Fair Attribution of Functional Contribution in Artificial and Biological Networks

Keinan, Alon; Sandbank, Ben; Hilgetag, Claus C.; Meilijson, Isaac; Ruppin, Eytan

doi:10.1162/0899766041336387

Cited by 100 publications

(131 citation statements)

References 46 publications

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“…For example, Tononi and Sporns stimulate selected subsets of a network with Gaussian noise and interpret the resulting mutual information between the subset and the rest of the network as a measure of their causal connectivity [10]. Similarly, Keinan et al assess the functional contribution of individual network elements by measuring network performance following lesions to subsets of elements [11]. While these approaches are in principle robust to artifacts induced by common input, in practice their use is restricted to situations in which networks can be repeatedly and reversibly perturbed, which for many biological systems is currently not possible.…”

Section: Discussionmentioning

confidence: 99%

“…The method is based on vector autoregressive modeling and 'Granger causality', adapted from time-series analysis [6,7], together with techniques from graph theory [8]. Unlike alternative approaches for determining causality [9,10,11], the method does not require perturbation or lesioning of network elements, and hence is well suited to analyzing data sets acquired during behavior from intact (simulated or biological) neural systems.…”

Section: Introductionmentioning

confidence: 99%

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Causal connectivity of evolved neural networks during behavior

Seth

2005

Network: Computation in Neural Systems

219

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Abstract.To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality', for characterizing causal interactions generated within intact neural mechanisms. This method, called causal connectivity analysis, is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Causal connectivity of evolved neural networks during behavior

Seth

2005

Network: Computation in Neural Systems

219

View full text Add to dashboard Cite

show abstract

“…For example, Tononi and Sporns (2003) stimulate selected subsets of a network with Gaussian noise and interpret the resulting mutual information between the subset and the rest of the network as a measure of causal influence. Similarly, Keinan et al (2004) assess causal influence by measuring network performance following selective lesions to subsets of elements. These interventionist approaches are in principle robust to artifacts induced by common input and thus allow identification of physical causal chains (Timme 2007).…”

Section: Relation To Other Causal Methodsmentioning

confidence: 99%

Causal networks in simulated neural systems

Seth

2007

Cogn Neurodyn

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Neurons engage in causal interactions with one another and with the surrounding body and environment. Neural systems can therefore be analyzed in terms of causal networks, without assumptions about information processing, neural coding, and the like. Here, we review a series of studies analyzing causal networks in simulated neural systems using a combination of Granger causality analysis and graph theory. Analysis of a simple targetfixation model shows that causal networks provide intuitive representations of neural dynamics during behavior which can be validated by lesion experiments. Extension of the approach to a neurorobotic model of the hippocampus and surrounding areas identifies shifting causal pathways during learning of a spatial navigation task. Analysis of causal interactions at the population level in the model shows that behavioral learning is accompanied by selection of specific causal pathways-''causal cores''-from among large and variable repertoires of neuronal interactions. Finally, we argue that a causal network perspective may be useful for characterizing the complex neural dynamics underlying consciousness.

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“…edges linking segregated clusters of brain regions. The issue of defining measures of robustness or vulnerability in brain networks is conceptually linked to the problem of objectively defining the functional contributions of individual network elements (Keinan et al, 2004). …”

Section: Measures Of Structural Brain Connectivitymentioning

confidence: 99%