Neuronal populations with reciprocal inhibition and rebound currents: Effects of synaptic and threshold noise

Coombes, Stephen; Doole, SH

doi:10.1103/physreve.54.4054

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…We discuss examples of both. In terms of emergent behaviors, we will consider dynamics on structure (Bressler and Tognoli, 2006;Buice and Cowan, 2009;Coombes and Doole, 1996;Freeman, 1994Freeman, , 2005Kriener et al, 2008;Robinson et al, 1997;Rubinov et al, 2009;Tsuda, 2001) and how this behavior has been applied to characterizing autonomous or endogenous fluctuations in fMRI (e.g., Deco et al, 2009Deco et al, , 2011Ghosh et al, 2008;Honey et al, 2007Honey et al, , 2009). We will then consider causal models that are used to explain empirical observations.…”

Section: Modeling Distributed Neuronal Systemsmentioning

confidence: 99%

Functional and Effective Connectivity: A Review

2011

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Over the past 20 years, neuroimaging has become a predominant technique in systems neuroscience. One might envisage that over the next 20 years the neuroimaging of distributed processing and connectivity will play a major role in disclosing the brain's functional architecture and operational principles. The inception of this journal has been foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. I accepted the invitation to write this review with great pleasure and hope to celebrate and critique the achievements to date, while addressing the challenges ahead.

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Section: Modeling Distributed Neuronal Systemsmentioning

confidence: 99%

Functional and Effective Connectivity: A Review

2011

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“…In other words, they are used to simulate brain-like dynamics or are used explicitly to predict observed brain activity. We discuss examples of both: in terms of emergent behaviors, we will consider dynamics on structure (Freeman, 1994; Coombes and Doole, 1996; Robinson et al, 1997; Tsuda, 2001; Freeman, 2005; Bressler and Tognoli, 2006; Kriener et al, 2008; Rubinov et al, 2009; Buice and Cowan, 2009) and how this has been applied to characterizing autonomous or endogenous fluctuations in fMRI signals ( e.g. , Honey et al, 2007, 2009; Deco et al, 2009).…”

Section: Modeling Distributed Neuronal Systemsmentioning

confidence: 99%

Distributed processing; distributed functions?

Fox

Friston

2012

NeuroImage

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After more than twenty years busily mapping the human brain, what have we learned from neuroimaging? This review (coda) considers this question from the point of view of structure–function relationships and the two cornerstones of functional neuroimaging; functional segregation and integration. Despite remarkable advances and insights into the brain’s functional architecture, the earliest and simplest challenge in human brain mapping remains unresolved: We do not have a principled way to map brain function onto its structure in a way that speaks directly to cognitive neuroscience. Having said this, there are distinct clues about how this might be done: First, there is a growing appreciation of the role of functional integration in the distributed nature of neuronal processing. Second, there is an emerging interest in data-driven cognitive ontologies, i.e., that are internally consistent with functional anatomy. We will focus this review on the growing momentum in the fields of functional connectivity and distributed brain responses and consider this in the light of meta-analyses that use very large data sets to disclose large-scale structure–function mappings in the human brain.

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“…We discuss examples of both. In terms of emergent behaviors, we will consider dynamics on structure [61][62][63][64][65][66][67][68][69] and how this behavior has been applied to characterizing autonomous or endogenous fluctuations in fMRI [70][71][72][73]. This section concludes with recent advances in dynamic causal modeling of directed neuronal interactions that support endogenous fluctuations.…”

Section: Biophysical Modelling Of Neuronal Dynamicsmentioning

confidence: 99%

The Connected Brain: Causality, models, and intrinsic dynamics

Razi

Friston

2016

IEEE Signal Process. Mag.

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-Recently, there have been several concerted international efforts -the BRAIN initiative, European Human Brain Project and the Human Connectome Project, to name a few -that hope to revolutionize our understanding of the connected brain. Over the past two decades, functional neuroimaging has emerged as the predominant technique in systems neuroscience. This is foreshadowed by an ever increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. In this article, we summarize pedagogically the (deep) history of brain mapping. We will highlight the theoretical advances made in the (dynamic) causal modelling of brain function -that may have escaped the wider audience of this article -and provide a brief overview of recent developments and interesting clinical applications. We hope that this article will engage the signal processing community by showcasing the inherently multidisciplinary nature of this important topic and the intriguing questions that are being addressed.Index terms -dynamic causal modelling · effective connectivity · functional connectivity · resting state · fMRI · graph · Bayesian · intrinsic dynamics 2

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Neuronal populations with reciprocal inhibition and rebound currents: Effects of synaptic and threshold noise

Abstract: . Neuronal populations with reciprocal inhibition and rebound currents: effects of synaptic and threshold noise.

Cited by 10 publications

References 22 publications

Functional and Effective Connectivity: A Review

Functional and Effective Connectivity: A Review

Distributed processing; distributed functions?

The Connected Brain: Causality, models, and intrinsic dynamics

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