Competition and cooperation of assembly sequences in recurrent neural networks.

ABSTRACT: Neural activity sequences are ubiquitous in the brain and play pivotal roles in functions such as long-term memory formation and motor control. While conditions for storing and reactivating individual sequences have been thoroughly characterized, it remains unclear how multiple sequences may interact when activated simultaneously in recurrent neural networks. This question is especially relevant for weak sequences, comprised of fewer neurons, competing against strong sequences. Using a non-linear rate -based and a spiking model with discrete, pre-configured assemblies, we demonstrate that weak sequences can compensate for their competitive disadvantage either by increasing excitatory connections between subsequent assemblies or by cooperating with other co-active sequences. Further, our mo

SUBMITTER: Stober TM 

PROVIDER: S-EPMC12469390 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature