Unknown

Dataset Information

0

From space to time: Spatial inhomogeneities lead to the emergence of spatiotemporal sequences in spiking neuronal networks.


ABSTRACT: 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.

SUBMITTER: Spreizer S 

PROVIDER: S-EPMC6834288 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

altmetric image

Publications

From space to time: Spatial inhomogeneities lead to the emergence of spatiotemporal sequences in spiking neuronal networks.

Spreizer Sebastian S   Aertsen Ad A   Kumar Arvind A  

PLoS computational biology 20191025 10


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 sequence  ...[more]

Similar Datasets

| S-EPMC4148205 | biostudies-literature
| S-EPMC6586365 | biostudies-literature
| S-EPMC2731936 | biostudies-other
| S-EPMC6737503 | biostudies-literature
| S-EPMC4461886 | biostudies-literature
| S-EPMC7250921 | biostudies-literature
| S-EPMC3093492 | biostudies-literature
| S-EPMC7972481 | biostudies-literature
| S-EPMC7505196 | biostudies-literature
| S-EPMC10692671 | biostudies-literature