Fundamental Properties of Intensity, Form, and Motion Perception in the Visual Nervous Systems of Calliphora phaenicia and Musca domestica

Several classes of interneurons in the optic lobes and brain of the insects, Musca domestica and Calliphora phaenicia, have been studied in detail. Visual stimuli have been categorized on the basis of the properties of intensity, form, and motion. Response characteristics of the classes of neural units are described with respect to these three classes of visual stimuli. While those units that detect motion in select directions have a tonic response, form detection units have a phasic response only. Through correlation of the responses of these classes with visual stimuli, it is shown that these units integrate the responses of other units which have very small visual fields. The small-field units are presumed to integrate the output of a small group of adjacent retinula cells and to respond differentially to intensity, form, and motion. It is shown that the response of both form and motion detection units is independent of the direction of pattern intensity gradation. As a consequence of this independence, it is further shown that failure to detect motion properly must start at a spatial wavelength four times the effective sampling station spacing rather than twice as has been predicted previously

PLEXUS—an on-line system for modeling neural networks

A description is presented of PLEXUS, a system which enables a user to construct and specify a neural network, to analyze the output data produced by the network, and to store and retrieve networks and data from a library. The system, operated entirely from a digital display unit, interacts directly with the user and permits easy and rapid transitions between the various phases of the modeling process. PLEXUS is designed to complement neurophysiological research so that the systematic development of neural models can be coordinated with experimental work. PLEXUS networks are built up from components representing individual neurons, external stimuli, and interconnecting fibers, each component being of a relatively detailed nature. Provision is also made for the use of experimental data as input to a network. Convenient means for specification and modification of a network and extensive error-checking capabilities are provided. Data resulting from the simulation of a network may be analyzed by a variety of techniques ranging from examinations of the gross characteristics of the data to the determination of detailed statistical properties

PLEXUS—an on-line system for modeling neural networks

A description is presented of PLEXUS, a system which enables a user to construct and specify a neural network, to analyze the output data produced by the network, and to store and retrieve networks and data from a library. The system, operated entirely from a digital display unit, interacts directly with the user and permits easy and rapid transitions between the various phases of the modeling process. PLEXUS is designed to complement neurophysiological research so that the systematic development of neural models can be coordinated with experimental work. PLEXUS networks are built up from components representing individual neurons, external stimuli, and interconnecting fibers, each component being of a relatively detailed nature. Provision is also made for the use of experimental data as input to a network. Convenient means for specification and modification of a network and extensive error-checking capabilities are provided. Data resulting from the simulation of a network may be analyzed by a variety of techniques ranging from examinations of the gross characteristics of the data to the determination of detailed statistical properties