Towards a neurally-inspired computer architecture

Abstract

The first two main rounds of neural computing focused on adaptation and self-organization in neural networks, and on use of analog VLSI for compartmental modeling of the neuron, respectively. This paper is a prospectus for a third round of neural computing: analyzing the architecture of the primate brain to extract neural information processing principles and translate them into biologically-inspired operating systems and computer architectures. The way in which the cerebellum interacts with other brain regions in learning how to better control and coordinate movements provides a case study to introduce key ideas for these three rounds of neural computation. It is argued that the third round will develop and exploit general insights into brain architectures, their function and dynamics, that will provide a principled combination of cooperative computation, learning and perceptual robotics (i.e., the integration of action, perception and computation). This new effort is motivated by recent advances in computational neuroscience research, studying examples of system evolution ranging from low-level vision to how the interactions between frontal and parietal cortices serve action recognition (the mirror system), and language. The paper also notes the vast difference between the slow biological evolution of diverse brains and the needs of computer technology for explicit tools for the design and testing of novel programs and architectures, and suggests the challenges of developing a reflection methodology for wrapping modules with descriptions that can be automatically updated as the module itself adapts through learning. The Appendix provides a road map for approaching the voluminous literature on brain theory and neural networks.