Linking Rate-Based And Spiking Models: A Quest Towards Biologically Relevant Neural Systems

Authors: Aiswarya P S, Indian Institute of Science Education and Research, Thiruvananthapuram, India

Presentation type: Poster at SNUFA 2023 online workshop (7-8 Nov 2023)

Abstract

Present-day computational advancements allow us to model large networks with considerable detail in individual neurons. As much as rate-based networks are convenient and easily implemented, they lack biological relevance. Therefore, we aim to find the parameters and conditions in which a spiking network can behave like a rate-based network and thus favorably replace them.

We have tried to make a comprehensive and general attempt to see if this is possible. To this end, we examined and compared two well-known networks the Brunel Network and the Continuous Attractor Network.

The Brunel network, a spiking neuronal network, and the continuous attractor network, a rate-based neuronal network are carefully chosen as they have been reported multiple times and show a variety of essential behaviors. The Brunel network is a well-studied network of Leaky Inte grated and Fire (LIF) neurons whose network behavior is very close to the theoretical behavior observed in real neurons. Continuous attractor networks explain one of the high-level cognitive tasks: path integration for spatial navigation in grid cells. Using these models as a template, a simplistic model was created in which both networks with the same parameters could reproduce similar population activity.