Undersökning av Synaptiska Viktfördelningars Roll i att Överskrida Nätverksstrukturers Inverkan på Neural Dynamik

Biological neural networks contain significant amounts of neurons. These networks form predictable structures and patterns regarding both connectivity (structure) and the strength of connections (synaptic weight). However, there is a gap in knowledge about how these aspects together shape network activity. This study investigates whether distributions of synaptic weights can affect or override the dynamics produced by commonly found subgraphs, called motifs, in network structure. The results in this study are drawn from computational simulations of spiking neural networks, parameterized by synaptic weight distributions and network structures. This paper focuses on varying shapes of Gaussian, uniform and log-normal distributions, and their effects on convergent, divergent, and chain motifs. The study found that networks with convergent motifs had amplified activity, while the other structures remained more rigid when changing synaptic weight distributions. These findings contribute to a better understanding of the importance of synaptic weight distributions, useful for constructing biologically accurate neural networks.