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Self-organized criticality has been observed in a number of complex systems, including neuronal networks. Another property of cortical networks is that a high proportion of neurons collectively alternate between high activity (so-called up states), and quiescence (down states). Theoretical work now shows these two phenomena are intimately related. During sleep, under anaesthesia and in vitro , cortical neurons in sensory, motor, association and executive areas fluctuate between so-called up and down states, which are characterized by distinct membrane potentials and spike rates 1 , 2 , 3 , 4 , 5 . Another phenomenon observed in preparations similar to those that exhibit up and down states—such as anaesthetized rats 6 , brain slices and cultures devoid of sensory input 7 , as well as awake monkey cortex 8 —is self-organized criticality (SOC). SOC is characterized by activity ‘avalanches’ with a branching parameter near unity and size distribution that obeys a power law with a critical exponent of about −3/2. Recent work has demonstrated SOC in conservative neuronal network models 9 , 10 , but critical behaviour breaks down when biologically realistic ‘leaky’ neurons are introduced 9 . Here, we report robust SOC behaviour in networks of non-conservative leaky integrate-and-fire neurons with short-term synaptic depression. We show analytically and numerically that these networks typically have two stable activity levels, corresponding to up and down states, that the networks switch spontaneously between these states and that up states are critical and down states are subcritical.

Vasoactive intestinal peptide-expressing (VIP) interneurons in the cortex regulate feedback inhibition of pyramidal neurons through suppression of somatostatin-expressing (SST) interneurons and, reciprocally, SST neurons inhibit VIP neurons. Although VIP neuron activity in the primary visual cortex (V1) of mouse is highly correlated with locomotion, the relevance of locomotion-related VIP neuron activity to visual coding is not known. Here we show that VIP neurons in mouse V1 respond strongly to low contrast front-to-back motion that is congruent with self-motion during locomotion but are suppressed by other directions and contrasts. VIP and SST neurons have complementary contrast tuning. Layer 2/3 contains a substantially larger population of low contrast preferring pyramidal neurons than deeper layers, and layer 2/3 (but not deeper layer) pyramidal neurons show bias for front-to-back motion specifically at low contrast. Network modeling indicates that VIP-SST mutual antagonism regulates the gain of the cortex to achieve sensitivity to specific weak stimuli without compromising network stability.

Extracellular electrophysiology and two-photon calcium imaging are widely used methods for measuring physiological activity with single-cell resolution across large populations of cortical neurons. While each of these two modalities has distinct advantages and disadvantages, neither provides complete, unbiased information about the underlying neural population. Here, we compare evoked responses in visual cortex recorded in awake mice under highly standardized conditions using either imaging of genetically expressed GCaMP6f or electrophysiology with silicon probes. Across all stimulus conditions tested, we observe a larger fraction of responsive neurons in electrophysiology and higher stimulus selectivity in calcium imaging, which was partially reconciled by applying a spikes-to-calcium forward model to the electrophysiology data. However, the forward model could only reconcile differences in responsiveness when restricted to neurons with low contamination and an event rate above a minimum threshold. This work established how the biases of these two modalities impact functional metrics that are fundamental for characterizing sensory-evoked responses.

The mammalian neocortex is subdivided into a series of cortical areas that are functionally and anatomically distinct and are often distinguished in brain sections using histochemical stains and other markers of protein expression. We searched the Allen Mouse Brain Atlas, a database of gene expression, for novel markers of cortical areas. To screen for genes that change expression at area borders, we employed a random forest algorithm and binary region classification. Novel genetic markers were identified for 19 of 39 areas and provide code that quickly and efficiently searches the Allen Mouse Brain Atlas. Our results demonstrate the utility of the random forest algorithm for cortical area classification and we provide code that may be used to facilitate the identification of genetic markers of cortical and subcortical structures and perhaps changes in gene expression in disease states.

Strengthening of concrete structures can take various forms. In many cases, the as-built conditions may limit the type of strengthening that can be accomplished. Within the engineer's toolbox are methods that aim to increase the area of tensile reinforcement while limiting the potentially destructive means of bonding the reinforcement to the concrete member requiring strengthening. Two of these methods are externally bonded reinforcement (EBR) and near-surface-mounted (NSM) reinforcement. The goal of these methods is to install additional tensile reinforcement at or near the surface of a concrete member while avoiding the need for extensive demolition of the concrete section. In many cases, these installations can be accomplished while the structure remains in service. While EBR, such as carbon-fiber reinforced polymer sheets, strips, or plates will be on the concrete element's surface, NSM reinforcement will be bonded to the concrete in channels cut into the cover zone of a concrete element. This article will focus on the NSM technique.

To understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes the cortical activity of nearly 60,000 neurons from six visual areas, four layers, and 12 transgenic mouse lines in a total of 243 adult mice, in response to a systematic set of visual stimuli. We classify neurons on the basis of joint reliabilities to multiple stimuli and validate this functional classification with models of visual responses. While most classes are characterized by responses to specific subsets of the stimuli, the largest class is not reliably responsive to any of the stimuli and becomes progressively larger in higher visual areas. These classes reveal a functional organization wherein putative dorsal areas show specialization for visual motion signals.

