4,579 research outputs found
Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches
In the past two decades, functional Magnetic Resonance Imaging has been used
to relate neuronal network activity to cognitive processing and behaviour.
Recently this approach has been augmented by algorithms that allow us to infer
causal links between component populations of neuronal networks. Multiple
inference procedures have been proposed to approach this research question but
so far, each method has limitations when it comes to establishing whole-brain
connectivity patterns. In this work, we discuss eight ways to infer causality
in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality,
Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and
Transfer Entropy. We finish with formulating some recommendations for the
future directions in this area
JIDT: An information-theoretic toolkit for studying the dynamics of complex systems
Complex systems are increasingly being viewed as distributed information
processing systems, particularly in the domains of computational neuroscience,
bioinformatics and Artificial Life. This trend has resulted in a strong uptake
in the use of (Shannon) information-theoretic measures to analyse the dynamics
of complex systems in these fields. We introduce the Java Information Dynamics
Toolkit (JIDT): a Google code project which provides a standalone, (GNU GPL v3
licensed) open-source code implementation for empirical estimation of
information-theoretic measures from time-series data. While the toolkit
provides classic information-theoretic measures (e.g. entropy, mutual
information, conditional mutual information), it ultimately focusses on
implementing higher-level measures for information dynamics. That is, JIDT
focusses on quantifying information storage, transfer and modification, and the
dynamics of these operations in space and time. For this purpose, it includes
implementations of the transfer entropy and active information storage, their
multivariate extensions and local or pointwise variants. JIDT provides
implementations for both discrete and continuous-valued data for each measure,
including various types of estimator for continuous data (e.g. Gaussian,
box-kernel and Kraskov-Stoegbauer-Grassberger) which can be swapped at run-time
due to Java's object-oriented polymorphism. Furthermore, while written in Java,
the toolkit can be used directly in MATLAB, GNU Octave, Python and other
environments. We present the principles behind the code design, and provide
several examples to guide users.Comment: 37 pages, 4 figure
Investigating Information Flows in Spiking Neural Networks With High Fidelity
The brains of many organisms are capable of a wide variety of complex computations. This capability must be undergirded by a more general purpose computational capacity. The exact nature of this capacity, how it is distributed across the brains of organisms and how it arises throughout the course of development is an open topic of scientific investigation.
Individual neurons are widely considered to be the fundamental computational units of brains. Moreover, the finest scale at which large scale recordings of brain activity can be performed is the spiking activity of neurons and our ability to perform these recordings over large numbers of neurons and with fine spatial resolution is increasing rapidly. This makes the spiking activity of individual neurons a highly attractive data modality on which to study neural computation.
The framework of information dynamics has proven to be a successful approach towards interrogating the capacity for general purpose computation. It does this by revealing the atomic information processing operations of information storage, transfer and modification. Unfortunately, the study of information flows and other information processing operations from the spiking activity of neurons has been severely hindered by the lack of effective tools for estimating these quantities on this data modality. This thesis remedies this situation by presenting an estimator for information flows, as measured by Transfer Entropy (TE), that operates in continuous time on event-based data such as spike trains. Unlike the previous approach to the estimation of this quantity, which discretised the process into time bins, this estimator operates on the raw inter-spike intervals. It is demonstrated to be far superior to the previous discrete-time approach in terms of consistency, rate of convergence and bias. Most importantly, unlike the discrete-time approach, which requires a hard tradeoff between capturing fine temporal precision or history effects occurring over reasonable time intervals, this estimator can capture history effects occurring over relatively large intervals without any loss of temporal precision.
This estimator is applied to developing dissociated cultures of cortical rat neurons, therefore providing the first high-fidelity study of information flows on spiking data. It is found that the spatial structure of the flows locks in to a significant extent. at the point of their emergence and that certain nodes occupy specialised computational roles as either transmitters, receivers or mediators of information flow. Moreover, these roles are also found to lock in early.
