COCONET

Connectivity in Complex Networks of interacting stochastic nonlinear systems. Applications in neuroscience

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 Obiettivo del progetto (Objective)

'The context of the project is the study of networks of interacting nonlinear systems with application to neuroscience. The project adresses two timely and innovative questions: - Are we able to estimate the connectivity/directivity graph from the observation? - What is the influence of the network topology on the information processing of the network? To answer the questions we develop four tracks of research: 1- Practical inference for connectivity and directivity in complex networks (statistics, kernel methods, information theory) 2- Synthesis of multivariate signals interacting according to a complex network. (stochastic processes) 3- Information processing by pooling networks (information theory, modeling of neural of spiking neurons) 4- Taking into account directionality : directed information theory in neural networks (feedback/feedforward in neural networks) Outgoing phase of the project hosted by the University of Melbourne, Dept. of Mathematics and Statistics with collaboration with the neuroengineering group and the Center for Neural engineering. Return phase hosted by CNRS/ GIPSAlab, Grenoble, France. Expected outcomes and benefits for the EU includes: 1- The design of a practical methodology for inference of connectivity/directionality between signals with nonproperties (nonstationarity, nonGaussianity, ...). Designed for neuroscience applications, but easily extended to other scientific domains. 2. Enriched understanding of the influence of the topology of networks onto information processing. 3. Enriched understanding of the importance of feedback/feedforward onto the information processing of neural networks. Anticipated related outcome includes improvements in prostheses, brain computer interfaces, better understanding in diseases such as epilepsy. Expected experience acquired in management of mulidisciplinary project through the collaboration with Math/stat dept and Center for Neural Engineering'

Introduzione (Teaser)

EU-funded scientists developed numerous models and methods for describing biologically realistic neuronal communication. The models not only shed light on the workings of the brain, they are also important for other applications of signal processing.

Descrizione progetto (Article)

Computational neuroscience is distinguished from machine learning in that it employs biologically realistic neurons and nervous system networks, including the spatiotemporal dynamics of network connectivity. Such consideration requires mathematical models of highly non-linear stochastic (random) systems. Neural network modelling is therefore an excellent case study for the development of both signal processing tools and neuroscience knowledge.

Scientists modelled complex neural networks of interacting stochastic non-linear systems with EU funding of the project COCONET. The main focus was on the role of network topology and connectivity/directionality in information processing. These themes were addressed thorough research exploiting statistics, kernel methods, information theory and models of biological neurons.

Among the numerous results, the team showed that background neural activity (synaptic noise) can actually facilitate spatiotemporal coding of connection strength with a very small time lag. The team also made major progress in developing much-needed representations of multivariate processes (multiple variables recording the same event) that are well-represented by fractals (useful in modelling partly random phenomena). Although many fractal-based models exist for single-variable systems, multivariate ones were lacking. They are necessary to represent, for example, simultaneous recordings of brain activity by numerous sensors or electrodes covering the scalp.

The team also studied instantaneous signal coupling and cases where two signals appear to be coupled but actually are not. A special model of coherence provided insight into brain communication during sleep states.

An original mathematical approach for studying connectivity in networks has been presented to a broader audience at an international conference on signal processing. Finally, a toolbox of procedures for studying network connectivity incorporates all the above. It is freely available in both Python and Matlab code through the http://www.gipsa-lab.grenoble-inp.fr/ (GIPSA-lab website).

COCONET scientists developed and applied advanced mathematical models of non-linear stochastic systems to studies of brain interconnectivity and signal processing. The methods developed are equally applicable to other systems and fields, including economics and currency exchange rates. The models have provided insight into the nature of signal representation in the brain, and could find use in brain-computer interfaces, prosthetics and even epilepsy treatment.