The contraction bias has been extensively studied in psychophysics, but only recently has its origin begun to be investigated with neural activity recordings. Leveraging the fact that it is currently possible to train recurrent networks of spiking neurons to perform cognitive tasks, we investigated the causes of the contraction bias and how behavior relates to population firing activity in networks trained to discriminate temporal intervals. A novel normative model containing potential sources of the bias guided us in investigating how it emerged in the networks. Various geometric features of state-space trajectories encoded compressed representations of the durations of the first interval, which were modulated by sensory history. Furthermore, these compressions conveyed a Bayesian estimate of the durations, thus relating activity and behavior. We conjecture that this occurs in areas of the brain where information about the current and preceding stimuli converges, and that it holds generally in delayed comparison tasks.

Synaptic plasticity allows cortical circuits to learn new tasks and to adapt to changing environments. How do cortical circuits use plasticity to acquire functions such as decision-making or working memory? Neurons are connected in complex ways, forming recurrent neural networks, and learning modifies the strength of their connections. Moreover, neurons communicate emitting brief discrete electric signals. Here we describe how to train recurrent neural networks in tasks like those used to train animals in neuroscience laboratories and how computations emerge in the trained networks. Surprisingly, artificial networks and real brains can use similar computational strategies.