Initial topology in hierarchically organized evolvable neural network determines the emergence of synfire chains

Paolo Masulli, Alessandro E.P. Villa

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Abstract

We investigate the eects of network topology on the dynamical activity of a hierarchically organized network of simulated spiking neurons. With a fixed basic two-by-two grid structure of processing modules each composed by almost 6000 leaky integrate-and-fire neurons and dierent connectivity schemes inbetween these modules, we study how the activation and the biologically-inspired processes of plasticity on the network shape its topology using invariants based on algebro-topological constructions. By definition, a clique is a fully-connected directed subnetwork, that means there is one source and one sink in the subnetwork. We define ‘k-clique hub cells’ for a positive integer k any cell which is sink and source cell of at least k 3-cliques. We show that there is a statistically dierent distribution of in- and out-degrees between clique hubs and other cells. Furthermore, we show that by identifying ‘clique hub cells’ we can find synfire chains that are involved in spatio-temporal firing patterns. Hence, the results suggest a link exists between an initial topological structure characterized by sub-networks cliques and a functional connectivity emerging at a later stage as the outcome of synaptic plasticity mechanisms
Original languageEnglish
Title of host publicationProceedings of 7th International Congress on Cognitive Neurodynamics
Number of pages1
Publication date2019
Pages23-23
Publication statusPublished - 2019
Event7th International Conference on Cognitive Neurodynamics 2019 - Alghero, Italy
Duration: 29 Sep 20192 Oct 2019

Conference

Conference7th International Conference on Cognitive Neurodynamics 2019
CountryItaly
CityAlghero
Period29/09/201902/10/2019

Cite this

Masulli, P., & Villa, A. E. P. (2019). Initial topology in hierarchically organized evolvable neural network determines the emergence of synfire chains. In Proceedings of 7th International Congress on Cognitive Neurodynamics (pp. 23-23)