A system model of the primate neocortex

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1 Neurocomputing 26}27 (1999) 617}623 A system model of the primate neocortex Alan H. Bond California Institute of Technology, Mailstop , 1201, California Boulevard, Pasadena, CA 91125, USA Accepted 18 December 1998 Abstract Experimental evidence has shown that the primate neocortex consists in the main of a set of cortical regions which form a perception hierarchy, an action hierarchy and connections between them. We develop a computational architecture for the brain in which each cortical region is represented by a computational module with processing and storage abilities. Modules are interconnected according to the connectivity of the corresponding cortical regions. We explain our computational principles for designing such a hierarchical and parallel computing system, and report on results obtained with an implementation of this causal functioning model of the brain Elsevier Science B.V. All rights reserved. Keywords: Primate; Neocortex; Modular architecture; Perception-action hierarchy 1. Primate brain and behavior This article describes a new class of information-processing models for computational neuroscience. They are derived from primate brain architecture, the processing in brain regions, the interactions among brain regions, and the social behavior of primates. By examining neuroanatomical evidence for connections among neural areas, we were able to establish anatomical regions and connections. We then examined evidence for speci"c functional involvements of the di!erent neural areas and found some support for hierarchical functioning, not only for the perception hierarchies but also for the planning and action hierarchy in the frontal lobes. We concluded that the overall structure, both anatomical and functional, of the primate brain is that of a set of cortical regions with spatial localization and clustered interconnectivity. Each region is specialized in data processing and data storage to a limited number of data types. Regions process data in about 20 ms, and communication channels comprise about 1 million "bers each carrying a "ring rate, perhaps /99/$ } see front matter 1999 Elsevier Science B.V. All rights reserved. PII: S ( 9 8 )

2 618 A.H. Bond / Neurocomputing 26}27 (1999) 617}623 usable as 8 bits of information. Cortical regions are connected as a perception hierarchy, a planning and action hierarchy, and connections between corresponding levels of these two hierarchies. From a study of primate behavior, we concluded that social behavior is fundamental, and that joint plans are co-constructed by primates interacting socially. 2. Our computational architecture To obtain a computational architecture for the primate brain, we take the functional hierarchy composed of neural regions, and interpret each region as a computational module. Each module will have processing and storage abilities. We take connections between neural regions to be communication channels that data may be transmitted along. Fig. 1 diagrams our brain model. The top of the cortical hierarchy is towards the front (left in the diagram), with perception below and action above. The brain model functions as a parallel computer, whose processors are intelligent memory modules, which represent neural regions and operate concurrently. The model processes streams of logical expressions, called descriptions, which are input from its sensors and transformed via intelligent memory modules to produce changes in these memories and a continuous stream of output actions. From continously generated goals, a plan is continuously selected and elaborated, receiving input from the perception hierarchy to allow it to elaborate appropriately in realtime. The perception hierarchy receives tuning information and direct requests, attention information, and prediction information from the action hierarchy enabling it to use its processing and communication resources optimally to support the realtime action. The basic action of the brain model is to try to establish a plan consistent with the response of the environment and with its own motivations. It does this by trying di!erent alternatives at each level on a competitive basis, and subject to con"rmation of successful elaboration. 3. Computational approach The representation of system modules by neural nets, and the representation of module interaction by large parallel sets of axons carrying neural signals, seemed to us to be impractical for systems with 10 or more modules. The action of a large set of parallel axons transmitting a "ring pattern of neural spikes will be abstracted to the transmission of a stream of descriptions which represent the information being communicated between the modules. For example, a set of 10,000 axons carrying a population coding of an angle giving the orientation of a perceived primate might be abstracted as an expression of the form: body}angle(primate,angle), for instance, body}angle(adam,35),

3 A.H. Bond / Neurocomputing 26}27 (1999) 617} Fig. 1. Modules from neural areas of the primate neocortex, and our initial system model.

