Task-dependent recurrent dynamics in visual cortex

The capacity for flexible sensory-action association in animals has been related to context-dependent attractor dynamics outside the sensory cortices. Here, we report a line of evidence that flexibly modulated attractor dynamics during task switching are already present in the higher visual cortex in macaque monkeys. With a nonlinear decoding approach, we can extract the particular aspect of the neural population response that reflects the task-induced emergence of bistable attractor dynamics in a neural population, which could be obscured by standard unsupervised dimensionality reductions such as PCA. The dynamical modulation selectively increases the information relevant to task demands, indicating that such modulation is beneficial for perceptual decisions. A computational model that features nonlinear recurrent interaction among neurons with a task-dependent background input replicates the key properties observed in the experimental data. These results suggest that the context-dependent attractor dynamics involving the sensory cortex can underlie flexible perceptual abilities.

Task-dependent recurrent dynamics in visual cortex

The capacity for flexible sensory-action association in animals has been related to context-dependent attractor dynamics outside the sensory cortices. Here we report a line of evidence that flexibly modulated attractor dynamics during task switching are already present in the higher visual cortex in macaque monkeys. With a nonlinear decoding approach, we can extract the particular aspect of the neural population response that reflects the task-induced emergence of bistable attractor dynamics in a neural population, which could be obscured by standard unsupervised dimensionality reductions such as PCA. The dynamical modulation selectively increases the information relevant to task demands, indicating that such modulation is beneficial for perceptual decisions. A computational model that features nonlinear recurrent interaction among neurons with a task-dependent background input replicates the key properties observed in the experimental data. These results suggest that the context-dependent attractor dynamics involving the sensory cortex can underlie flexible perceptual abilities.

Analytical Calculation of Mutual Information between Weakly Coupled Poisson-Spiking Neurons in Models of Dynamically Gated Communication

Mutual information is a commonly used measure of communication between neurons, but little theory exists describing the relationship between mutual information and the parameters of the underlying neuronal interaction. Such a theory could help us understand how specific physiological changes affect the capacity of neurons to synaptically communicate, and, in particular, they could help us characterize the mechanisms by which neuronal dynamics gate the flow of information in the brain. Here we study a pair of linear-nonlinear-Poisson neurons coupled by a weak synapse. We derive an analytical expression describing the mutual information between their spike trains in terms of synapse strength, neuronal activation function, the time course of postsynaptic currents, and the time course of the background input received by the two neurons. This expression allows mutual information calculations that would otherwise be computationally intractable. We use this expression to analytically explore the interaction of excitation, information transmission, and the convexity of the activation function. Then, using this expression to quantify mutual information in simulations, we illustrate the information-gating effects of neural oscillations and oscillatory coherence, which may either increase or decrease the mutual information across the synapse depending on parameters. Finally, we show analytically that our results can quantitatively describe the selection of one information pathway over another when multiple sending neurons project weakly to a single receiving neuron.

Factores de contexto, entrada y proceso asociados al rendimiento en Matemática: un estudio multinivel/Factors of background, input and process associated with Mathematics performance: A Multilevel analyses

Resumen. El propósito de esta investigación fue determinar el grado de asociación de los factores de contexto, entrada y proceso con el rendimiento académico en el Curso de Matemática General desde la perspectiva teórica del modelo CIPP. El estudio se basó en la aplicación del modelo de regresión lineal de dos niveles, con la participación del estudiantado (nivel uno) y los docentes universitarios (nivel dos) del curso en 2010. Los hallazgos evidencian una asociación con significancia estadística e importancia práctica del rendimiento con ciertas variables del estudiantado relativas al historial académico (contexto), la interacción docente-estudiante y estrategias metodológicas (proceso) y del docente (entrada). Finalmente, la articulación del modelo CIPP con el análisis multinivel favoreció un acercamiento integral de un fenómeno educativo multifactorial.

Dynamical Properties of Discrete-Time Background Neural Networks with Uniform Firing Rate

The dynamics of a discrete-time background network with uniform firing rate and background input is investigated. The conditions for stability are firstly derived. An invariant set is then obtained so that the nondivergence of the network can be guaranteed. In the invariant set, it is proved that all trajectories of the network starting from any nonnegative value will converge to a fixed point under some conditions. In addition, bifurcation and chaos are discussed. It is shown that the network can engender bifurcation and chaos with the increase of background input. The computations of Lyapunov exponents confirm the chaotic behaviors.

Impact of Satellite Winds on Marine Wind Simulations

A variational data assimilation method is applied to remotely sensed wind data from Hurricanes Gustav (2002) and Isabel (2003) to produce enhanced marine wind estimates. The variational method utilizes constraints to ensure that an optimum combination of winds is determined, in the sense of minimization of a cost function measuring the misfit between observations and background input field data and constraining nongeophysical features in the spatial derivatives. Constraints are multiplied by weights, which are objectively determined by cross validation. Verification is obtained by comparison with available operational in situ buoy observations and analyses winds. It is shown that the newly constructed midlatitude wind fields represent an improvement relative to background wind field estimates and also relative to Quick Scatterometer–National Centers for Environmental Prediction reanalysis blended winds, and that the new winds have an impact on simulations of waves and upper-ocean currents.