Modeling neuronal networks for metric representation of space and spatial navigation
Brain system for metric representation of space and spatial navigation include neural networks of medial temporal lobe. Patterns of activity, connectivity and development of these networks and biophysical properties of their cellular units have been extensively investigated by neuroscientists. Interpretation of the increasing amount of obtained data as well as theoretical understanding of how the neural networks for spatial navigation are functioning poses important challenges for computational neuroscience.
Among other types of neurons with spatially modulated patterns of activity grid cells (GC) are especially interesting. They were in the focus of scientist’s attention because the hexagonal structure of their firing fields is not imported from the outside environment, but generated within the brain. Understanding the origin and properties of GCs networks is an attractive challenge for anybody wanting to know how brain circuits compute.
In this course we will review the main types of spatially selective activity patterns observed in hippocampal and entorhinal cortex neuronal networks and discuss computational models developed to reproduce main features of these patterns. We will focus on continuous attractor network models of GC network and their self-organization resulting from synaptic plasticity.
Project prerequisites: Basics of llinear algebra, basics of ordinary differential equations, MATLAB.
Associated topics: Computational Neuroscience.
Dr. Andrey Stepanyuk