The CTLN models: from neuroscience to economics.
There is a family of systems of differential equations called Combinatorial Threshold Linear Networks (CTLN's).
These systems of differential equations have some characteristics that have caught my attention since I learned about
them. They are inspired by biological neural networks. Each dependent variable $x_i(t)$ represents the activity of a
neuron i at time t. Although all neurons are assumed to be connected to each other, it is also assumed that their
interactions may be different, and this is reflected in a directed graph. Essentially, the parameters of the system of
equations are determined by the directed graph, and it is interesting to predict the dynamics from the graph.
Attractors (fixed points in the system) are obtained as well as limit cycles. In this talk I will introduce these systems
of equations and present what may be the first steps in qualitative applications to economics.
