A new generation of terminal copper nitrenes and their application in aromatic C-H amination reactions.

Copper nitrene complexes are highly reactive species and are known as intermediates in the copper catalyzed C-H amination. In this study, three novel copper tosyl nitrene complexes were synthesized at low temperatures, stabilized with heteroscorpionate ligands of the bis(pyrazolyl)methane family. The copper nitrenes were obtained by the reaction of a copper(i) acetonitrile complex with SPhINTs in dichloromethane. We show that the ligand design has a major influence on the catalytic activity and the thermal stability of the copper nitrene complex. Not only the choice of the third N donor, but also the substituent in the 5-position of the pyrazolyl moiety, have an impact on the stability. Furthermore, the novel copper nitrene complexes were used for catalytic aziridination of styrenes and C-H amination reactions of aromatic and aliphatic substrates under mild reaction conditions. Even challenging substrates like benzene and cyclohexane were aminated with good yields. The copper nitrene complexes were characterized using UV/Vis spectroscopy, low temperature Evans NMR spectroscopy, density functional theory, domain-based local pair natural orbital coupled cluster calculations (DLPNO-CCSD(T)) and cryo-UHR mass spectrometry.

CAVIAR: a 45k neuron, 5M synapse, 12G connects/s AER hardware sensory-processing- learning-actuating system for high-speed visual object recognition and tracking.

This paper describes CAVIAR, a massively parallel hardware implementation of a spike-based sensing-processing-learning-actuating system inspired by the physiology of the nervous system. CAVIAR uses the asychronous address-event representation (AER) communication framework and was developed in the context of a European Union funded project. It has four custom mixed-signal AER chips, five custom digital AER interface components, 45k neurons (spiking cells), up to 5M synapses, performs 12G synaptic operations per second, and achieves millisecond object recognition and tracking latencies.

Computation with spikes in a winner-take-all network.

The winner-take-all (WTA) computation in networks of recurrently connected neurons is an important decision element of many models of cortical processing. However, analytical studies of the WTA performance in recurrent networks have generally addressed rate-based models. Very few have addressed networks of spiking neurons, which are relevant for understanding the biological networks themselves and also for the development of neuromorphic electronic neurons that commmunicate by action potential like address-events. Here, we make steps in that direction by using a simplified Markov model of the spiking network to examine analytically the ability of a spike-based WTA network to discriminate the statistics of inputs ranging from stationary regular to nonstationary Poisson events. Our work extends previous theoretical results showing that a WTA recurrent network receiving regular spike inputs can select the correct winner within one interspike interval. We show first for the case of spike rate inputs that input discrimination and the effects of self-excitation and inhibition on this discrimination are consistent with results obtained from the standard rate-based WTA models. We also extend this discrimination analysis of spiking WTAs to nonstationary inputs with time-varying spike rates resembling statistics of real-world sensory stimuli. We conclude that spiking WTAs are consistent with their continuous counterparts for steady-state inputs, but they also exhibit high discrimination performance with nonstationary inputs.