In order to fully understand the structure of neural information flows, however, we are required to go beyond pairwise interactions, and indeed multivariate information flows have become an important tool in the inference of effective networks from neuroscience data. These are directed networks where each node is connected to a minimal set of sources which maximally reduce the uncertainty in its present state. However, the application of multivariate information flows to the inference of effective networks from spiking data has been hampered by the above-mentioned issues with preexisting estimation techniques. Here, a greedy algorithm which iteratively builds a set of parents for each target node using multivariate transfer entropies, and which has already been well validated in the context of traditional discretely sampled time series, is adapted to use in conjunction with the newly developed estimator for event-based data. The combination of the greedy algorithm and continuous-time estimator is then validated on simulated examples for which the ground truth is known.
The new capabilities in the estimation of information flows and the inference of effective networks on event-based data presented in this work represent a very substantial step forward in our ability to perform these analyses on the ever growing set of high resolution, large scale recordings of interacting neurons. As such, this work promises to enable substantial quantitative insights in the future regarding how neurons interact, how they process information, and how this changes under different conditions such as disease
Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package
We introduce the \texttt{pyunicorn} (Pythonic unified complex network and
recurrence analysis toolbox) open source software package for applying and
combining modern methods of data analysis and modeling from complex network
theory and nonlinear time series analysis. \texttt{pyunicorn} is a fully
object-oriented and easily parallelizable package written in the language
Python. It allows for the construction of functional networks such as climate
networks in climatology or functional brain networks in neuroscience
representing the structure of statistical interrelationships in large data sets
of time series and, subsequently, investigating this structure using advanced
methods of complex network theory such as measures and models for spatial
networks, networks of interacting networks, node-weighted statistics or network
surrogates. Additionally, \texttt{pyunicorn} provides insights into the
nonlinear dynamics of complex systems as recorded in uni- and multivariate time
series from a non-traditional perspective by means of recurrence quantification
analysis (RQA), recurrence networks, visibility graphs and construction of
surrogate time series. The range of possible applications of the library is
outlined, drawing on several examples mainly from the field of climatology.Comment: 28 pages, 17 figure
The serotonergic psychedelic N,N-dipropyltryptamine alters information-processing dynamics in cortical neural circuits
Most of the recent work in psychedelic neuroscience has been done using
non-invasive neuroimaging, with data recorded from the brains of adult
volunteers under the influence of a variety of drugs. While this data provides
holistic insights into the effects of psychedelics on whole-brain dynamics, the
effects of psychedelics on the meso-scale dynamics of cortical circuits remains
much less explored. Here, we report the effects of the serotonergic psychedelic
N,N-diproptyltryptamine (DPT) on information-processing dynamics in a sample of
in vitro organotypic cultures made from rat cortical tissue. Three hours of
spontaneous activity were recorded: an hour of pre-drug control, and hour of
exposure to 10M DPT solution, and a final hour of washout, once again
under control conditions. We found that DPT reversibly alters information
dynamics in multiple ways: first, the DPT condition was associated with higher
entropy of spontaneous firing activity and reduced the amount of time
information was stored in individual neurons. Second, DPT also reduced the
reversibility of neural activity, increasing the entropy produced and
suggesting a drive away from equilibrium. Third, DPT altered the structure of
neuronal circuits, decreasing the overall information flow coming into each
neuron, but increasing the number of weak connections, creating a dynamic that
combines elements of integration and disintegration. Finally, DPT decreased the
higher-order statistical synergy present in sets of three neurons.
Collectively, these results paint a complex picture of how psychedelics
regulate information processing in meso-scale cortical tissue. Implications for
existing hypotheses of psychedelic action, such as the Entropic Brain
Hypothesis, are discussed.Comment: 19 pages, 2 figure
Informative and misinformative interactions in a school of fish
It is generally accepted that, when moving in groups, animals process
information to coordinate their motion. Recent studies have begun to apply
rigorous methods based on Information Theory to quantify such distributed
computation. Following this perspective, we use transfer entropy to quantify
dynamic information flows locally in space and time across a school of fish
during directional changes around a circular tank, i.e. U-turns. This analysis
reveals peaks in information flows during collective U-turns and identifies two
different flows: an informative flow (positive transfer entropy) based on fish
that have already turned about fish that are turning, and a misinformative flow
(negative transfer entropy) based on fish that have not turned yet about fish
that are turning. We also reveal that the information flows are related to
relative position and alignment between fish, and identify spatial patterns of
information and misinformation cascades. This study offers several
methodological contributions and we expect further application of these
methodologies to reveal intricacies of self-organisation in other animal groups
and active matter in general
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