Partial synchronization of neural activity and information processing

We study dynamics of neural activity in brain-inspired neural networks which comprise both low and high layers of information processing. Information propagates from the low layer which includes “peripheral neurons” (PNs), and the dynamics is controlled by the feedback from the higher layer of “central neurons” (CNs). We use the Hodgkin-Huxley type model to describe spike generation properties of neural elements. Synaptic connections are of excitatory and inhibitory type and some of them have fixed connection strengths and some are adjustable according to Hebbian type learning rule. The regime of partial synchronization between spiking activity of the CNs and PNs has been found. It is shown that PNs with higher firing rates are selected preferentially by the central neurons. In the case of local connections between PNs, we have found that local excitatory connections facilitate synchronization; while local inhibitory connections help distinguishing two groups of PNs with similar intrinsic frequencies. We hypothesize that the regime of partial synchronization can be used to simulate neural mechanisms of perception and attention. In particular, sequential selection of stimuli simultaneously present in the visual scene is demonstrated by the model which deals with a real image in the frequency domain.