A coupling network for brain computing: E–I balanced embedding in dual-attractor dynamics systems

As an important neural network model, the continuous attractor neural network (CANN) demonstrates unique advantages in simulating and explaining the representation and storage mechanisms of continuous variables (such as position, direction, etc.) in the brain. However, in the real brain network, the connections between neurons are not only random or equal, but regulated by various factors. The coding methods and interactions of multiple neuron groups form the collective behavior of the complex brain network. To study the role of complex networks in the brain, this research proposes a selective coupling network model based on CANN, which operates under the dynamic balance between excitatory and inhibitory inputs. In this study, we have investigated the interaction between two classes of CANNs with distinct selective preferences for motion direction recognition under fast excitation and inhibition (E-I) balance. Based on simulation results and theoretical analysis, we found that the fast E-I balance located in the brain can provide indirect linking effects among multiple different networks. The indirect mutual inhibition of the coupled network through E-I balance can promote the accuracy and stability of the network response to a given position. This suggests that a dynamic system of collaborative work activity is possible between different neural networks. We hope that this work could provide insightful references for the development of multi-network coupling in brain-like computing.