Transitions from monotonic to tuned responses in recurrent neural network models during timing prediction

Abstract

The brain exhibits a gradual transition in responses to visual event duration and frequency through the visual processing hierarchy: from monotonically increasing to timing-tuned responses. Over their hierarchies, properties of both response types are progressively transformed. Here, we implement simulations based on artificial neural networks to investigate the requirements of neural systems for the emergence of such responses and their properties transformations. We see that recurrent networks develop monotonic responses whose properties progressions over network layers resemble those over brain areas. Responses to another sensory quantity, Furthermore, recurrent networks can further develop tuned responses, but only with training, a gradual transition between monotonic and tuned responses emerges. Particularly, if this training is done on predictable sequences, the tuned properties progressions resemble those observed in the brain. These results suggest that the emergence of visual timing-tuned responses and the subsequent hierarchical transformations of these responses result from recurrent neural computation and predictive processing of sensory event timing.