A Computationally Improved Model of Brain Activity in Visual Attentional State

In this paper, a computationally improved model of brain activity is developed in visual attention state.
This model, based on Hodgkin–Huxley neurons, is a two-layer architecture of excitatory and inhibitory connections in a network of spiking neurons, with star-like connections between the central units and peripheral neurons. This model is composed of three modules and the focused attention is represented by those peripheral neurons that generate spikes synchronously with the central neuron while the activities of other peripheral neurons are suppressed. We propose rising and falling dynamic connections based on a Hebbian-rule of the spiking neurons, while also considering noise and random effects.
To validate the model, we consider five types of dynamics by setting different parameters: asynchronous state, global synchronization, partial synchronization, transitional state, and quiescence state. Simulation results show that the improved model can sequentially select objects with different frequencies and has a reliable shift of attention from one object to another.
Results are consistent with the previous experimental works while exhibiting higher robustness.