Juliana Londono-Alvarez Publishes a Paper on Attractor-Based Models

Juliana Londono-Alvarez’a paper on stable attractors (also called limit cycles) using recurrent networks opens up possibilities for the field of neural networks.

Here is an excerpt of “Attractor-Based Models for Sequences and Pattern Generation in Neural Circuits” (Juliana Londono-Alvarez, Katherine Morrison & Carina Curto)

Neural circuits in the brain perform a variety of essential functions, including input clas- sification, pattern completion, and the generation of rhythms and oscillations that support functions such as breathing and locomotion. There is also substantial evidence that the brain encodes memories and processes information via sequences of neural activity. Tra- ditionally, rhythmic activity and pattern generation have been modeled using coupled os- cillators, whereas input classification and pattern completion have been modeled using at- tractor neural networks. Here, we present a theoretical framework that demonstrates how attractor-based networks can also generate diverse rhythmic patterns, such as those of central pattern generator circuits (CPGs). Additionally, we propose a mechanism for tran- sitioning between patterns. Specifically, we construct a network that can step through a sequence of five different quadruped gaits. It is composed of two dynamically distinct mod- ules: a “counter” network, that can count the number of external inputs it receives via a sequence of fixed points; and a locomotion network, that encodes five different quadruped gaits as limit cycles. A sequence of locomotive gaits is obtained by connecting the counter network with the locomotion network. Specifically, we introduce a new architecture for lay- ering networks that produces “fusion” attractors, binding pairs of attractors from individual layers. All of this is accomplished within a unified framework of attractor-based models using threshold-linear networks.

• The paper figures out primitives and assembly strategies to easily build desired patterns.

This work will soon appear in the prestigious Neural Computation!  Excitingly, it seems likely that a plethora of actual brain circuits share the general connectivity properties (inhibitory networks) that are key to Juliana’s work.  It’s one of those things that makes sense at a number of different levels.

So, congratulations to Juliana on her first big paper from your thesis! Congratulations to Carina Curto as well on being mentor to Juliana’s keen intellect.