Implications of a spontaneously active ground state for computing with brain-inspired circuits

Commonly used brain-inspired models assume that neurons in the network are silent until perturbed by external stimuli. This assumption has created a family of feedforward neural models that progressively detects complex features. However, experimental observations suggest that the brain is spontaneously active where neuronal activity is driven by internal fluctuations in total synaptic input leading to asynchronous and irregular patterns of activity even in the absence of external inputs. The implications of this ground state of the brain is explored for designing brain-inspired circuits that are more driven top-down rather than bottom-up activity leading to a more radical mode of information processing.