in the middle temporal visual area (MT) of anesthetized, paralyzed macaque monkeys. A computer-driven stimulator was used to make quantitative tests of selectivity for stimulus direction, speed, and orientation. The data were taken from 168 units that were histologically identified as being in MT. 2. The results confirm previous reports of a high degree of direction selectivity in MT. The response above background to stimuli moving in a unit’s preferred direction was, on average, 10.9 times that to stimuli moving in the opposite direction. There was a marked tendency for nearby units to have similar preferred directions. 3. Most units were also sharply tuned for the speed of stimulus motion. For some cells the response fell to less than half-maximal at speeds only a factor of two from the optimum; on average, responses were greater than half-maximal only over a 7.7-fold range of speed. The distribution of preferred speeds for different units was unimodal, with a peak near 32”/s; the total range of preferred speeds extended from 2 to 256”/s. Nearby units generally responded best to similar speeds of motion. 4. Most units in MT showed selectivity for stimulus orientation when tested with stationary, flashed bars. However, stationary stimuli generally elicited only brief responses; when averaged over the duration of the stimulus, the responses were much less than those to moving stimuli. The preferred orientation was usually, but not always, perpendicular to the preferred direction of movement. 5. A comparison of the results of the present study with a previous quantitative investigation in the owl monkey shows a striking similarity in response properties in MT of the two species. 6. The presence of both direction and speed selectivity in MT of the macaque suggests that this area is more specialized for the analysis of visual motion than has been previously recognized.

This paper is a study of the behavioral and spatial firing correlates of neurons in the rat postsubiculum. Recordings were made from postsubicular neurons as rats moved freely throughout a cylindrical chamber, where the major cue for orientation was a white card taped to the inside wall. An automatic video/computer system monitored cell discharge while simultaneously tracking the position of 2 colored light emitting diodes (LEDs) secured to the animal’s head. The animal’s location was calculated from the position of one of the LEDs and head direction in the horizontal plane calculated from the relative positions of the 2 LEDs. Approximately 26 % of the cells were classified as headdirection cells because they discharged as a function of the animal’s head direction in the horizontal plane, independent of the animal’s behavior, location, or trunk position. For each

To investigate the relations between structure and function in both artificial and natural neural networks, we present a series of simulations and analyses with modular neural networks. We suggest a number of design principles in the form of explicit ways in which neural modules can cooperate in recognition tasks. These results may supplement recent accounts of the relation between structure and function in the brain. The networks used consist out of several modules, standard subnetworks that serve as higher-order units with a distinct structure and function. The simulations rely on a particular network module called CALM (Murre, Phaf, and Wolters, 1989, 1992). This module, developed mainly for unsupervised categorization and learning, is able to adjust its local learning dynamics. The way in which modules are interconnected is an important determinant of the learning and categorization behaviour of the network as a whole. Based on arguments derived from neuroscience, psychology, compu...

The cortical motor system has been classically considered as the unitary, output stage of the brain processing of sensory information. According to this idea, the motor cortex � the acting brain � receives the result of the perceptual processing Ž visual, acoustical, tactile, etc.. elaborated by the ‘associative cortex’. During the last two decades this perspective has been challenged by a series of anatomical, hodological, and neurophysiological data. This converging evidence delineates a dramatically changed picture. Far from being unitary, the cortical motor system appears to be constituted by a constellation of distinct areas, each of those endowed with specific functional properties and linked by reciprocal connections with distinct sectors of the parietal cortex. Furthermore, several ‘motor ’ neurons in addition to their motor discharge, are also activated by somatosensory and visual stimulation Ž somatomotor and visuomotor neurons.. In the present paper we will discuss the functional properties of those sensorimotor neurons located in the ventral part of the monkey premotor cortex. On the basis of electrophysiological data, we will propose that the apparent parodox stemming from the coexistence within the same neuron of motor and sensory properties can be solved by postulating that the motor system not only executes actions but also internally represents them in terms of ‘motor ideas’. These motor ideas may provide the neurobiological basis for space representation, understanding of actions made by others and, possibly, semantic categorization of objects.

