Optimal modularity and memory capacity of neural reservoirs

The neural network is a powerful computing framework that has been exploited by biological evolution and by humans for solving diverse problems. Although the computational capabilities of neural networks are determined by their structure, the current understanding of the relationships between a neural network's architecture and function is still primitive. Here we reveal that neural network's modular architecture plays a vital role in determining the neural dynamics and memory performance of the network of threshold neurons. In particular, we demonstrate that there exists an optimal modularity for memory performance, where a balance between local cohesion and global connectivity is established, allowing optimally modular networks to remember longer. Our results suggest that insights from dynamical analysis of neural networks and information spreading processes can be leveraged to better design neural networks and may shed light on the brain's modular organization

Ensemble tractography

Fiber tractography uses diffusion MRI to estimate the trajectory and cortical projection zones of white matter fascicles in the living human brain. There are many different tractography algorithms and each requires the user to set several parameters, such as curvature threshold. Choosing a single algorithm with a specific parameters sets poses two challenges. First, different algorithms and parameter values produce different results. Second, the optimal choice of algorithm and parameter value may differ between different white matter regions or different fascicles, subjects, and acquisition parameters. We propose using ensemble methods to reduce algorithm and parameter dependencies. To do so we separate the processes of fascicle generation and evaluation. Specifically, we analyze the value of creating optimized connectomes by systematically combining candidate fascicles from an ensemble of algorithms (deterministic and probabilistic) and sweeping through key parameters (curvature and stopping criterion). The ensemble approach leads to optimized connectomes that provide better cross-validatedprediction error of the diffusion MRI data than optimized connectomes generated using the singlealgorithms or parameter set. Furthermore, the ensemble approach produces connectomes that contain both short- and long-range fascicles, whereas single-parameter connectomes are biased towards one or the other. In summary, a systematic ensemble tractography approach can produce connectomes that are superior to standard single parameter estimates both for predicting the diffusion measurements and estimating white matter fascicles.Fil: Takemura, Hiromasa. University of Stanford; Estados Unidos. Osaka University; JapónFil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Wandell, Brian A.. University of Stanford; Estados UnidosFil: Pestilli, Franco. Indiana University; Estados Unido

The Structural and Kinematic Evolution of Central Star Clusters in Dwarf Galaxies and Their Dependence on Dark Matter Halo Profiles

Through a suite of direct N-body simulations, we explore how the structural and kinematic evolution of a star cluster located at the center of a dwarf galaxy is affected by the shape of its host's dark matter density profile. The stronger central tidal fields of cuspier halos minimize the cluster's ability to expand in response to mass loss due to stellar evolution during its early evolutionary stages and during its subsequent long-term evolution driven by two-body relaxation. Hence clusters evolving in cuspier dark matter halos are characterized by more compact sizes, higher velocity dispersions and remain approximately isotropic at all clustercentric distances. Conversely, clusters in cored halos can expand more and develop a velocity distribution profile that becomes increasingly radially anisotropic at larger clustercentric distances. Finally, the larger velocity dispersion of clusters evolving in cuspier dark matter profiles results in them having longer relaxation times. Hence clusters in cuspy galaxies relax at a slower rate and, consequently, they are both less mass segregated and farther from complete energy equipartition than cluster's in cored galaxies. Application of this work to observations allows for star clusters to be used as tools to measure the distribution of dark matter in dwarf galaxies and to distinguish isolated star clusters from ultra-faint dwarf galaxies.Comment: 8 pages, 7 figures, Accepted for publication in MNRA