Unit of Neuroscience Information and Complexity

The magnetic ground state and electronic structure properties of solid solutions EuFe2–xNixAs2 (x = 0, 0.08, 0.12, 0.20, 0.40, 2.0) are investigated using the full-potential linearized augmented-plane wave method. The results show that Ni substitution has a robust influence on the structural, magnetic ground state, and electronic properties of EuFe2As2. When 10% of Ni is substituted for the Fe atoms, orthorhombic-tetragonal distortion is observed. Simultaneously, as the Ni concentration increases, the density of states (DOS) at the Fermi level (EF) decreases implying that the electronic state becomes more stable. In alloys with x = 0.20, 0.40, the partial DOS of both Eu and Fe, as well as their strong spin polarization, show a dominant contribution around EF. For x > 0.12, there is a stronger interband scattering, as evidenced by a large number of electronic band crossing EF. The multi-sheet structure is revealed by the Fermi surface topologies in all compounds. The electron localization functions show metallic and covalent bonds in EuFe2As2, EuFe1.6Ni0.4As2, and EuNi2As2 with bonds in EuFe2As2 being the most covalent.

The development of the human brain is a complex process that starts during early pregnancy and extends until the end of adolescence. In parallel to morphological changes in brain size and gyrification, several microstructural changes occur in the cortex, such as the development of dendritic arborization, synaptogenesis and pruning, and fiber myelination. Magnetic Resonance Imaging (MRI) can provide indirect markers of these mechanisms through the mapping of quantitative parameters. Here, we used a dedicated methodological framework to perform reliable voxel-wise analyses over the infant cortex. The examination of hemispheric asymmetries in microstructure required careful alignment of morphological asymmetries through registration of native and flipped brains using a 2-step matching strategy of sulci (DISCO approach) and cortical ribbon (DARTEL approach). We tested the potential of this approach in 1-to-5-month-old infants, with a focus on cortical longitudinal diffusivity from Diffusion Tensor Imaging (DTI). This enabled us to unravel different microstructural evolution patterns of specific sensorimotor and language regions in the left and right hemispheres.

Advances in experimental techniques and computational power allowing researchers to gather anatomical and electrophysiological data at unprecedented levels of detail have fostered the development of increasingly complex models in computational neuroscience. Large-scale, biophysically detailed cell models pose a particular set of computational challenges, and this has led to the development of a number of domain-specific simulators. At the other level of detail, the ever growing variety of point neuron models increases the implementation barrier even for those based on the relatively simple integrate-and-fire neuron model. Independently of the model complexity, all modeling methods crucially depend on an efficient and accurate transformation of mathematical model descriptions into efficiently executable code. Neuroscientists usually publish model descriptions in terms of the mathematical equations underlying them. However, actually simulating them requires they be translated into code. This can cause problems because errors may be introduced if this process is carried out by hand, and code written by neuroscientists may not be very computationally efficient. Furthermore, the translated code might be generated for different hardware platforms, operating system variants or even written in different languages and thus cannot easily be combined or even compared. Two main approaches to addressing this issues have been followed. The first is to limit users to a fixed set of optimized models, which limits flexibility. The second is to allow model definitions in a high level interpreted language, although this may limit performance. Recently, a third approach has become increasingly popular: using code generation to automatically translate high level descriptions into efficient low level code to combine the best of previous approaches. This approach also greatly enriches efforts to standardize simulator-independent model description languages. In the past few years, a number of code generation pipelines have been developed in the computational neuroscience community, which differ considerably in aim, scope and functionality. This article provides an overview of existing pipelines currently used within the community and contrasts their capabilities and the technologies and concepts behind them.

Many neurons possess dendrites enriched with sodium channels and are capable of generating action potentials. However, the role of dendritic sodium spikes remain unclear. Here, we study computational models of neurons to investigate the functional effects of dendritic spikes. In agreement with previous studies, we found that point neurons or neurons with passive dendrites increase their somatic firing rate in response to the correlation of synaptic bombardment for a wide range of input conditions, i.e. input firing rates, synaptic conductances, or refractory periods. However, neurons with active dendrites show the opposite behavior: for a wide range of conditions the firing rate decreases as a function of correlation. We found this property in three types of models of dendritic excitability: a Hodgkin-Huxley model of dendritic spikes, a model with integrate and fire dendrites, and a discrete-state dendritic model. We conclude that fast dendritic spikes confer much broader computational properties to neurons, sometimes opposite to that of point neurons.

We describe here an attempt to compose music, constrained by recordings of brain activity where excitatory and inhibitory neurons were discriminated and used to trigger simple tones or more complex sounds. We used experimental recordings of brain activity under the form of “spikes”, recorded with microelectrodes in human subjects. The recordings come from different sources, which have been all published in the neuroscience literature. We emphasize here the natural rhythmical activity of neurons, in particular, that of inhibitory neurons. Inhibitory neurons are thus naturally suited for driving bass sounds and rhythmic sections. The sparser activity of excitatory neurons is exploited here to reveal melodic capabilities, which are sometimes exacerbated by subjective choice of scales and tones. We explain step by step how this can be done and provide examples of musical sequences and tracks composed from neuronal activity during different brain states, such as wakefulness, deep sleep, or paradoxical sleep (dreaming). We suggest to extend this approach to more global signals, such as the electroencephalogram or neuroimaging signals.

Voltage-sensitive dye imaging (VSDi) has revealed fundamental properties of neocortical processing at macroscopic scales. Since for each pixel VSDi signals report the average membrane potential over hundreds of neurons, it seems natural to use a mean-field formalism to model such signals. Here, we present a mean-field model of networks of Adaptive Exponential (AdEx) integrate-and-fire neurons, with conductance-based synaptic interactions. We study a network of regular-spiking (RS) excitatory neurons and fast-spiking (FS) inhibitory neurons. We use a Master Equation formalism, together with a semi-analytic approach to the transfer function of AdEx neurons to describe the average dynamics of the coupled populations. We compare the predictions of this mean-field model to simulated networks of RS-FS cells, first at the level of the spontaneous activity of the network, which is well predicted by the analytical description. Second, we investigate the response of the network to time-varying external input, and show that the mean-field model predicts the response time course of the population. Finally, to model VSDi signals, we consider a one-dimensional ring model made of interconnected RS-FS mean-field units. We found that this model can reproduce the spatio-temporal patterns seen in VSDi of awake monkey visual cortex as a response to local and transient visual stimuli. Conversely, we show that the model allows one to infer physiological parameters from the experimentally-recorded spatio-temporal patterns.