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We investigated the local magnetism of the organic conductor, $\kappa$-(ET)$_{2}$Hg(SCN)$_{2}$Cl, with a quasi-triangular lattice of weakly dimerized molecules through $^{13}$C NMR spectroscopy. The NMR spectra and nuclear relaxation show that charge disproportionation occurs, associated with the metal-insulator transition at 31 K. The relaxation rate indicates that the paramagnetic spins in the insulating phase undergo a transition into a spin-singlet ground state with the emergence of orphan spins, a possible valence-bond-glass state. The present results are in high contrast to the spin-cluster paramagnetism of the electric dipole-liquid candidate, $\kappa$--(ET)$_{2}$Hg(SCN)$_{2}$Br, having nearly identical material parameters. This fact indicates that these two systems are on the verge between distinct phases in both charge and spin degrees of freedom; a spin-singlet charge-ordered state versus a spin-active Mott insulating state, competing with each other on a triangular lattice of dimerized sites.

Mutual interactions in many-body systems bring about a variety of exotic phases, among which liquid-like states failing to order due to frustration are of keen interest. Recently, an organic system with an anisotropic triangular lattice of molecular dimers has been suggested to host a dipole liquid arising from intradimer charge-imbalance instability, possibly offering an unprecedented stage for the spin degrees of freedom. Here we show that an extraordinary unordered(unfrozen) spin state having soft-matter-like spatiotemporal characteristics is substantiated in this system. $^1$H NMR spectra and magnetization measurements indicate that gigantic, staggered moments are non-linearly and inhomogeneously induced by magnetic field whereas the moments vanish in the zero-field limit. The analysis of the NMR relaxation rate signifies that the moments fluctuate at a characteristic frequency slowing down to below MHz at low temperatures. The inhomogeneity, local correlation, and slow dynamics indicative of middle-scale dynamical correlation length suggest a novel frustration-driven spin clusterization., Comment: 13 pages, 11 figures, 1 table

Neuroscientists today can measure activity from more neurons than ever before, and are facing the challenge of connecting these brain-wide neural recordings to computation and behavior. Here, we first describe emerging tools and technologies being used to probe large-scale brain activity and new approaches to characterize behavior in the context of such measurements. We next highlight insights obtained from large-scale neural recordings in diverse model systems, and argue that some of these pose a challenge to traditional theoretical frameworks. Finally, we elaborate on existing modelling frameworks to interpret these data, and argue that interpreting brain-wide neural recordings calls for new theoretical approaches that may depend on the desired level of understanding at stake. These advances in both neural recordings and theory development will pave the way for critical advances in our understanding of the brain.

With the increasing acquisition of large-scale neural recordings comes the challenge of inferring the computations they perform and understanding how these give rise to behavior. Here, we review emerging conceptual and technological advances that begin to address this challenge, garnering insights from both biological and artificial neural networks. We argue that neural data should be recorded during rich behavioral tasks, to model cognitive processes and estimate latent behavioral variables. Careful quantification of animal movements can also provide a more complete picture of how movements shape neural dynamics and reflect changes in brain state, such as arousal or stress. Artificial neural networks (ANNs) could serve as an important tool to connect neural dynamics and rich behavioral data. ANNs have already begun to reveal how particular behaviors can be optimally solved, generating hypotheses about how observed neural activity might drive behavior and explaining diversity in behavioral strategies.