The rapid collection of brain images from healthy and diseased subjects has stimulated the development of powerful mathematical algorithms to compare, pool and average brain data across whole populations. Brain structure is so complex and variable that new approaches in computer vision, partial differential equations, and statistical field theory are being formulated to detect and visualize disease-specific patterns. We present some novel mathematical strategies for computational anatomy, focusing on the creation of population-based brain atlases. These atlases describe how the brain varies with age, gender, genetics, and over time. We review applications in Alzheimer's disease, schizophrenia and brain development, outlining some current challenges in the field.

A key goal of behavioral and cognitive neuroscience is to link brain mechanisms to behavioral functions. The present article describes recent progress towards explaining how the visual cortex sees. Visual cortex, like many parts of perceptual and cognitive neocortex, is organized into six main layers of cells, as well as characteristic sub-lamina. Here it is proposed how these layered circuits help to realize processes of development, learning, perceptual grouping, attention, and 3D vision through a combination of bottom-up, horizontal, and top-down interactions. A key theme is that the mechanisms which enable development and learning to occur in a stable way imply properties of adult behavior. These results thus begin to unify three fields: infant cortical development, adult cortical neurophysiology and anatomy, and adult visual perception. The identified cortical mechanisms promise to generalize to explain how other perceptual and cognitive processes work.

Recent parallels between neurophysiological and neuroimaging findings suggest that repeated stimulus processing produces decreased responses in brain regions associated with that processing — a ‘repetition suppression ’ effect. In the present study, volunteers performed two tasks on repeated presentation of famous and unfamiliar faces during functional magnetic resonance imaging (fMRI). In the implicit task, they made fame-judgements (regardless of repetition); in the explicit task, they made episodic recognition judgements (regardless of familiarity). Only in the implicit task was repetition suppression observed: for famous faces in a right lateral fusiform region, and for both famous and unfamiliar faces in a left inferior occipital region. Repetition suppression is therefore not an automatic consequence of repeated perceptual processing of stimuli.

How is development of cortical maps in V1 coordinated across cortical layers to form cortical columns? Previous neural models propose how maps of orientation (OR), ocular dominance (OD), and related properties develop in V1. These models show how spontaneous activity, before eye opening, combined with correlation learning and competition, can generate maps similar to those found in vivo. These models have not discussed laminar architecture or how cells develop and coordinate their connections across cortical layers. This is an important problem since anatomical evidence shows that clusters of horizontal connections form, between iso-oriented regions, in layer 2/3 before being innervated by layer 4 afferents. How are orientations in different layers aligned before these connections form? Anatomical evidence demonstrates that thalamic afferents wait in the subplate for weeks before innervating layer 4. Other evidence shows that ablation of the cortical subplate interferes with the development of OR and OD columns. The model proposes how the subplate develops OR and OD maps, which then entrain and coordinate the development of maps in other lamina. The model demonstrates how these maps may develop in layer 4 by using a known transient subplate-to-layer 4 circuit as a teacher. The model subplate also guides the early clustering of horizontal connections in layer 2/3, and the formation of the interlaminar circuitry that forms cortical columns. It is shown how layer 6 develops and helps to stabilize the network when the subplate atrophies. Finally the model clarifies how BDNF manipulations may influence cortical development.

Introduction Image registration is a prerequisite to numerous imaging applications in the neurosciences. Registration is required for three dimensional reconstruction, multimodality image mappings, atlas construction and arithmetic operations such as image averaging, subtraction and correlation. Physical sectioning procedures, unlike various tomographic imaging techniques, also require additional superpositioning schemes to register serial sections. Since the mid 1980's investigators have used several approaches, with varying degrees of manual interaction, to perform image registration. These approaches use either information obtained about the shape and topology of objects in the image, or the presumed consistency in the intensity information from one slice to its immediate neighbor or from one brain or image set to another. Registration algorithms have a powerful range of applications when used in conjunction with a brain atlas. Digital atlases of the brain,

Abstract not found