Information processing dynamics in neural networks of macaque cerebral cortex reflect cognitive state and behavior

Varley, Thomas F.; Sporns, Olaf; Scherberger, Hansjörg; Dann, Benjamin

doi:10.1101/2021.09.05.458983

Cited by 4 publications

(11 citation statements)

References 132 publications

(281 reference statements)

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“…The field of network analysis of time series is still novel, and its application to neuroscience even moreso. This opens the door to a rich area to explore at the intersection of informationtheoretic, dynamic, and network science-based approaches (for a discussion of the intersection of dynamical systems and computational approaches to neuroscience, see Mediano et al, 2021;Varley et al, 2021a). By enabling time-resolved inference of complex state-spaces, network analysis of time series allows researchers to leverage the considerable power of network science and graph theory to questions of neural activity in ways that were not previously enabled by "classical" network neuroscience, and we are optimistic that these new methods will provide a powerful, complementary branch of network neuroscience with which to explore brain structure and function.…”

Section: Discussionmentioning

confidence: 99%

“…This is a non-trivial problem, which can be avoided by deriving both the signal analysis and connectivity analysis from the same underlying VNs. As evidence accumulates that dynamics, in addition to connectivity are essential for complex cognition (Ezaki et al, 2020;Varley et al, 2020Varley et al, , 2021a tools to simultaneously analyse dynamics and connectivity are likely to play a significant role in the future of computational neuroscience.…”

Section: Applications In Neurosciencementioning

confidence: 99%

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Network Analysis of Time Series: Novel Approaches to Network Neuroscience

Varley

Sporns

2022

Front. Neurosci.

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In the last two decades, there has been an explosion of interest in modeling the brain as a network, where nodes correspond variously to brain regions or neurons, and edges correspond to structural or statistical dependencies between them. This kind of network construction, which preserves spatial, or structural, information while collapsing across time, has become broadly known as “network neuroscience.” In this work, we provide an alternative application of network science to neural data: network-based analysis of non-linear time series and review applications of these methods to neural data. Instead of preserving spatial information and collapsing across time, network analysis of time series does the reverse: it collapses spatial information, instead preserving temporally extended dynamics, typically corresponding to evolution through some kind of phase/state-space. This allows researchers to infer a, possibly low-dimensional, “intrinsic manifold” from empirical brain data. We will discuss three methods of constructing networks from nonlinear time series, and how to interpret them in the context of neural data: recurrence networks, visibility networks, and ordinal partition networks. By capturing typically continuous, non-linear dynamics in the form of discrete networks, we show how techniques from network science, non-linear dynamics, and information theory can extract meaningful information distinct from what is normally accessible in standard network neuroscience approaches.

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Section: Discussionmentioning

confidence: 99%

Section: Applications In Neurosciencementioning

confidence: 99%

Network Analysis of Time Series: Novel Approaches to Network Neuroscience

Varley

Sporns

2022

Front. Neurosci.

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“…Rather, the goal is to offer a crisp window into the types of insights that can emerge. We draw preferentially on our own work out of convenience, but this work in turn was inspired by, and complements, other exemplary lines of empirical work (for examples, see [ 5 , 21 , 35 , 41 , 42 , 43 , 44 , 45 , 46 ]).…”

Section: Pid In Actionmentioning

confidence: 99%

“…These caveats have informed recent applications of PID to questions of neural dynamics. Varley et al [ 5 ] analyzed spiking neural activity from the pre-motor and motor regions of three macaques while the monkeys were engaged in a multi-phase behavioral task involving symbol recognition, motor planning, memory storage, and motor execution [ 52 ]. As with the in vitro work, this study began by inferring transfer entropy networks for each behavioral epoch and then calculated the PID for every triad in the resulting networks.…”

Section: Pid In Actionmentioning

confidence: 99%

“…This is not intended to be an exhaustive review of relevant findings but rather to serve as an approachable overview for those inside and outside neuroscience who may be interested in how multivariate information-theoretic analysis can be applied to questions of interest to them. For those already familiar with neuroscience and information theory, we take this opportunity to highlight recent advances in our own work and our vision for future directions in this area (e.g., [ 2 , 3 , 4 , 5 ]).…”

Section: Introductionmentioning

confidence: 99%

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Revealing the Dynamics of Neural Information Processing with Multivariate Information Decomposition

Newman

Varley

Parakkattu

et al. 2022

Entropy

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The varied cognitive abilities and rich adaptive behaviors enabled by the animal nervous system are often described in terms of information processing. This framing raises the issue of how biological neural circuits actually process information, and some of the most fundamental outstanding questions in neuroscience center on understanding the mechanisms of neural information processing. Classical information theory has long been understood to be a natural framework within which information processing can be understood, and recent advances in the field of multivariate information theory offer new insights into the structure of computation in complex systems. In this review, we provide an introduction to the conceptual and practical issues associated with using multivariate information theory to analyze information processing in neural circuits, as well as discussing recent empirical work in this vein. Specifically, we provide an accessible introduction to the partial information decomposition (PID) framework. PID reveals redundant, unique, and synergistic modes by which neurons integrate information from multiple sources. We focus particularly on the synergistic mode, which quantifies the “higher-order” information carried in the patterns of multiple inputs and is not reducible to input from any single source. Recent work in a variety of model systems has revealed that synergistic dynamics are ubiquitous in neural circuitry and show reliable structure–function relationships, emerging disproportionately in neuronal rich clubs, downstream of recurrent connectivity, and in the convergence of correlated activity. We draw on the existing literature on higher-order information dynamics in neuronal networks to illustrate the insights that have been gained by taking an information decomposition perspective on neural activity. Finally, we briefly discuss future promising directions for information decomposition approaches to neuroscience, such as work on behaving animals, multi-target generalizations of PID, and time-resolved local analyses.

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Response inhibition in premotor cortex corresponds to a complex reshuffle of the mesoscopic information network

Bardella

Giarrocco

Andujar

et al. 2021

Preprint

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Despite recent works have investigated functional and effective cortical networks in animal models, the dynamical information transfer among functional modules underneath cognitive control is still largely unknown. Here we addressed the issue by using transfer entropy and graph theory methods on neural activities recorded from a multielectrode (96 recording sites) array in the dorsal premotor cortex of rhesus monkeys. We focused our analysis on the decision time of a stop-signal (countermanding) task. When comparing trials with successful inhibition to those with generated movement we found evidence of heterogeneous interacting modules described by 4 main classes, hierarchically organized. Interestingly, the hierarchical organization resulted different in the two type of trials. Our results suggest that motor decisions are based on the local re-organization of the premotor cortical network

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Information processing dynamics in neural networks of macaque cerebral cortex reflect cognitive state and behavior

Cited by 4 publications

References 132 publications

Network Analysis of Time Series: Novel Approaches to Network Neuroscience

Network Analysis of Time Series: Novel Approaches to Network Neuroscience

Revealing the Dynamics of Neural Information Processing with Multivariate Information Decomposition

Response inhibition in premotor cortex corresponds to a complex reshuffle of the mesoscopic information network

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