Diverse and flexible behavioral strategies arise in recurrent neural networks trained on multisensory decision making.

ABSTRACT: Behavioral variability across individuals leads to substantial performance differences during cognitive tasks, although its neuronal origin and mechanisms remain elusive. Here we use recurrent neural networks trained on a multisensory decision-making task to investigate inter-subject behavioral variability. By uniquely characterizing each network with a random synaptic-weights initialization, we observed a large variability in the level of accuracy, bias and decision speed across these networks, mimicking experimental observations in mice. Performance was generally improved when networks integrated multiple sensory modalities. Additionally, individual neurons developed modality-, choice- or mixed-selectivity, these preferences were different for excitatory and inhibitory neurons, and the c

SUBMITTER: Wierda TS 

PROVIDER: S-EPMC12520346 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature