Hierarchical anti-Hebbian network model for the formation of spatial cells in 3D (Soman et al 2019)

This model shows how spatial representations in 3D space could emerge using unsupervised neural networks. Model is a hierarchical one which means that it has multiple layers, where each layer has got a specific function to achieve. This architecture is more of a generalised one i.e. it gives rise to different kinds of spatial representations after training.

Model Type: Connectionist Network

Region(s) or Organism(s): Hippocampus; Entorhinal cortex

Cell Type(s): Abstract rate-based neuron

Model Concept(s): Spatial Navigation; Learning; Unsupervised Learning

Simulation Environment: MATLAB

Implementer(s): Soman, Karthik [karthi.soman at gmail.com]

References:

Soman K, Chakravarthy S, Yartsev MM. (2018). A hierarchical anti-Hebbian network model for the formation of spatial cells in three-dimensional space. Nature communications. 9 [PubMed]