Solving the binding problem of the brain with bi-directional functional connectivity

We propose a neural network model which gives one solution to the binding problem on the basis of 'functional connectivity' and bidirectional connections. Here, 'functional connectivity' is dynamic neuronal connectivity peculiar to temporal spike coding neural networks with coincidence detector neurons. The model consists of a single primary map and two higher modules which extract two different features shown on the primary map. There exist three layers in each higher module and the layers are connected bi-directionally. An object in the outer world is represented by a 'global dynamical cell assembly' which is organized across the primary map and the two higher modules. Detailed, but spatially localized, information is coded in the primary map, whereas coarse, but spatially extracted information or globally integrated information is coded in the higher modules. Computer simulations of the proposed model show that multiple cell assemblies sharing the same neurons partially can co-exist. Furthermore, we introduce a three-dimensional J-PSTH (Joint-Peri Stimulus Time Histogram) which is capable of tracking such cell assemblies, altering its constituent neurons as in our proposed model.