666 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, VOL. SMC-13, NO. 5, SEPTEMBER/OCTOBER 1983

Special Issue on Neural and Sensory Information Processing

A R T H U R C. SANDERSON, MEMBER, I E E E , AND Y E H O S H U A Y. ZEEVI, MEMBER, I E E E

Guest Editors

IN RECENT YEARS, experimental and theoretical pro­gress has rekindled interest in the study of organizational

and computational principles underlying neural systems. Neural systems are both complex and intriguing, and the relation of neural and sensory information processing to communications, control, and behavior in living organisms continues to pose fundamental research issues in a variety of disciplines. This Special Issue on Neural and Sensory Information Processing is intended to provide a timely sampling of current views and progress in these studies.

The papers in this issue have been organized into six broad categories. The intent of the organization is to assist the reader in assimulating a large body of contrasting, as well as complementary, views, and not to stereotype the nature of the work reported.

Experimental Design and Data Analysis (Section I) are fundamental to progress in neurophysiology, and recent advances in the recording of multiple neural units offer enormous potential for increased understanding of the parallel and distributed nature of neural information processing. We are fortunate to have papers (Gerstein et al., Reitboeck, and Abeles et al.) that describe multiunit recording and data analysis in three of the pioneering laboratories addressing these issues. Nakahama et al. dis­cuss serial dependency in single spike trains, though such correlation measures may also apply to multiunit data.

In Section II, Single Neurons and Local Mechanisms, Lewis presents an excellent review of single neuron activity and its relation to information processing. The diversity of single neuron activity has become a key element in under­standing complex networks, and Holden and Winlow, Henningsen and Liestol, and Goldring and Lisman ex­amine aspects of single neuron and local activity and their consequences for system behavior.

As experimental studies of multiple neural units provide more information on the nature of neural interactions, a variety of computational and organizational mechanisms of neural networks may be explored. In Section III, Neural Networks and Parallel Processing, Leibovic focuses atten­tion on parallel structures and the implications of diverging and converging pathways. Inbar also addresses this issue in

A. C. Sanderson is with the Robotics Institute, Carnegie-Mellon Uni­versity, Pittsburgh, PA 15213.

Υ. Y. Zeevi is with the Department of Electrical Engineering, Technion Institute, Technion City, Haifa, 3200, Israel.

relation to motor control. Amari discusses a field theoreti­cal formalism for distributed networks. Selverstein et al., Torre et al., Oguztoreli, Harth, and Guevara et al. address a range of topics regarding dynamic behavior of large networks. Particularly intriguing are the requirements for orderly versus chaotic dynamics, and the relation between system dynamics and information processing.

Higher level aspects of cognition and behavior are more difficult to associate with specific properties of neurons and neural networks. There have been various approaches to these issues, and examples of these approaches are included in Section IV, Cognitive and Learning Models. Anderson examines computational models underlying aspects of psychological and cognitive behavior. Cohen and Grossberg interpret the mathematical properties of competitive networks as a basis for pattern formation and recall. Fukushima et al. address a visual pattern-recogni­tion model. Barto et al. discuss motor learning riiodels. Nagano and Miyajima treat adaptive models related to development. Hirai addresses more abstract issues of con­straint satisfaction relevent to many of these issues in adaptation and decisionmaking.

Sensory Signal Processing (Section V) has remained one of the primary focal points for studies of information processing in neural systems. The papers in this section provide a mixture of experimental and theoretical results. Julesz has provided some of the best examples of experi­mental design which offers theoretical insight, and the paper presented here presents new results related to the discrimination of complex visual patterns. The section in­cludes a series of papers on vision (Gottschalk and Buchs-baum, Cohn, Daugman, Henry, Zaagman et al., Pollen and Ronner, van D o o m and Koenderink, Zuidema et al., Cur-lander and Marmarelis, Normann et al., Tsukada et al.) and a single paper on auditory system (Teich). These papers cover a wide range of physiology, experimental, and analytical tools.

The papers on Motor Control (Section VI) illustrate the integration of system analysis and information processing concepts that is necessary to understand motor system behavior. Stark introduced many systems analysis tools into experimental physiology, and the paper by Lehman and Stark illustrates the use of such tools for a complex set of movement models. Eckmiller presents data and analysis related to eye movements, where a behavioral synthesis of sensory processing and motor control must take place.

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IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, VOL. SMC-13, NO. 5, SEPTEMBER/OCTOBER 1983 667

Inbar explores the consequences of parallel processing in the motor control system.

The Correspondence section touches on a variety of is­sues including psychophysics (McCann and Houston), lo­cal membrane modeling (Deutsch), analog neural simula­tion (Keener), neural delays (Niznik), visual coding (Caelli and Hubner), retinal modeling (Siminoff), neural counting processes (Teich), and parallel channels (Inbar and Ginat) .

There are a variety of topics that we did not try to include due to space constraints. Among the topics are tactile, chemical, and electrical sensing, invertebrate experi­mentation and modeling, nerve cell culture studies, and many additional topics in cognitive and psychological be­havior.

Readers who are not familiar with literature in this area may find the references on cellular neurophysiology (Katz [3], Kuffler and Nicholls, [4] Shepherd [10]), neural model­ing (Leibovic [5], Scott [9], Holden [2], MacGregor and Lewis, [6] Sanderson and Peterka [8]), and neural data analysis (Marmarelis and Marmarelis [7], Glaser and Ruchkin [1]) a good starting place.

As Guest Editors of this issue we would like to express our thanks to all the contributors for an unexpectedly large

number of high quality papers, to the reviewers of the manuscripts, to Andrew Sage, for his encouragement and cooperation throughout this project, and to Robin Wallace at C M U and Judy MacArthur at MIT who kept the whole project moving on schedule.

R E F E R E N C E S

[1] E. R. Glaser and D. S. Ruchkin, Principles of Neurobiological Signal Analysis. New York: Academic 1976.

[2] Α. V. Holden, Models of the Stochastic Activity of Neurons. New York: Springer-Verlag, 1976.

[3] B. Katz, Nerve, Muscle, and Synapse. New York: McGraw-Hill, 1966.

[4] S. W. Kuffler and J. G. Nicholls, From Neuron to Brain. Sunder­land, MA: Sinauer Assoc., 1976.

[5] Κ. N. Leibovic, Nervous System Theory. New York: Academic, 1972.

[6] R. J. MacGregor and E. R. Lewis, Neural Modeling. New York: Plenum, 1977.

[7] P. Z. Marmarelis and V. Z. Marmarelis, Analysis of Physiological Systems. The White Noise Approach. New York: Plenum, 1978.

[8] A. C. Sanderson and R. J. Peterka, "Neural Modeling and Model Identification," CRC Critical Rev. Bioeng., 1983, in press.

[9] A. C. Scott, Neurophysics. New York: Wiley, 1977. [10] G. M. Shepherd, the Synaptic Organization of the Brain. New

York: Oxford Univ., 1974.