Indications for a metal-insulator transition in quasicrystalline i-AlPdRe

We present indications for a metal-insulator transition in the icosahedral AlPdRe phase of high structural quality. We follow the conductivity through the transition and its dependence on temperature and magnetic field in a series of i-AlPdRe samples. Features typical for systems on both sides of the metal-insulator transition are found. Moreover, the variation of conductivity with temperature is very unusual with $\sigma (T)$ varying like $T ^\alpha$ ($1\leq\alpha\leq1.5$) in a wide temperature range from 7 K to 700 K

High-temperature transformation in AlMn(Si) quasi-crystals

Transformations of the quasi-crystalline icosahedral phases formed in the melt-spun AlMn(Si) alloys Al90Mn10, Al86Mn14 and Al75Mn20Si5 have been studied at high temperatures (300-1000 K) by combined measurements of resistivity, differential scanning calorimetry and X-ray diffraction. In Al86Mn14 a direct crystallisation of both the icosahedral phase and the intergranular AlMn solid solution into o-Al6Mn is observed. It is preceded by a `relaxation' phenomenon, as seen by the DSC curve. The amplitude and temperature dependence of resistivity are well understood by taking into account the different phase contributions to resistivity during the transformation. The same analysis accounts for the measurements of the Al90Mn10 sample. The crystallisation of Al75Mn20Si5 into β-Al9Mn3Si is more complex and involves several intermediate crystalline phases, in particular the α-Al12Mn3Si. Finally, the better stability of the icosahedral phase in Al75Mn20Si5 (~700 K) compared with AlMn samples (~600 K) suggests possible structural and electronic differences between the two icosahedral phase

Astrocyte signaling controls spike timing-dependent depression at neocortical synapses

Endocannabinoid mediated spike timing-dependent depression (t-LTD) is crucially involved in the development of the sensory neocortex. t-LTD at excitatory synapses in the developing rat barrel cortex requires cannabinoid CB(1) receptor (CB(1)R) activation, as well as activation of NMDA receptors located on the presynaptic terminal, but the exact signaling cascade leading to t-LTD remains unclear. We found that astrocytes are critically involved in t-LTD. Astrocytes gradually increased their Ca(2+) signaling specifically during the induction of t-LTD in a CB(1)R-dependent manner. In this way, astrocytes might act as a memory buffer for previous coincident neuronal activity. Following activation, astrocytes released glutamate, which activated presynaptic NMDA receptors to induce t-LTD. Astrocyte stimulation coincident with afferent activity resulted in long-term depression, indicating that astrocyte activation is sufficient for the induction of synaptic depression. Taken together, our findings describe the retrograde signaling cascade underlying neocortical t-LTD. The critical involvement of astrocytes in this process highlights their importance for experience-dependent sensory remodeling

Computational models of reinforcement learning: the role of dopamine as a reward signal

Reinforcement learning is ubiquitous. Unlike other forms of learning, it involves the processing of fast yet content-poor feedback information to correct assumptions about the nature of a task or of a set of stimuli. This feedback information is often delivered as generic rewards or punishments, and has little to do with the stimulus features to be learned. How can such low-content feedback lead to such an efficient learning paradigm? Through a review of existing neuro-computational models of reinforcement learning, we suggest that the efficiency of this type of learning resides in the dynamic and synergistic cooperation of brain systems that use different levels of computations. The implementation of reward signals at the synaptic, cellular, network and system levels give the organism the necessary robustness, adaptability and processing speed required for evolutionary and behavioral success