Attractor-based models for sequences and pattern generation in neural circuits

Neural circuits in the brain perform a variety of essential functions, including input classification, 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. Traditionally, rhythmic activity and pattern generation have been modeled using coupled oscillators, whereas input classification and pattern completion have been modeled using attractor neural networks. Here, we present a theoretical model that demonstrates how attractor-based networks can also generate diverse rhythmic patterns, such as those of central pattern generators (CPGs). Additionally, we propose a mechanism for transitioning between patterns. Specifically, we construct a network that can step through a sequence of five different quadruped gaits. It is composed of two dynamically different networks: a “counter” network that can count the number of external inputs it receives, encoded 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 sequence of fixed points in the counter network with the dynamic attractors of the locomotion network. To accomplish this, we introduce a new architecture for layering networks that produces “fusion” attractors, minimizing interference between the attractors of individual layers. All of this is accomplished within a unified framework of attractor-based models using threshold-linear networks.