The large range of timescales empirically observed in neural circuits can be naturally explained when neural assemblies of heterogeneous size are recurrently coupled, empowering the neural circuits to efficiently process complex time-varying input signals.
An information theoretic and systems biology approach shows that cellular compartmentalisation and receptor promiscuity can facilitate the accurate and robust inference of position from noisy morphogen profiles, verified by experiments in Drosophila wing imaginal disc.
Despite ongoing rewiring and continuous turnover of synapses, a computational model shows that memories can maintain and even strengthen their connectivity by self-reactivating during periods without sensory input.
Keeping flexible adaptable representations of speech categories at different time scales allows the brain to maintain stable perception in the face of varying speech sound characteristics.
Biologically plausible changes in the excitabilities of single neurons may suffice to selectively modulate sequential network dynamics, without modifying of recurrent connectivity.
The neural representation of position in the medial entorhinal cortex may be stabilized by synaptic connectivity across modules, which enforces coherent updates in their states.
Persistent, non-random sensorimotor connectivity reveals the capacity of intrinsic spinal networks to purposefully replay and modify learned patterns of neural transmission during unconsciousness.
Naturalistic animal behavior exhibits a complex organization in the temporal domain, whose variability stems from hierarchical, contextual, and stochastic sources and can be naturally explained in terms of metastable attractor models.
