On How Network Architecture Determines the Dominant Patterns of Spontaneous Neural Activity

By Roberto Fernández Galán. PLoS ONE, 2008

183 citations recorded by the source corpus.

In the absence of sensory stimulation, neocortical circuits display complex patterns of neural activity. These patterns are thought to reflect relevant properties of the network, including anatomical features like its modularity. It is also assumed that the synaptic connections of the network constrain the repertoire of emergent, spontaneous patterns. Although the link between network architecture and network activity has been extensively investigated in the last few years from different perspectives, our understanding of the relationship between the network connectivity and the structure of its spontaneous activity is still incomplete. Using a general mathematical model of neural dynamics we have studied the link between spontaneous activity and the underlying network architecture. In particular, here we show mathematically how the synaptic connections between neurons determine the repertoire of spatial patterns displayed in the spontaneous activity. To test our theoretical result, we have also used the model to simulate spontaneous activity of a neural network, whose architecture is inspired by the patchy organization of horizontal connections between cortical columns in the neocortex of primates and other mammals. The dominant spatial patterns of the spontaneous activity, calculated as its principal components, coincide remarkably well with those patterns predicted from the network connectivity using our theory. The equivalence between the concept of dominant pattern and the concept of attractor of the network dynamics is also demonstrated. This in turn suggests new ways of investigating encoding and storage capabilities of neural networks.

Subfields: Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/e17490eb1883418841dddaa6911e5e5da0d9a4ad

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In Cognitive Neuroscience

In Statistical and Nonlinear Physics

Citations in the corpus

Controllability of structural brain networks (2014, unknown): “Since A is stable, a first order approximation yields (I−A2)−1 ≈ I+A(2), (6) and the j-th diagonal element of (I − A2)−1 is 1 + ∑N i=1A 2 ij .”
Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms (2017, background): “…et al., 2016b) Mesoscopic Models Linear Stochastic Model Noise representing firing rate deviations Fixed Point Fixed Point Noise deviations around equilibrium Yes No No (Fernandez Galan, 2008, Cabral et al., 2012, Goni et al., 2014, Hansen et al., 2015) Spatial Autoregression Structured Noise - - -”
Functional connectivity dynamics: Modeling the switching behavior of the resting state (2015, methodology): “In this linear stochastic model (LSM) (Galan, 2008; Goñi et al., 2014; Messé et al., 2014), the ﬂ uctuations of local activations and their correlations among different sites are shaped only by the SC, and remain unchanged with respect to Deco and Jirsa (2012)andDecoetal.”
Communication dynamics in complex brain networks (2017, methodology): “This principle has been implemented in various random-walk models 52,122,123 , threshold models 22,124–127 , epidemic-spreading models 128–130 and avalanche models 131 .”
Predicting human resting-state functional connectivity from structural connectivity (2009, methodology): “See reference [5] for further details.”
Complex brain networks: graph theoretical analysis of structural and functional systems (2009, background): “The covariance structure of the endogenous activity generated by these neural units yields a functional network that can be generated by computer simulations or in some cases analytically 108 .”