4 620 A.H. Bond / Neurocomputing 26}27 (1999) 617}623 or a form carrying breadth and height of a population code might be: body}angle(primate,angle,height,width), for instance, body}angle(adam,35,10,3). We can capture the idea that a given module only processes certain kinds of data by limiting it to storing and processing only descriptions with a given limited set of predicate names. Thus an early visual module might store descriptions of the form: retinal}position(feature,rx,ry), an auditory module: sound(frequency,intensity), a motor module: action(motor}action,x,y,z), a planning module: plan}action(action,agent), and a goal module: goal(goal). The processing within a module may actually be a sophisticated learned and learning neural net, but we will abstract and approximate information processing by a set of processing rules. The complexity of rule bodies is intended to be that achievable by a simple neural net, which we imagine to allow arithmetic operations, simple geometric functions and inequality tests. The big advantage of a logical approach is that it allows arbitrary information structures and arbitrary information processing to be represented. It includes real variables and functions of real variables, but it also allows us to represent discrete structures such as plans as sequences of actions, and the conditional temporal sequential processing of plans. In addition, logical expressions are linguistic forms, which can be made intuitively expressive by an appropriate choice of names. This allows us to bring a precisely speci"ed model close to the language that practising primate neurobiologists might use in discussing information and its storage, processing and transmission in the brain. Each stored description has a weight which is a real number in the range [0.0,1.0] representing its strength at the current time. Rules use description weights to compute the weights of the descriptions they create, using a weighted linear sum. Typically, only the results of the strongest rule activation are transmitted to other modules. By the use of logical representations, we avoid all use of random access addresses; instead, basing all computation in the model only on associative matching and competition. The system model uses a discrete time, with one unit of time corresponding to 20 ms. In each time unit, all matching instances of all rules in all modules "re and then all transmissions of information among modules occur. This is intended as an abstract representation of all the activity that occurs in the brain during that period of one time unit. 4. Joint action We generalized the standard arti"cial intelligence notion of plan to one suitable for action by more than one collaborating agent. A plan will be data which represents a sequence of joint actions, and which causes the system to attempt to carry out such

5 A.H. Bond / Neurocomputing 26}27 (1999) 617} Fig. 2. Instantaneous behavioral states of two interacting primates.

6 622 A.H. Bond / Neurocomputing 26}27 (1999) 617}623 Fig. 3. Grooming scenario visualization generated by our model. actions. Each step gives an action for each agent collaborating in the joint action, and each such action may be conditional upon certain tests on the situation being true. The way a plan is executed is to attempt each step in turn, and for each step to verify that each agent is performing their corresponding action and that any tests are satis"ed, and for the subject agent to attempt to execute its individual action. Fig. 2 shows how our perception}action computational architecture models the functioning of the brain in social behavior. 5. Implementation and results We de"ned a particular example of such a model for primates, based on rhesus macaques, which inhabit a naturalistic three-dimensional environment. We used Prolog for the brain model, including our own rule interpreter, and OpenGL for visualization as animated movies. The classes of behaviors we have demonstrated include (i) two primate grooming behavior and joint action, (ii) social con#ict involving four primates, change and termination of action, (iii) the direct perception of disposition, and (iv) social spacing. Fig. 3 shows an image produced by our system during a grooming scenario. This implementation has demonstrated the viability of our system modeling concepts for the theoretical de"nition and practical construction of a system model of the primate neocortex. Acknowledgements This work has been partially supported by the National Science Foundation, Information Technology and Organizations Program managed by Dr. Les Gasser.

7 A.H. Bond / Neurocomputing 26}27 (1999) 617} The author would like to thank Professor Pietro Perona for his generous support and encouragement, and Professor Steven Mayo for providing invaluable computer resources, at Caltech, where this research is currently being pursued. Alan H. Bond was born in England and received a Ph.D. degree in theoretical physics in 1966 from Imperial College of Science and Technology, University of London. From 1966 to 1969, he was on the faculty of the Computer Science Department at Carnegie-Mellon University, Pittsburgh. During the period 1969}1984, he was on the faculty of the Computer Science Department at Queen Mary College, London University, where he founded and directed the Arti"cial Intelligence and Robotics Laboratory. From 1985 to 1992, he led research in applied arti"cial intelligence at the University of California, Los Angeles. He has published research papers in autonomous robotics, multiagent systems and parallel computer architectures. Since 1996, he has been a Senior Scientist at California Institute of Technology. His main research interest concerns the system modeling of the primate brain.

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