Exact linear theory of perturbation response in a space- and feature-dependent cortical circuit model.

What are the principles that govern the responses of cortical networks to their inputs and the emergence of these responses from recurrent connectivity? Recent experiments have probed these questions by measuring cortical responses to two-photon optogenetic perturbations of single cells in the mouse primary visual cortex. A robust theoretical framework is needed to determine the implications of these responses for cortical recurrence. Here, we propose a formulation of the dependence of cell-type-specific connectivity on spatial distance that yields an exact analytic solution for the linear perturbation response of a model with multiple cell types and space- and feature-dependent connectivity.

Theory of interaction between untuned modulatory inputs and tuned sensory inputs.

How does the brain integrate sensory inputs with non-feature-tuned signals, such as those arising from behavioral state changes or neuromodulation? Here, we show that the dynamics of disordered E/I networks with structured, feature-dependent connectivity can be well characterized by an effective model describing interactions between the responses of cells who prefer the current sensory stimulus ("matched" cells) and the responses of cells firing at the baseline. This effective network exhibits strong feedback from the baseline onto the matched responses but weak reverse projections. Thus, an untuned stimulus not only directly drives matched cells, but also indirectly drives them via modulation of the baseline.

Stabilized Supralinear Network Model of Responses to Surround Stimuli in Primary Visual Cortex.

In the mammalian primary visual cortex (V1), there are complex interactions between responses to stimuli present in the cell's classical receptive field (CRF) or "center" and in the surrounding region or "surround." The circuit mechanisms underlying these behaviors are likely to represent more general cortical mechanisms for integrating information. Here, we develop a circuit model that accounts for three important features of surround suppression (suppression of response to a center stimulus by addition of a surround stimulus): (1) The surround stimulus suppresses the inhibitory and excitatory currents that the cell receives; (2) The strongest suppression arises when the surround orientation matches that of the center stimulus, even when the center stimulus orientation differs from the cell's preferred orientation; and (3) A surround stimulus of a given orientation most strongly suppresses that orientation's component of the response to a plaid center stimulus ("feature-specific suppression").

The geometry of the neural state space of decisions.

How do populations of neurons collectively encode and process information during cognitive tasks? We analyze high-yield population recordings from the macaque lateral intraparietal area (LIP) during a reaction-time random-dot-motion direction-discrimination task. We find that the trajectories of neural population activity patterns during single decisions lie within a curved two-dimensional manifold. The reaction time of trajectories systematically varies along one dimension, such that slow and fast decisions trace distinct activity patterns.

Exact linear theory of perturbation response in a space- and feature-dependent cortical circuit model.

What are the principles that govern the responses of cortical networks to their inputs and the emergence of these responses from recurrent connectivity? Recent experiments have probed these questions by measuring cortical responses to two-photon optogenetic perturbations of single cells in the mouse primary visual cortex. A robust theoretical framework is needed to determine the implications of these responses for cortical recurrence. Here we propose a novel analytical approach: a formulation of the dependence of cell-type-specific connectivity on spatial distance that yields an exact solution for the linear perturbation response of a model with multiple cell types and space- and feature-dependent connectivity.

Adaptation shapes the representational geometry in mouse V1 to efficiently encode the environment.

Sensory adaptation dynamically changes neural responses as a function of previous stimuli, profoundly impacting perception. The response changes induced by adaptation have been characterized in detail in individual neurons and at the population level after averaging across trials. However, it is not clear how adaptation modifies the aspects of the representations that relate more directly to the ability to perceive stimuli, such as their geometry and the noise structure in individual trials.

Contextual modulation emerges by integrating feedforward and feedback processing in mouse visual cortex.

Sensory systems use context to infer meaning. Accordingly, context profoundly influences neural responses to sensory stimuli. However, a cohesive understanding of the circuit mechanisms governing contextual effects across different stimulus conditions is still lacking.

Circuit-motivated generalized affine models characterize stimulus-dependent visual cortical shared variability.

Correlated variability in the visual cortex is modulated by stimulus properties. The stimulus dependence of correlated variability impacts stimulus coding and is indicative of circuit structure. An affine model combining a multiplicative factor and an additive offset has been proposed to explain how correlated variability in primary visual cortex (V1) depends on stimulus orientations.

The stabilized supralinear network accounts for the contrast dependence of visual cortical gamma oscillations.

When stimulated, neural populations in the visual cortex exhibit fast rhythmic activity with frequencies in the gamma band (30-80 Hz). The gamma rhythm manifests as a broad resonance peak in the power-spectrum of recorded local field potentials, which exhibits various stimulus dependencies. In particular, in macaque primary visual cortex (V1), the gamma peak frequency increases with increasing stimulus contrast.

Response sub-additivity and variability quenching in visual cortex.

Sub-additivity and variability are ubiquitous response motifs in the primary visual cortex (V1). Response sub-additivity enables the construction of useful interpretations of the visual environment, whereas response variability indicates the factors that limit the precision with which the brain can do this. There is increasing evidence that experimental manipulations that elicit response sub-additivity often also quench response variability.

