Formation of brain-wide neural geometry during visual item recognition in monkeys
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Neural dynamics reflect canonical computations that relay and transform information in the brain. Previous studies have identified the neural population dynamics in many individual brain regions as a trajectory geometry in a low-dimensional neural space. However, whether these populations share particular geometric patterns across brain-wide neural populations remains unclear. Here, by mapping neural dynamics widely across temporal/frontal/limbic regions in the cortical and subcortical structures of monkeys, we show that 10 neural populations, including 2,500 neurons, propagate visual item information in a stochastic manner. We found that the visual inputs predominantly evoked rotational dynamics in the higher-order visual area, the TE and its downstream striatum tail, while curvy/straight dynamics appeared more frequently downstream in the orbitofrontal/hippocampal network. These geometric changes were not deterministic but rather stochastic according to their respective emergence rates. These results indicated that visual information propagates as a heterogeneous mixture of stochastic neural population signals in the brain.
