Probing spin dynamics and quantum relaxation in LiY0.998Ho0.002F4 via 19F NMR

We report measurements of 19F nuclear spin-lattice relaxation 1/T1 as a function of temperature and external magnetic field in LiY0.998Ho0.002F4 single crystal, a single-ion magnet exhibiting interesting quantum effects. The 19F 1/T1 is found to depend on the coupling with the diluted rare-earth (RE) moments. Depending on the temperature range, a fast spin diffusion regime or a diffusion limited regime is encountered. In both cases we find it possible to use the 19F nucleus as a probe of the rare-earth spin dynamics. The results for 1/T1 show a behavior similar to that observed in molecular nanomagnets, a result which we attribute to the discreteness of the energy levels in both cases. At intermediate temperatures the lifetime broadening of the crystal field split RE magnetic levels follows a T3 power law. At low temperature the field dependence of 1/T1 shows peaks in correspondence to the critical magnetic fields for energy level crossings (LC). The results can be explained by inelastic scattering between the fluorine nuclear spins and the RE magnetic levels. A key result of this study is that the broadening of the levels at LC is found to be become extremely small at low temperatures, about 1.7 mT, a value which is comparable to the weak dipolar fields at the RE lattice positions. Thus, unlike the molecular magnets, decoherence effects are strongly suppressed, and it may be possible to measure directly the level repulsions at avoided level crossings.Comment: 21 pages, 5 figure

Decoding the activity of neuronal populations in macaque primary visual cortex

Visual function depends on the accuracy of signals carried by visual cortical neurons. Combining information across neurons should improve this accuracy because single neuron activity is variable. We examined the reliability of information inferred from populations of simultaneously recorded neurons in macaque primary visual cortex. We considered a decoding framework that computes the likelihood of visual stimuli from a pattern of population activity by linearly combining neuronal responses and tested this framework for orientation estimation and discrimination. We derived a simple parametric decoder assuming neuronal independence and a more sophisticated empirical decoder that learned the structure of the measured neuronal response distributions, including their correlated variability. The empirical decoder used the structure of these response distributions to perform better than its parametric variant, indicating that their structure contains critical information for sensory decoding. These results show how neuronal responses can best be used to inform perceptual decision-making

From neuronal populations to behavior: a computational journey

Cognitive behaviors originate in the responses of neuronal populations. We have a reasonable understanding of how the activity of a single neuron can be related to a specific behavior. However, it is still unclear how more complex behaviors are inferred from the responses of neuronal populations. This is a particularly timely problem because multi-neuronal recording techniques have recently become increasingly available, simultaneously spurring advances in the analysis of neuronal population data. These developments are, however, constrained by the challenges of combining theoretical and experimental approaches because both approaches have their unique set of constraints. A solution to this problem is to design computational models that are either derived or inspired by cortical computations

Fermi-liquid effects in the Fulde-Ferrell-Larkin-Ovchinnikov state of two-dimensional d-wave superconductors

We study the effects of Fermi-liquid interactions on quasi-two-dimensional d-wave superconductors in a magnetic field. The phase diagram of the superconducting state, including the periodic Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state in high magnetic fields, is discussed for different strengths of quasiparticle many-body interactions within Landau's theory of Fermi liquids. Decreasing the Fermi-liquid parameter $F_0^a$ causes the magnetic spin susceptibility to increase, which in turn leads to a reduction of the FFLO phase. It is shown that a negative $F_0^a$ results in a first-order phase transition from the normal to the uniform superconducting state in a finite temperature interval. Finally, we discuss the thermodynamic implications of a first-order phase transition for CeCoIn$_5$.Comment: published version; removed direct comparison with experiment for the upper critical field, as required by the referee

Predicting oculomotor behaviour from correlated populations of posterior parietal neurons

Oculomotor function critically depends on how signals representing saccade direction and eye position are combined across neurons in the lateral intraparietal (LIP) area of the posterior parietal cortex. Here we show that populations of parietal neurons exhibit correlated variability, and that using these interneuronal correlations yields oculomotor predictions that are more accurate and also less uncertain. The structure of LIP population responses is therefore essential for reliable read-out of oculomotor behaviour

19F nuclear spin relaxation and spin diffusion effects in the single ion magnet LiYF4:Ho3+

Temperature and magnetic field dependences of the 19F nuclear spin-lattice relaxation in a single crystal of LiYF4 doped with holmium are described by an approach based on a detailed consideration of the magnetic dipole-dipole interactions between nuclei and impurity paramagnetic ions and nuclear spin diffusion processes. The observed non-exponential long time recovery of the nuclear magnetization after saturation at intermediate temperatures is in agreement with predictions of the spin-diffusion theory in a case of the diffusion limited relaxation. At avoided level crossings in the spectrum of electron-nuclear states of the Ho3+ ion, rates of nuclear spin-lattice relaxation increase due to quasi-resonant energy exchange between nuclei and paramagnetic ions, in contrast to the predominant role played by electronic cross-relaxation processes in the low-frequency ac-susceptibility.Comment: 27 pages total, 5 figures, accepted for publication, Eur. Phys. J.

Brain–machine interface for eye movements

A number of studies in tetraplegic humans and healthy nonhuman primates (NHPs) have shown that neuronal activity from reach-related cortical areas can be used to predict reach intentions using brain–machine interfaces (BMIs) and therefore assist tetraplegic patients by controlling external devices (e.g., robotic limbs and computer cursors). However, to our knowledge, there have been no studies that have applied BMIs to eye movement areas to decode intended eye movements. In this study, we recorded the activity from populations of neurons from the lateral intraparietal area (LIP), a cortical node in the NHP saccade system. Eye movement plans were predicted in real time using Bayesian inference from small ensembles of LIP neurons without the animal making an eye movement. Learning, defined as an increase in the prediction accuracy, occurred at the level of neuronal ensembles, particularly for difficult predictions. Population learning had two components: an update of the parameters of the BMI based on its history and a change in the responses of individual neurons. These results provide strong evidence that the responses of neuronal ensembles can be shaped with respect to a cost function, here the prediction accuracy of the BMI. Furthermore, eye movement plans could be decoded without the animals emitting any actual eye movements and could be used to control the position of a cursor on a computer screen. These findings show that BMIs for eye movements are promising aids for assisting paralyzed patients

Inferring eye position from populations of lateral intraparietal neurons

Understanding how the brain computes eye position is essential to unraveling high-level visual functions such as eye movement planning, coordinate transformations and stability of spatial awareness. The lateral intraparietal area (LIP) is essential for this process. However, despite decades of research, its contribution to the eye position signal remains controversial. LIP neurons have recently been reported to inaccurately represent eye position during a saccadic eye movement, and to be too slow to support a role in high-level visual functions. We addressed this issue by predicting eye position and saccade direction from the responses of populations of LIP neurons. We found that both signals were accurately predicted before, during and after a saccade. Also, the dynamics of these signals support their contribution to visual functions. These findings provide a principled understanding of the coding of information in populations of neurons within an important node of the cortical network for visual-motor behaviors