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Poster Session C: Friday, August 15, 2:00 – 5:00 pm, de Brug & E‑Hall

Small-Worlds Memory Generation and Capacity in the Neuroidal Model

Ethan Irick Wolfe¹; ¹#NAME?

Presenter: Mugizi Robert Rwebangira

Understanding higher level cognitive processes is a central problem in neuroscience. The Neuroidal model provides a useful framework for posing these problems in a computer science context. There has been significant recent work trying to understand memory capacity in the Neuroidal model but this work was done assuming that the network of neurons was an Erdős-Rényi (ER) random graph. However, the network of neurons in the brain has been shown to exhibit small-world properties, which are not present in ER graphs. In this research we explore replacing ER graphs with Watts-Strogatz and Barabási-Albert small-worlds graphs in order to more accurately model the biological reality. We aim to investigate the implications for memory capacity and interference within the Neuroidal model. We show that the algorithm JOIN can function with small-worlds graph structures and allow the Neuroidal model to reach capacity.

Topic Area: Brain Networks & Neural Dynamics

Extended Abstract: Full Text PDF