A decoding-based, state-space reconstruction reveals that neurons in macaque IT cortex change the structure of their collective attractor dynamics depending on task contexts.
A fundamental lower-bound on memory recall precision, which declines with storage duration and number of stored items, is derived, and human performance is shown to be well-fit by this theoretical bound.
Mathematical modeling suggests that grid cells in the rodent brain use fundamental principles of number theory to maximize the efficiency of spatial mapping, enabling animals to accurately encode their location with as few neurons as possible.
A longer birdsong-learning window evolves in response to sexual selection for song complexity and is associated with faster evolution of song performance characteristics.
Model-based analyses of human behaviour and neural activity show that representations of concurrent task-sets emerge by merging together representations of individual stimulus-response associations that occur in temporal proximity.
A novel experimental method and analysis is conceived, based on perturbation and sparse recording, to elucidate the circuit structure and mechanisms underlying the responses of grid cells.
A large variety of spatial representations implied in rodent navigation could arise robustly and rapidly from inputs with a weak spatial structure, by an interaction of excitatory and inhibitory synaptic plasticity.
