A Distributed Modular Logic Programming Model Based on the Cortex

We developed a distributed modular architecture based on distribution of processing and storage according to data type, inspired by an analysis of the primate cortex. Data items and rules have sets of weights, and these are updated, attenuated and combined in various ways to allow data storage management and rule competition. There are many motivations for using weights, including modeling strengths of different primate behaviors, storage management so that data items are stored and removed from store as needed, rule selection by competition based on computed weight, rule selection stability by confirmation feedback among modules, time smearing to prevent propagation delay problems within the distributed architecture, and the desire to eventually find corresponding neural models. 1 Motivation from neuroscience We used a modeling approach of a modular distributed computational architecture and an abstract logical description of data and control [1] [2] [5], for which we have also analyzed the correspondence to the primate neocortex [6]. We analyzed the neuroanatomy of the cortex, which is divided into neural areas with genetically determined connections among areas [6]. We reviewed experiments which indicated the functions that each neural area appeared to be involved in, and we construed these results as each neural area’s action being to construct data items of data types characteristic of that area. We also defined neural regions made up of small numbers of areas, as there are about 50 cortical areas whose functions are sometimes not clearly differentiated. The end result of our analysis is summarized in Figure 1. In order to design a model of the cortex, we abstracted from our review some biological information-processing principles: 1. Each neural area stores and processes data items of given types characteristic of that neural area; data items are of bounded size. 2. To form systems, neural areas are connected in a fixed network with dedicated point-to-point channels. 3. The set of neural areas is organized as a perception-action hierarchy.