BackgroundUnderstanding multicellular interactions within the tumor-immune microenvironment (TME) is essential for developing cancer therapies and precision medicine strategies. Tumors pose significant biological complexity due to patient and tissue heterogeneity, cell-cell interactions, and intricate signaling pathways. Macrophages are a heterogeneous cell population associated both positively and negatively with tumor progression and response to therapy, but the factors driving macrophage influence on the TME remain poorly understood. Spatial analysis of human tumors with machine learning offers a novel approach to deciphering the mechanisms underlying macrophage function.MethodsWe developed a spatial virtual cell foundation model, a custom multimodal transformer with 450M parameters, trained on CosMx spatial transcriptomics across over 40 million cells from 1399 non-small cell lung cancer (NSCLC) tumor resections.1 2 This model allows the simulation of gene expression in ‘virtual cells’ placed within spatially resolved TMEs and unlocks the ability to conduct in silico, cell-type specific experiments. To identify local cell-cell interactions between macrophages and particular cell types, we designed virtual ‘clonal neighborhood’ simulations, in which a virtual macrophage is surrounded by digital replicates of real tumor cells or fibroblasts sampled from NSCLC patients’ TME. We simulated 500 tumor or fibroblast clonal neighborhoods for each patient, leading to inferred gene expression on a total of 873,000 virtual macrophages. This allowed us to identify tumor-intrinsic and fibroblast-mediated transcriptional programs spatially associated with macrophage state. Topic modeling was applied to interpret the inferred macrophage transcriptional states, and predictive modeling analyses were used to identify tumor and fibroblast gene expression programs associated with different states.ResultsAnalysis of virtual macrophage revealed a diversity of transcriptional programs acquired from interacting with tumor cells or fibroblasts neighborhoods. A subset of these programs were associated with macrophage polarization towards immunosuppressive SPP1+ or immunogenic CXCL9+ states, known to correlate with patients‘ response to immunotherapy. We further identified programs in tumor cells and fibroblasts that correlate with these macrophage states. Specifically, our experiments unveiled collagen expressing fibroblasts promoting SPP1+ virtual macrophage phenotypes. This population of fibroblasts was enriched in KRAS STK11 mutant vs KRAS mutant tumors, suggesting genotype specific TME characteristics that may account for resistance to immune checkpoint therapy in KRAS STK11 mutant tumors.ConclusionsOur spatial virtual cell foundation model approach uncovers novel multicellular processes within the NSCLC TME, and helps elucidate how spatially-resolved events shape macrophage polarization. This approach identifies actionable targets, offering new insights to enhance immunotherapy efficacy and reverse immunosuppressive tumor environments.ReferencesLacey J Padron, et al. #1231 Foundation models of cell and tissue biology enabled by custom scaled data generation: insights from 1000 lung tumor samples. Journal for ImmunoTherapy of Cancer. 2024;12. Yubin Xie, et al. #3652 OCTO-virtual cell: a foundation model of cell and tissue spatial biology with application to patient stratification and target discovery. AACR. 2025.

Rodents can successfully learn multiple, novel stimulus-response associations after only a few repetitions when the contingencies predict reward. The circuits modified during such reinforcement learning to support decision making are not known, but the olfactory tubercle (OT) and posterior piriform cortex (pPC) are candidates for decoding reward category from olfactory sensory input and relaying this information to cognitive and motor areas. Here, we show that an explicit representation for reward category emerges in the OT within minutes of learning a novel odor-reward association, whereas the pPC lacks an explicit representation even after weeks of overtraining. The explicit reward category representation in OT is visible in the first sniff (50-100ms) of an odor on each trial, and precedes the motor action. Together, these results suggest that coding of stimulus information required for reward prediction does not occur within olfactory cortex, but rather in circuits involving the olfactory striatum.

Identifying dangerous or rewarding elements in an animal’s surroundings is an important – if not primary – function of sensory systems. This holds particularly true for the mouse olfactory system since odors convey crucial information about predators, mates, kin and food. Thus, the olfactory system needs to effectively determine which odors are present as well as whether each odor has a positive or negative association, termed valence. Currently, we have little knowledge of how reward influences the processing of odors in the olfactory system of behaving mice. My work focuses on two high-level olfactory areas, the posterior piriform cortex (pPC) and olfactory tubercle (OT), that are situated at the intersection of sensory and reward-related brain regions. The pPC receives direct input from early olfactory areas and makes reciprocal connections to cognitive brain regions such as orbitofrontal cortex, limbic structures and the medial temporal lobe. The OT is a part of the ventral striatum which also receives input from early olfactory areas and is heavily interconnected with the reward system. To examine odor and reward coding in these areas, I developed a novel odor categorization task and recorded individual pPC and OT neurons during task performance. Mice successfully learn multiple, novel odor-response associations after only a few repetitions when the contingencies predict reward. I find that an explicit representation for reward category emerges in the OT within minutes of learning a novel odor-response association, whereas the pPC lacks an explicit representation even after more than one month of overtraining. The explicit representation is visible in the first sniff of an odor on each trial, when the motor decision is made, and is not correlated with the trial-to-trial motor decision. Together, these results suggest that decoding of stimulus information required for reward-driven sensorimotor decision making does not occur within olfactory cortex, rather decoding occurs in circuits involving olfactory striatum.

Air Data Systems (FADS) are becoming more prevalent on re-entry vehicles, as evi- denced by the Mars Science Laboratory and the Orion Multipurpose Crew Vehicle. A FADS consists of flush-mounted pressure transducers located at various locations on the fore-body of a flight vehicle or the heat shield of a re-entry capsule. A pressure model converts the pressure readings into useful air data quantities. Two algorithms for converting pressure readings to air data have become predominant- the iterative Least Squares State Estimator (LSSE) and the Triples Algorithm. What follows herein is a new algorithm that takes advantage of the best features of both the Triples Algorithm and the LSSE. This approach employs the potential flow model and strategic differencing of the Triples Algorithm to obtain the defective flight angles; however, the requirements on port placement are far less restrictive, allowing for configurations that are considered optimal for a FADS.