Attractor Metadynamics in Adapting Neural Networks

Slow adaption processes, like synaptic and intrinsic plasticity, abound in the brain and shape the landscape for the neural dynamics occurring on substantially faster timescales. At any given time the network is characterized by a set of internal parameters, which are adapting continuously, albeit slowly. This set of parameters defines the number and the location of the respective adiabatic attractors. The slow evolution of network parameters hence induces an evolving attractor landscape, a process which we term attractor metadynamics. We study the nature of the metadynamics of the attractor landscape for several continuous-time autonomous model networks. We find both first- and second-order changes in the location of adiabatic attractors and argue that the study of the continuously evolving attractor landscape constitutes a powerful tool for understanding the overall development of the neural dynamics

A Novel Sparse Graphical Approach for Multimodal Brain Connectivity Inference

International audienceDespite the clear potential benefits of combining fMRI and diffusion MRI in learning the neural pathways that underlie brain functions, little methodological progress has been made in this direction. In this paper, we propose a novel multimodal integration approach based on sparse Gaussian graphical model for estimating brain connectivity. Casting functional connectivity estimation as a sparse inverse covariance learning problem, we adapt the level of sparse penalization on each connection based on its anatomical capacity for functional interactions. Functional connections with little anatomical support are thus more heavily penalized. For validation, we showed on real data collected from a cohort of 60 subjects that additionally modeling anatomical capacity significantly increases subject consistency in the detected connection patterns. Moreover, we demonstrated that incorporating a connectivity prior learned with our multimodal connectivity estimation approach improves activation detection

Immune pathways and defence mechanisms in honey bees Apis mellifera

Social insects are able to mount both group-level and individual defences against pathogens. Here we focus on individual defences, by presenting a genome-wide analysis of immunity in a social insect, the honey bee Apis mellifera. We present honey bee models for each of four signalling pathways associated with immunity, identifying plausible orthologues for nearly all predicted pathway members. When compared to the sequenced Drosophila and Anopheles genomes, honey bees possess roughly one-third as many genes in 17 gene families implicated in insect immunity. We suggest that an implied reduction in immune flexibility in bees reflects either the strength of social barriers to disease, or a tendency for bees to be attacked by a limited set of highly coevolved pathogens

A place for time: the spatiotemporal structure of neural dynamics during natural audition

We use functional magnetic resonance imaging (fMRI) to analyze neural responses to natural auditory stimuli. We characterize the fMRI time series through the shape of the voxel power spectrum and find that the timescales of neural dynamics vary along a spatial gradient, with faster dynamics in early auditory cortex and slower dynamics in higher order brain regions. The timescale gradient is observed through the unsupervised clustering of the power spectra of individual brains, both in the presence and absence of a stimulus, and is enhanced in the stimulus-locked component that is shared across listeners. Moreover, intrinsically faster dynamics occur in areas that respond preferentially to momentary stimulus features, while the intrinsically slower dynamics occur in areas that integrate stimulus information over longer timescales. These observations connect the timescales of intrinsic neural dynamics to the timescales of information processing, suggesting a temporal organizing principle for neural computation across the cerebral cortex. © 2013 the American Physiological Society

MR connectomics: Principles and challenges.

MR connectomics is an emerging framework in neuro-science that combines diffusion MRI and whole brain tractography methodologies with the analytical tools of network science. In the present work we review the current methods enabling structural connectivity mapping with MRI and show how such data can be used to infer new information of both brain structure and function. We also list the technical challenges that should be addressed in the future to achieve high-resolution maps of structural connectivity. From the resulting tremendous amount of data that is going to be accumulated soon, we discuss what new challenges must be tackled in terms of methods for advanced network analysis and visualization, as well data organization and distribution. This new framework is well suited to investigate key questions on brain complexity and we try to foresee what fields will most benefit from these approaches

Slow cortical dynamics and the accumulation of information over long timescales

Making sense of the world requires us to processinformation over multiple timescales. We sought to identify brain regions that accumulate information over short and long timescales and to characterize the distinguishing features of their dynamics. We recorded electrocorticographic (ECoG) signals from individuals watching intact and scrambled movies. Within sensory regions, fluctuations of high-frequency (64-200 Hz) power reliably tracked instantaneous low-level properties of the intact and scrambled movies. Within higher order regions, the power fluctuations were more reliable for the intact movie than the scrambled movie, indicating that these regions accumulate information over relatively long time periods (several seconds or longer). Slow (<0.1 Hz) fluctuations of high-frequency power with time courses locked to the movies were observed throughout the cortex. Slow fluctuations were relatively larger in regions that accumulated information over longer time periods, suggesting a connection between slow neuronal population dynamics and temporally extended information processing