The stabilized supralinear network accounts for the contrast dependence of visual cortical gamma oscillations.

When stimulated, neural populations in the visual cortex exhibit fast rhythmic activity with frequencies in the gamma band (30-80 Hz). The gamma rhythm manifests as a broad resonance peak in the power-spectrum of recorded local field potentials, which exhibits various stimulus dependencies. In particular, in macaque primary visual cortex (V1), the gamma peak frequency increases with increasing stimulus contrast.

A human-specific modifier of cortical connectivity and circuit function.

The cognitive abilities that characterize humans are thought to emerge from unique features of the cortical circuit architecture of the human brain, which include increased cortico-cortical connectivity. However, the evolutionary origin of these changes in connectivity and how they affected cortical circuit function and behaviour are currently unknown. The human-specific gene duplication SRGAP2C emerged in the ancestral genome of the Homo lineage before the major phase of increase in brain size.

What is the dynamical regime of cerebral cortex?

Many studies have shown that the excitation and inhibition received by cortical neurons remain roughly balanced across many conditions. A key question for understanding the dynamical regime of cortex is the nature of this balancing. Theorists have shown that network dynamics can yield systematic cancellation of most of a neuron's excitatory input by inhibition.

Interrogating theoretical models of neural computation with emergent property inference.

A cornerstone of theoretical neuroscience is the circuit model: a system of equations that captures a hypothesized neural mechanism. Such models are valuable when they give rise to an experimentally observed phenomenon -- whether behavioral or a pattern of neural activity -- and thus can offer insights into neural computation. The operation of these circuits, like all models, critically depends on the choice of model parameters.

Acute Liver Failure in Sickle Cell Disease: A Perfect Storm.

Sickle cell hepatopathy is a well-described but uncommonly seen complication of sickle cell disease and is usually caused by multiple overlapping processes. A more acute liver complication is hepatic sequestration which is important to recognize in order to initiate life-saving treatment. A 33-year-old woman with sickle cell disease complicated by painful crises, splenic infarction and significant alcohol abuse presented with gastrointestinal distress, pain crisis, acute-on-chronic anemia, and hyperbilirubinemia in the setting of greater than baseline alcohol consumption.

A Disinhibitory Circuit for Contextual Modulation in Primary Visual Cortex.

Context guides perception by influencing stimulus saliency. Accordingly, in visual cortex, responses to a stimulus are modulated by context, the visual scene surrounding the stimulus. Responses are suppressed when stimulus and surround are similar but not when they differ.

Hyperprogression of Liver Metastasis With Neoadjuvant Immunotherapy for Soft Tissue Sarcoma.

Hyperprogression associated with immunotherapy has been reported previously with melanoma, non-small cell lung cancer (NSCLC), renal, and urothelial cancers but not with sarcoma. A 63-year old man with a biopsy-proven, localized 13 cm high-grade myxoid/round cell liposarcoma of the thigh was treated with concurrent, neoadjuvant checkpoint inhibitor immunotherapy and radiotherapy. After his subsequent wide surgical resection, he developed small hepatic lesions that rapidly progressed and caused his death, raising the possibility of hyperprogression in this entity.

How biological attention mechanisms improve task performance in a large-scale visual system model.

How does attentional modulation of neural activity enhance performance? Here we use a deep convolutional neural network as a large-scale model of the visual system to address this question. We model the feature similarity gain model of attention, in which attentional modulation is applied according to neural stimulus tuning. Using a variety of visual tasks, we show that neural modulations of the kind and magnitude observed experimentally lead to performance changes of the kind and magnitude observed experimentally.

The Dynamical Regime of Sensory Cortex: Stable Dynamics around a Single Stimulus-Tuned Attractor Account for Patterns of Noise Variability.

Correlated variability in cortical activity is ubiquitously quenched following stimulus onset, in a stimulus-dependent manner. These modulations have been attributed to circuit dynamics involving either multiple stable states ("attractors") or chaotic activity. Here we show that a qualitatively different dynamical regime, involving fluctuations about a single, stimulus-driven attractor in a loosely balanced excitatory-inhibitory network (the stochastic "stabilized supralinear network"), best explains these modulations.

Spotlight on dabrafenib/trametinib in the treatment of non-small-cell lung cancer: place in therapy.

Advanced non-small-cell lung cancer (NSCLC) remains a challenging disease. The limited utility of chemotherapy indicates the need for additional therapeutic options. Targeted therapy continues to be an important tool in the treatment of NSCLC.

A Unifying Motif for Spatial and Directional Surround Suppression.

In the visual system, the response to a stimulus in a neuron's receptive field can be modulated by stimulus context, and the strength of these contextual influences vary with stimulus intensity. Recent work has shown how a theoretical model, the stabilized supralinear network (SSN), can account for such modulatory influences, using a small set of computational mechanisms. Although the predictions of the SSN have been confirmed in primary visual cortex (V1), its computational principles apply with equal validity to any cortical structure.