Higher-order nonlinear electron-acoustic solitary excitations in partially degenerate quantum electron-ion plasmas

Propagation of dressed solitary excitations are studied in a partially degenerate quantum plasma in the framework of quantum-hydrodynamics (QHD) model using multiple scales technique. The evolution equation together with a linear inhomogeneous differential equation is solved using Kodama-Taniuti renormalizing technique. It is shown that the type of solitary excitations (bright or dark) is defined by two critical plasma parameter values.Comment: To appear in Indian Journal of Physic

Converging Neuronal Activity in Inferior Temporal Cortex during the Classification of Morphed Stimuli

How does the brain dynamically convert incoming sensory data into a representation useful for classification? Neurons in inferior temporal (IT) cortex are selective for complex visual stimuli, but their response dynamics during perceptual classification is not well understood. We studied IT dynamics in monkeys performing a classification task. The monkeys were shown visual stimuli that were morphed (interpolated) between pairs of familiar images. Their ability to classify the morphed images depended systematically on the degree of morph. IT neurons were selected that responded more strongly to one of the 2 familiar images (the effective image). The responses tended to peak ∼120 ms following stimulus onset with an amplitude that depended almost linearly on the degree of morph. The responses then declined, but remained above baseline for several hundred ms. This sustained component remained linearly dependent on morph level for stimuli more similar to the ineffective image but progressively converged to a single response profile, independent of morph level, for stimuli more similar to the effective image. Thus, these neurons represented the dynamic conversion of graded sensory information into a task-relevant classification. Computational models suggest that these dynamics could be produced by attractor states and firing rate adaptation within the population of IT neurons