Cooperation in Neural Systems: Bridging Complexity and Periodicity

Abstract

Inverse power law distributions are generally interpreted as a manifestation of complexity, and waiting time distributions with power index μ< 2 reflect the occurrence of ergodicity breaking renewal events. In this Letter we show how to combine these properties with the apparently foreign clocklike nature of biological processes. We use a two-dimensional regular network of leaky integrate-and-fire neurons, each of which is linked to its four nearest neighbors, to show that both complexity and periodicity are generated by locality breakdown: links of increasing strength have the effect of turning local into long-range interaction, thereby generating first time complexity and then time periodicity. Increasing the density of neuron firings reduces the influence of periodicity thus creating a cooperation-induced distinctly non-Poisson renewal condition.

The paper is under review and might be under copyright of a journal

References in corpus (9)

• Emergent complex neural dynamics
• Dynamical synapses causing self-organized criticality in neural networks
• Random Time-Scale Invariant Diffusion and Transport Coefficients
• Self-Organized Criticality model for Brain Plasticity
• Circadian pattern and burstiness in mobile phone communication
• Pesin-Type Identity for Weak Chaos
• Collective behavior of heterogeneous neural networks
• Robust Statistical Tests of Dragon-Kings beyond Power Law Distributions
• Infinite Invariant Density Determines Statistics of Time Averages for Weak Chaos

Cited by in corpus (3)

• Spike-Phase Coupling As an Order Parameter in a Leaky Integrate-and-Fire Model
• Multi-chimera states in the Leaky Integrate-and-Fire model
• Critical behaviour at the onset of synchronization in a neuronal model