Abstract
A rigorous understanding of brain dynamics and function requires a conceptual bridge between multiple levels of organization, including neural spiking and network-level population activity. Mounting evidence suggests that neural networks of cerebral cortex operate at a critical regime, which is defined as a transition point between two phases of short lasting and chaotic activity. However, despite the fact that criticality brings about certain functional advantages for information processing, its supporting evidence is still far from conclusive, as it has been mostly based on power law scaling of size and durations of cascades of activity. Moreover, to what degree such hypothesis could explain some fundamental features of neural activity is still largely unknown. One of the most prevalent features of cortical activity in vivo is known to be spike irregularity of spike trains, which is measured in terms of the coefficient of variation (CV) larger than one. Here, using a minimal computational model of excitatory nodes, we show that irregular spiking (CV > 1) naturally emerges in a recurrent network operating at criticality. More importantly, we show that even at the presence of other sources of spike irregularity, being at criticality maximizes the mean coefficient of variation of neurons, thereby maximizing their spike irregularity. Furthermore, we also show that such a maximized irregularity results in maximum correlation between neuronal firing rates and their corresponding spike irregularity (measured in terms of CV). On the one hand, using a model in the universality class of directed percolation, we propose new hallmarks of criticality at single-unit level, which could be applicable to any network of excitable nodes. On the other hand, given the controversy of the neural criticality hypothesis, we discuss the limitation of this approach to neural systems and to what degree they support the criticality hypothesis in real neural networks. Finally, we discuss the limitations of applying our results to real networks and to what degree they support the criticality hypothesis.
Highlights
Having fundamental principles underlying neural dynamics and function entails a unifying theory that captures various universal features of cortical activity at different levels
To explore the impact of critical dynamics on the statistics of single-neuron spiking, we used a minimal network model consisting of excitatory binary probabilistic neurons with sparse connectivity and external inputs
A wealth of evidence suggests the hypothesis that neural networks of cerebral cortex operate at criticality [2, 3, 5, 7, 9, 33]
Summary
Having fundamental principles underlying neural dynamics and function entails a unifying theory that captures various universal features of cortical activity at different levels. Criticality predicts maximum irregularity in recurrent networks of excitatory nodes other hand, collective behavior of neural systems is characterized by complex spatiotemporal patterns of activity, including scale-free activity [2,3,4,5,6,7,8,9,10] This scale invariance is manifested by the power law distributed cascades of activity, or the so-called neuronal avalanches, and is predicted to occur for a network at a critical state; a state of balanced propagation at the transition between two phases of short lasting and chaotic activity [11,12,13]. These observations at two adjacent levels of brain organization raise the question to what extent the network state controls the variability of single-neuron spiking?
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