatively few patterns of neural co-modulation. The study of such low-dimensional structure of neural population activity has provided important insights into how the brain generates behaviour. Virtually all of these studies have used linear dimensionality reduction techniques to estimate these population-wide co-modulation patterns, constraining them to a flat "neural manifold". Here, we hypothesised that since neurons have nonlinear responses and make thousands of distributed and recurrent connections that likely amplify such nonlinearities, neural manifolds should be intrinsically nonlinear. Combining neural population recordings from monkey motor cortex, mouse motor cortex, mouse striatum, and human motor cortex, we show that: 1) neural manifolds are intrinsically nonlinear; 2) the degree of their nonlinearity varies across architecturally distinct brain regions; and 3) manifold nonlinearity becomes more evident during complex tasks that require more varied activity patterns. Simulations using recurrent neural network models confirmed the proposed relationship between circuit connectivity and manifold nonlinearity, including the differences across architecturally distinct regions. Thus, neural manifolds underlying the generation of behaviour are inherently nonlinear, and properly accounting for such nonlinearities will be critical as neuroscientists move towards studying numerous brain regions involved in increasingly complex and naturalistic behaviours.

Using ultralow light intensities that are well suited for investigating biological samples, we demonstrate whole-cell superresolution imaging by nonlinear structured-illumination microscopy. Structured-illumination microscopy can increase the spatial resolution of a wide-field light microscope by a factor of two, with greater resolution extension possible if the emission rate of the sample responds nonlinearly to the illumination intensity. Saturating the fluorophore excited state is one such nonlinear response, and a realization of this idea, saturated structured-illumination microscopy, has achieved approximately 50-nm resolution on dye-filled polystyrene beads. Unfortunately, because saturation requires extremely high light intensities that are likely to accelerate photobleaching and damage even fixed tissue, this implementation is of limited use for studying biological samples. Here, reversible photoswitching of a fluorescent protein provides the required nonlinearity at light intensities six orders of magnitude lower than those needed for saturation. We experimentally demonstrate approximately 40-nm resolution on purified microtubules labeled with the fluorescent photoswitchable protein Dronpa, and we visualize cellular structures by imaging the mammalian nuclear pore and actin cytoskeleton. As a result, nonlinear structured-illumination microscopy is now a biologically compatible superresolution imaging method.

The relationship between a sound and its neural representation in the auditory cortex remains elusive. Simple measures such as the frequency response area or frequency tuning curve provide little insight into the function of the auditory cortex in complex sound environments. Spectrotemporal receptive field (STRF) models, despite their descriptive potential, perform poorly when used to predict auditory cortical responses, showing that nonlinear features of cortical response functions, which are not captured by STRFs, are functionally important. We introduce a new approach to the description of auditory cortical responses, using multilinear modeling methods. These descriptions simultaneously account for several nonlinearities in the stimulus-response functions of auditory cortical neurons, including adaptation, spectral interactions, and nonlinear sensitivity to sound level. The models reveal multiple inseparabilities in cortical processing of time lag, frequency, and sound level, and suggest functional mechanisms by which auditory cortical neurons are sensitive to stimulus context. By explicitly modeling these contextual influences, the models are able to predict auditory cortical responses more accurately than are STRF models. In addition, they can explain some forms of stimulus dependence in STRFs that were previously poorly understood.

Nonmuscle myosin II (NM II) powers myriad developmental and cellular processes, including embryogenesis, cell migration, and cytokinesis [1]. To exert its functions, monomers of NM II assemble into bipolar filaments that produce a contractile force on the actin cytoskeleton. Mammalian cells express up to three isoforms of NM II (NM IIA, IIB, and IIC), each of which possesses distinct biophysical properties and supports unique as well as redundant cellular functions [2-8]. Despite previous efforts [9-13], it remains unclear whether NM II isoforms assemble in living cells to produce mixed (heterotypic) bipolar filaments or whether filaments consist entirely of a single isoform (homotypic). We addressed this question using fluorescently tagged versions of NM IIA, IIB, and IIC, isoform-specific immunostaining of the endogenous proteins, and two-color total internal reflection fluorescence structured-illumination microscopy, or TIRF-SIM, to visualize individual myosin II bipolar filaments inside cells. We show that NM II isoforms coassemble into heterotypic filaments in a variety of settings, including various types of stress fibers, individual filaments throughout the cell, and the contractile ring. We also show that the differential distribution of NM IIA and NM IIB typically seen in confocal micrographs of well-polarized cells is reflected in the composition of individual bipolar filaments. Interestingly, this differential distribution is less pronounced in freshly spread cells, arguing for the existence of a sorting mechanism acting over time. Together, our work argues that individual NM II isoforms are potentially performing both isoform-specific and isoform-redundant functions while coassembled with other NM II isoforms.

Both neurons and glia communicate via diffusible neuromodulatory substances, but the substrates of computation in such neuromodulatory networks are unclear. During behavioral transitions in the larval zebrafish, the neuromodulator norepinephrine drives fast excitation and delayed inhibition of behavior and circuit activity. We find that the inhibitory arm of this feedforward motif is implemented by astroglial purinergic signaling. Neuromodulator imaging, behavioral pharmacology, and perturbations of neurons and astroglia reveal that norepinephrine triggers astroglial release of adenosine triphosphate, extracellular conversion into adenosine, and behavioral suppression through activation of hindbrain neuronal adenosine receptors. This work, along with a companion piece by Lefton and colleagues demonstrating an analogous pathway mediating the effect of norepinephrine on synaptic connectivity in mice, identifies a computational and behavioral role for an evolutionarily conserved astroglial purinergic signaling axis in norepinephrine-mediated behavioral and brain state transitions.

Oblique dendrites of CA1 pyramidal neurons predominate in stratum radiatum and receive approximately 80% of the synaptic input from Schaffer collaterals. Despite this fact, most of our understanding of dendritic signal processing in these neurons comes from studies of the main apical dendrite. Using a combination of Ca2+ imaging and whole-cell recording techniques in rat hippocampal slices, we found that the properties of the oblique dendrites differ markedly from those of the main dendrites. These different properties tend to equalize the Ca2+ rise from single action potentials as they backpropagate into the oblique dendrites from the main trunk. Evidence suggests that this normalization of Ca2+ signals results from a higher density of a transient, A-type K+ current [I(K(A))] in the oblique versus the main dendrites. The higher density of I(K(A)) may have important implications for our understanding of synaptic integration and plasticity in these structures.

Activity related to movement is found throughout sensory and motor regions of the brain. However, it remains unclear how movement-related activity is distributed across the brain and whether systematic differences exist between brain areas. Here, we analyzed movement related activity in brain-wide recordings containing more than 50,000 neurons in mice performing a decision-making task. Using multiple techniques, from markers to deep neural networks, we find that movement-related signals were pervasive across the brain, but systematically differed across areas. Movement-related activity was stronger in areas closer to the motor or sensory periphery. Delineating activity in terms of sensory- and motor-related components revealed finer scale structures of their encodings within brain areas. We further identified activity modulation that correlates with decision-making and uninstructed movement. Our work charts out a largescale map of movement encoding and provides a roadmap for dissecting different forms of movement and decision-making related encoding across multi-regional neural circuits.