Localization-enhanced biexciton binding in semiconductors

The influence of excitonic localization on the binding energy of biexcitons is investigated for quasi-three-dimensional and quasi-two-dimensional AlxGa1−xAs structures. An increase of the biexciton binding energy is observed for localization energies comparable to or larger than the free biexciton binding energy. A simple analytical model for localization in the weak confinement regime ascribes the increase to a quenching of the additional kinetic energy of the exciton-exciton motion in the biexciton

Transient four-wave mixing in T-shaped GaAs quantum wires

The binding energy of excitons and biexcitons and the exciton dephasing in T-shaped GaAs quantum wires is investigated by transient four-wave mixing. The T-shaped structure is fabricated by cleaved-edge overgrowth, and its geometry is engineered to optimize the one-dimensional confinement. In this wire of 6.6×24 nm2 size, we find a one-dimensional confinement of more than 20 meV, an inhomogeneous broadening of 3.4 meV, an exciton binding energy of 12 meV, and a biexciton binding energy of 2.0 meV. A dispersion of the homogeneous linewidth within the inhomogeneous broadening due to phonon-assisted relaxation is observed. The exciton acoustic-phonon-scattering coefficient of 6.1±0.5 μeV/K is larger than in comparable quantum-well structures

Parallel Driving and Modulatory Pathways Link the Prefrontal Cortex and Thalamus

Pathways linking the thalamus and cortex mediate our daily shifts from states of attention to quiet rest, or sleep, yet little is known about their architecture in high-order neural systems associated with cognition, emotion and action. We provide novel evidence for neurochemical and synaptic specificity of two complementary circuits linking one such system, the prefrontal cortex with the ventral anterior thalamic nucleus in primates. One circuit originated from the neurochemical group of parvalbumin-positive thalamic neurons and projected focally through large terminals to the middle cortical layers, resembling ‘drivers’ in sensory pathways. Parvalbumin thalamic neurons, in turn, were innervated by small ‘modulatory’ type cortical terminals, forming asymmetric (presumed excitatory) synapses at thalamic sites enriched with the specialized metabotropic glutamate receptors. A second circuit had a complementary organization: it originated from the neurochemical group of calbindin-positive thalamic neurons and terminated through small ‘modulatory’ terminals over long distances in the superficial prefrontal layers. Calbindin thalamic neurons, in turn, were innervated by prefrontal axons through small and large terminals that formed asymmetric synapses preferentially at sites with ionotropic glutamate receptors, consistent with a driving pathway. The largely parallel thalamo-cortical pathways terminated among distinct and laminar-specific neurochemical classes of inhibitory neurons that differ markedly in inhibitory control. The balance of activation of these parallel circuits that link a high-order association cortex with the thalamus may allow shifts to different states of consciousness, in processes that are disrupted in psychiatric diseases

Evaluating contingencies by a dual system of learning the structure and the parameters of the environment

How does the brain identify stimuli that are relevant for predicting important events and how does it distinguish spurious relationships from truly predictive ones? We examined two contrasting theoretical frameworks: in the first, learning proceeds by considering a fixed hypothesis of the environment’s statistical structure (the set of predictive and causal relationships) and adjusting strength parameters for these relationships to optimize predictions. In contrast, the second approach directly assesses ambiguity in predictive relationships by evaluating multiple hypothesis of the environment’s statistical structure. We compared these frameworks in an animal model of aversive conditioning, allowing us to also manipulate the underlying brain systems. We show that when facing novel predictive stimuli, rats initially adopt a structure learning strategy, but switch to updating parameters during subsequent learnin