Sequences and Modularity of Dynamic Attractors in Inhibition-Dominated Neural Networks

Threshold-linear networks (TLNs) display a wide variety of nonlinear dynamics including multistability, limit cycles, quasiperiodic attractors, and chaos. Over the past few years, we have developed a detailed mathematical theory relating stable and unstable fixed points of TLNs to graph-theoretic properties of the underlying network. These results enable us to design networks that count stimulus pulses, track position, and encode multiple locomotive gaits in a single central pattern generator circuit.

Learning Objectives: 

1. What types of attractors that can be encoded in inhibition-dominated neural networks? Can multiple dynamic attractors coexist in the same network?

2. What neural network functions or computations can be performed by threshold-linear networks? Give two examples.