Causal networks in simulated neural systems

Abstract

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.

Measuring Autonomy and Emergence via Granger Causality

“…According to G-causality, Y causes X if the inclusion of past observations of Y reduces the prediction error of X in a linear regression model of X and Y, as compared to a model that includes only previous observations of X. Since its introduction, G-causality has found wide application in economics and many other fields, including neuroscience and climatology [16,38]. It is important to recognize that G-causality is a statistical formulation of causality, such that a significant G-causality interaction does not by itself imply the presence of a corresponding physical interaction [20].…”

Exposure to Mozart music reduces cognitive impairment in pilocarpine-induced status epilepticus rats

“…So far, the origin of cognitive deficits of TLE still remains unknown. Theoretically, many factors may affect cognitive ability, including seizures themselves (Marques et al 2007), interictal epileptiform discharges (Aldenkamp and Arends 2004), and the reorganization of the underlying neuronal circuitry (Morimoto et al 2004;Seth 2008;Brown 2013). Although antiepileptic drugs can relieve epileptic seizures, some studies have revealed that these drugs also have some adverse effects on cognition (Meador 2006).…”

More discussions for granger causality and new causality measures

“…To show whether the influence is a direct component or mediated by the third time series, conditional GC is defined (Geweke 1984;Freiwald et al 1999;Hesse et al 2003;Ding et al 2006;Oya et al 2007;Bressler and Anil 2010). In recent years there has been significant interest to discuss causal interactions between brain areas which are highly complex neural networks in both time and frequency domains (Freiwald et al 1999;Hesse et al 2003;Roebroeck et al 2005;Oya et al 2007;Wang et al 2007Wang et al , 2008; Atmanspacher and Rotter 2008;Gow et al 2008Gow et al , 2009Rajagovindan and Ding 2008;Seth 2008;Cadotte et al 2009;Zhang et al 2010).…”