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 through diffusible neuromodulators; however, how neuron-glial interactions in such neuromodulatory networks influence circuit computation and behavior is unclear. During futility-induced behavioral transitions in the larval zebrafish, the neuromodulator norepinephrine (NE) drives fast excitation and delayed inhibition of behavior and circuit activity. We found that astroglial purinergic signaling implements the inhibitory arm of this motif. In larval zebrafish, NE triggers astroglial release of adenosine triphosphate (ATP), extracellular conversion of ATP into adenosine, and behavioral suppression through activation of hindbrain neuronal adenosine receptors. Our results suggest a computational and behavioral role for an evolutionarily conserved astroglial purinergic signaling axis in NE-mediated behavioral and brain state transitions and position astroglia as important effectors in neuromodulatory signaling.

Preprint: https://www.biorxiv.org/content/early/2024/05/23/2024.05.23.595576

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.

Sensory areas are spontaneously active in the absence of sensory stimuli. This spontaneous activity has long been studied; however, its functional role remains largely unknown. Recent advances in technology, allowing large-scale neural recordings in the awake and behaving animal, have transformed our understanding of spontaneous activity. Studies using these recordings have discovered high-dimensional spontaneous activity patterns, correlation between spontaneous activity and behavior, and dissimilarity between spontaneous and sensory-driven activity patterns. These findings are supported by evidence from developing animals, where a transition toward these characteristics is observed as the circuit matures, as well as by evidence from mature animals across species. These newly revealed characteristics call for the formulation of a new role for spontaneous activity in neural sensory computation.

UNLABELLED: Temporal limits on perceptual decisions set strict boundaries on the possible underlying neural computations. How odor information is encoded in the olfactory system is still poorly understood. Here, we sought to define the limit on the speed of olfactory processing. To achieve this, we trained mice to discriminate different odor concentrations in a novel behavioral setup with precise odor delivery synchronized to the sniffing cycle. Mice reported their choice by moving a horizontal treadmill with their front limbs. We found that mice reported discriminations of 75% accuracy in 70-90 ms after odor inhalation. For a low concentration and nontrigeminal odorant, this time was 90-140 ms, showing that mice process odor information rapidly even in the absence of trigeminal stimulation. These response times establish, after accounting for odor transduction and motor delays, that olfactory processing can take tens of milliseconds. This study puts a strong limit on the underlying neural computations and suggests that the action potentials forming the neural basis for these decisions are fired in a few tens of milliseconds.

SIGNIFICANCE STATEMENT: Understanding how sensory information is processed requires different approaches that span multiple levels of investigation from genes to neurons to behavior. Limits on behavioral performance constrain the possible neural mechanisms responsible for specific computations. Using a novel behavioral paradigm, we established that mice can make decisions about odor intensity surprisingly fast. After accounting for sensory and motor delays, the limit on some olfactory neural computations can be as low as a few tens of milliseconds, which suggests that only the first action potentials across a population of neurons contribute to these computations.

Development of cell-based odorant sensor elements combined not only high degree of sensitivity and selectivity but also long-term stability is crucial for their practical applications. Here we report the development of a novel cell-based odorant sensor element that sensitively and selectively detects odorants and displays increased fluorescent intensities over a long period of time. Our odorant sensor elements, based on Sf21 cell lines expressing insect odorant receptors, are sensitive to the level of several tens of parts per billion in solution, can selectively distinguish between different types of odorants based on the odorant selectivity intrinsic to the expressed receptors, and have response times of approximately 13s. Specifically, with the use of Sf21 cells and insect odorant receptors, we demonstrated that the established cell lines stably expressing insect odorant receptors are able to detect odorants with consistent responsiveness for at least 2 months, thus exceeding the short life-span normally associated with cell-based sensors. We also demonstrated the development of a compact odorant sensor chip by integrating the established insect cell lines into a microfluidic chip. The methodology we established in this study, in conjunction with the large repertoire of insect odorant receptors, will aid in the development of practical cell-based odorant sensors for various applications, including food administration and health management.