A model of computation and representation in the brain

作者：Albus James S^*

来源：Information Sciences, 2010, 180(9): 1519-1554.

DOI：10.1016/j.ins.2009.12.031

• Brain modeling
• Cognitive modeling
• Human neocortex
• Image processing
• Knowledge representation
• Perception
• Reverse engineering the brain
• Segmentation
• Signals to symbols

The brain is first and foremost a control system that is capable of building an internal representation of the external world, and using this representation to make decisions, set goals and priorities, formulate plans, and control behavior with intent to achieve its goals. The internal representation is distributed throughout the brain in two forms: (1) firmware embedded in synaptic connections and axon-dendrite circuitry, and (2) dynamic state-variables encoded in the firing rates of neurons in computational loops in the spinal cord, midbrain, subcortical nuclei, and arrays of cortical columns. It assumes that clusters and arrays of neurons are capable of computing logical predicates, smooth arithmetic functions, and matrix transformations over a space defined by large input vectors and arrays. Feedback from output to input of these neural computational units enable them to function as finite-state-automata (fsa), Markov decision processes (MDP), or delay lines in processing signals and generating strings and grammars. Thus, clusters of neurons are capable of parsing and generating language, decomposing tasks, generating plans, and executing scripts. In the cortex, neurons are arranged in arrays of cortical columns that interact in tight loops with their underlying subcortical nuclei. It is hypothesized that these circuits compute sophisticated mathematical and logical functions that maintain and use complex abstract data structures. It is proposed that cortical hypercolumns together with their underlying thalamic nuclei can be modeled as a cortical computational unit (CCU) consisting of a frame-like data structure (containing attributes and pointers) plus the computational processes and mechanisms required to maintain it and use it for perception cognition, and sensory-motor behavior. In sensory processing areas of the brain, CCU processes enable focus of attention, segmentation, grouping, and classification. Pointers stored in CCU frames define relationships that link pixels and signals to objects and events in situations and episodes. CCU frame pointers also link objects and events to class prototypes and overlay them with meaning and emotional values. In behavior generating areas of the brain, CCU processes make decisions, set goals and priorities, generate plans, and control behavior. In general, CCU pointers are used to define rules, grammars, procedures, plans, and behaviors. CCU pointers also define abstract data structures analogous to lists, frames, objects, classes, rules, plans, and semantic nets. It is suggested that it may be possible to reverse engineer the human brain at the CCU level of fidelity using next-generation massively parallel computer hardware and software. Published by Elsevier Inc.

• 出版日期2010-5-1