Emergence of spatiotemporal sequences in spiking neuronal networks (Spreizer et al 2019)

"Spatio-temporal sequences of neuronal activity are observed in many brain regions in a variety of tasks and are thought to form the basis of meaningful behavior. However, mechanisms by which a neuronal network can generate spatio-temporal activity sequences have remained obscure. Existing models are biologically untenable because they either require manual embedding of a feedforward network within a random network or supervised learning to train the connectivity of a network to generate sequences. Here, we propose a biologically plausible, generative rule to create spatio-temporal activity sequences in a network of spiking neurons with distance-dependent connectivity. We show that the emergence of spatio- temporal activity sequences requires: (1) individual neurons preferentially project a small fraction of their axons in a specific direction, and (2) the preferential projection direction of neighboring neurons is similar. Thus, an anisotropic but correlated connectivity of neuron groups suffices to generate spatio-temporal activity sequences in an otherwise random neuronal network model."

Model Type: Realistic Network

Region(s) or Organism(s): Neocortex; Striatum

Cell Type(s): Abstract integrate-and-fire leaky neuron

Model Concept(s): Activity Patterns; Neuromodulation; Spatio-temporal Activity Patterns; Spatial connectivity

Simulation Environment: NEST

Implementer(s): Spreizer, Sebastian


Spreizer S, Aertsen A, Kumar A. (2019). From space to time: Spatial inhomogeneities lead to the emergence of spatiotemporal sequences in spiking neuronal networks. PLoS computational biology. 15 [PubMed]

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