A systematic evaluation of the Zr-in-rutile thermometer in ultra-high temperature (UHT) rocks

The Zr-in-rutile geothermometer is potentially a widely applicable tool to estimate peak metamorphic temperatures in rocks from diverse geological settings. In order to evaluate its usefulness and reliability to record and preserve high temperatures in granulite facies rocks, rutile from UHT rocks was investigated to assess different mechanisms of Zr (re-)distribution following cooling from high temperature. Granulite facies paragneisses from the lowermost part of the Ivrea Zone, Italy, incorporated as thin sheets into the extensive basaltic body of the Mafic Complex were selected for this study. The results show that Zr-in-rutile thermometry, if properly applied, is well suited to identify and study UHT terranes as it preserves a record of temperatures up to 1190 °C, although the thermometer is susceptible to partial post-peak metamorphic resetting by Zr diffusion. Texturally homogeneous rutile grains preserve Zr concentrations corresponding to temperatures of prograde rutile growth. Diverse rutile textures and relationships between some rutile host grains and included or adjacent Zr-bearing phases bear testimony to varying mechanisms of partial redistribution and resetting of Zr in rutile during cooling and link Zr-in-rutile temperatures to different steps of the metamorphic evolution. Rutile grains that equilibrated their Zr concentrations at temperatures above 1070 °C (i.e. 1.1 wt% Zr) could not retain all Zr in the rutile structure during cooling and exsolved baddeleyite (ZrO2). By subsequent reaction of baddeleyite exsolution lamellae with SiO2, zircon needles formed before the system finally closed at 650–700 °C without significant net loss of Zr from the whole host rutile grain. By reintegration of zircon exsolution needles, peak metamorphic temperatures of up to 1190 °C are derived for the studied rocks, which demonstrates the suitability of this solution thermometer to record UHT conditions and also confirms the extraordinary geological setting of the lowermost part of the Ivrea Zone.

Gradient estimation in dendritic reinforcement learning

We study synaptic plasticity in a complex neuronal cell model where NMDA-spikes can arise in certain dendritic zones. In the context of reinforcement learning, two kinds of plasticity rules are derived, zone reinforcement (ZR) and cell reinforcement (CR), which both optimize the expected reward by stochastic gradient ascent. For ZR, the synaptic plasticity response to the external reward signal is modulated exclusively by quantities which are local to the NMDA-spike initiation zone in which the synapse is situated. CR, in addition, uses nonlocal feedback from the soma of the cell, provided by mechanisms such as the backpropagating action potential. Simulation results show that, compared to ZR, the use of nonlocal feedback in CR can drastically enhance learning performance. We suggest that the availability of nonlocal feedback for learning is a key advantage of complex neurons over networks of simple point neurons, which have previously been found to be largely equivalent with regard to computational capability.