Modularity

Recent efforts have defined developmental trajectories for diverse neuroimaging biomarkers, allowing for the detection of deviation from normality on an individual level. The functional connectome captures how brain areas communicate and graph theory analyses can characterise this at a network level. We modeled typical developmental trajectories of the functional connectome using graph theory to explore network-level development during childhood and adolescence. Using resting state functional MRI, we measured functional connectivity between 106 ROIs (Harvard-Oxford atlas), in a cross-sectional

…oscillatory dynamics, heterogeneity, modularity, and conduction delays. However, it has remained unclear to what extent these serve computational purposes. Inspired by physiological findings, we simulate recurrent networks of damped harmonic oscillators (DHO) in which one network node represents the aggregate activity of an underlying E-I circuit such as a cortical column (HORNs, see figure), at the same time reducing single-node oscillatory dynamics to its most basic form. We successively endow the networks with additional biologi

To identify patterns of structural networks associated with cognitive phenotypes detected with robotic technology in patients with temporal lobe epilepsy (TLE). Network analysis was performed using multimodal MRI from TLE patients (n=22) and healthy subjects (n=20). Five tests with the Kinarm robot were used to evaluate five cognitive domains. Four graph theory metrics were computed to characterize structural networks. Structural and cognitive phenotypes were identified using an unsupervised learning algorithm, and the association between them evaluated. Three cognitive clusters were identifie

…The results show that the modularity of the neuronal network enhances classification performance in tasks involving multiple speech signals [3].Figure 1 outlines the experimental setup, employing a modular cultured neuronal network of rat cortical neurons on a patterned substrate. Neural activity was measure

The organization of neural circuitry plays a role in brain function. Investigations into this organization have generally had to trade off between coarse descriptions at a large scale and fine descriptions on a very small scale, due to the constraints of the tools and data processing methods available. Recently, reconstructions of tens to hundreds of thousands of neurons at synaptic resolution have been published, enabling investigations into the interplay between global, modular organization, and cell type-specific wiring. Analysis of these enormous data sets requires the development of new m

One of the fundamental functions of the brain is to flexibly plan and control movement production at different timescales to efficiently shape structured behaviors. I will present a model that clarifies how these complex computations could be performed in the mammalian brain, with an emphasis on the learning of an extendable library of autonomous motor motifs and the flexible stringing of these motifs in motor sequences. To build this model, we took advantage of the fact that the anatomy of the circuits involved is well known. Our results show how these architectural constraints lead to a prin

A wide variety of animals and some artificial agents can adapt their behavior to changing cues, contexts, and goals. But what neural network architectures support such behavioral flexibility? Agents with loosely structured network architectures and random connections can be trained over millions of trials to display flexibility in specific tasks, but many animals must adapt and learn with much less experience just to survive. Further, it has been challenging to understand how the structure of trained deep neural networks relates to their functional properties, an important objective for neuros

Threshold-linear networks (TLNs) display a wide variety of nonlinear dynamics including multistability, limit cycles, quasiperiodic attractors, and chaos. Over the past few years, we have developed a detailed mathematical theory relating stable and unstable fixed points of TLNs to graph-theoretic properties of the underlying network. In particular, we have discovered that a special type of unstable fixed points, corresponding to "core motifs," are predictive of dynamic attractors. Recently, we have used these ideas to classify dynamic attractors in a two-parameter family of inhibition-dominate