Non-canonical attractor dynamics underlie perceptual decision-making.

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Abstract

Perceptual decision-making is the process by which an animal uses sensory stimuli to choose an action or mental proposition. This process is thought to be mediated by neurons organized as attractor networks 1,2 . However, whether attractor dynamics underlie decision behavior and the complex neuronal responses remains unclear. Here we use an unsupervised, deep learning-based method to discover decision-related dynamics from the simultaneous activity of neurons in frontal cortex and striatum of rats while they accumulate pulsatile auditory evidence. We show that contrary to prevailing hypotheses, perceptual choices emerge from the dynamics driven by sensory inputs that are not aligned to discrete attractors in the input-independent dynamics. Input-driven and -independent dynamics differ in strength across the decision state space, resulting in the input-driven dynamics playing a dominant role in evidence integration, while input-independent dynamics playing a principal role in decision commitment. An extension of the classic drift-diffusion hypothesis 3 to approximate the non-canonical attractor dynamics precisely predicts the internal decision commitment time and captures diverse and complex single-neuron temporal profiles, such as ramping and stepping 4-6 . It also captures choice behavior and trial-averaged curved trajectories 7-9 and reveals distinctions between brain regions. Thus, non-canonical attractor dynamics inferred from unsupervised discovery conceptually extend a classic hypothesis and parsimoniously account for multiple neural and behavioral phenomena.