A neural network model of sensoritopic maps with predictive short-term memory properties.

Abstract

Coordinated orienting movements can be accurately performed without direct sensory control. Ocular saccades, for instance, have been shown to be reprogrammed after target disappearance when an intervening eye movement is electrically triggered before the saccade onset. Saccadic eye movements can also be executed toward memorized targets, even when the subject has been passively moved in darkness. Two hypotheses have been proposed to account for this goalinvariance property: either (a) the goal is reconstructed and memorized in the stable frame of reference linked to the environment ("allocentric coordinates") or (i) the goal is selected and memorized in the sensors-related maps ("egocentric coordinates") and is continuously updated by efferent copies of the motor commands. In this paper, we shall describe a formal neural network based on this second hypothesis. The results of the simulation show that target position can be memorized and accurately updated in a topologically ordered map, using a velocity-signal feedback. Moreover, this network has been submitted to a simple learning procedure by using the intermittent visual recurring afferent signal as the teaching signal. A similar mechanism could be involved in control of limb movement.Among the various types of orienting movements, visually triggered oculomotor saccades have been extensively studied in the past 2 decades. The stereotyped dynamic characteristics of saccades initially led to the consideration of saccades as an example of ballistic movements elicited from retinal stimulation by a "built-in temporal-pattern generator" (1). In 1975, Robinson (2) argued that visual-target position first has to be recomputed in a craniotopic frame of reference by adding an eye-position efferent copy. His model predicted that when the eyes were displaced by an internal command after the target had disappeared and before the saccade started, the saccade would still end where the target had been. This prediction was confirmed later by Mays and Sparks (3,4) in monkey and by Viviani and Velay (5) in humans. Tweed and Vilis (6) extended the Robinson model to the three-dimension eye-rotation space by using the formalism of quaternionic algebra to code the desired and instantaneous eye position. Moreover, Guitton et al. (7) proposed that target position and gaze position are reconstructed in space coordinates for the programming of eye/head coordinated movements.However, despite intensive investigations in the various neural structures involved in saccade generation, the neural coding of desired eye position in the orbit (and of target in craniotopic coordinates) has never been found. Becker and Jurgens (8) proposed a modification of the Robinson model, in which a dynamic motor-error signal is computed as the difference between the retinotopic target position and the instantaneous eye displacement, provided by an integrator that can be reset. Waitzman et al. (9) have recorded singlecell activities in the intermediate layers of monkey superior colliculus that are c...

Optimal computation with attractor networks

“…If we could construct a network that maps the whole ðN À 1Þ-dimensional space to a single point, the location of that point would provide a natural estimate ofŝ. Attractor networks [4,8,[10][11][12]18,20] could perform such a mapping. These networks evolve in time, starting from some initial condition, to an attractor--a submanifold of their full activity space.…”

Bayesian models of eye movement selection with retinotopic maps

“…They behave as if they were included in a retinotopic memory map, integrating a remapping mechanism allowing the displacement of the memorized activity when an eye movement is performed. Neural network models of that type of maps, either in the SC or the FEF, have already been proposed (Droulez and Berthoz 1991;Bozis and Moschovakis 1998;Mitchell and Zipser 2003). Such a mechanism, adapted to Bayesian programming, is used in the representation and memory layers of our model.…”

Anticipating the three-dimensional consequences of eye movements

“…The simulated-saccade demonstration strongly suggests that extraretinal signals are involved in the brain's solution of the 3D spatial constancy problem. ‡ An important idea in the psychological and physiological literature on spatial constancy is the link between constancy and sensorimotor anticipation (2,7,20,29,30): predicting the sensory reafferences of one's actions could help to distinguish Abbreviations: FP, fixation point; SfM, structure from motion; SO, stimulus onset; PSML, perisaccadic mislocalization. *E-mail: wexler@ccr.jussieu.fr.…”