A neural model of the saccade generator in the reticular formation

Gancarz, Gregory; Grossberg, Stephen

doi:10.1016/s0893-6080(98)00096-3

Cited by 62 publications

(64 citation statements)

References 32 publications

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“…This assumption is consistent with existing models of the brainstem saccade machinery. In those models (Gancarz and Grossberg 1998;Goossens and Van Opstal 2006;Lefevre et al 1998;Quaia et al 1998), SC outputs keep featuring in the computations of the weighted average during the generation of a saccade. Thus changes in SC input to the brainstem (Gancarz and Grossberg 1998) or the cerebellum (Lefevre et al 1998) should result in a changed saccade endpoint.…”

Section: Saccade Initiation and Trajectorymentioning

confidence: 99%

“…Several models of what is often called the brainstem saccade generator use the SC as a top layer, which sends inputs to more elaborately implemented brain stem and cerebellar structures (Gancarz and Grossberg 1998;Lefevre et al 1998;Optican 1995;Quaia et al 1998). Several others have focused on SC or FEF dynamics (Gancarz and Grossberg 1999;Heinzle et al 2007;Kopecz 1995;Trappenberg et al 2001).…”

Section: Comparison To Previous Modelsmentioning

confidence: 99%

“…The idea that activity in SC also codes the length of the saccade is embedded in many models of the brainstem saccade generator. Van Opstal and Van Gisbergen (1990) and Gancarz and Grossberg (1998) both code the length of the saccade in the strength of the signal from SC to the brainstem saccade generator. This would be possible in our model as well, but we did not explore it because in our model the drive from SC also prompts saccade initiation.…”

Section: Comparison To Previous Modelsmentioning

confidence: 99%

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A competitive integration model of exogenous and endogenous eye movements

2010

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We present a model of the eye movement system in which the programming of an eye movement is the result of the competitive integration of information in the superior colliculi (SC). This brain area receives input from occipital cortex, the frontal eye fields, and the dorsolateral prefrontal cortex, on the basis of which it computes the location of the next saccadic target. Two critical assumptions in the model are that cortical inputs are not only excitatory, but can also inhibit saccades to specific locations, and that the SC continue to influence the trajectory of a saccade while it is being executed. With these assumptions, we account for many neurophysiological and behavioral findings from eye movement research. Interactions within the saccade map are shown to account for effects of distractors on saccadic reaction time (SRT) and saccade trajectory, including the global effect and oculomotor capture. In addition, the model accounts for express saccades, the gap effect, saccadic reaction times for antisaccades, and recorded responses from neurons in the SC and frontal eye fields in these tasks.

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Section: Saccade Initiation and Trajectorymentioning

confidence: 99%

Section: Comparison To Previous Modelsmentioning

confidence: 99%

Section: Comparison To Previous Modelsmentioning

confidence: 99%

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A competitive integration model of exogenous and endogenous eye movements

2010

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“…[41,19,45,13] for a review see [15], but we believe that our developmental approach [26] is able to produce a method with a unique set of features, namely: (i) it is not pre-wired but learns how to saccade, (ii) it learns very rapidly -much faster than current neural network based approaches, (iii) it continuously adapts to correct errors and accommodate any changes in the ocular-motor system, (iv) it does not use or require any calibration process or prior knowledge, and (iv) the generated behaviour displays distinct and qualitatively different stages which emerge during learning.…”

Section: Introductionmentioning

confidence: 99%

A developmental algorithm for ocular–motor coordination

Chao

Lee

2010

Robotics and Autonomous Systems

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This paper presents a model of ocular-motor development, inspired by ideas and data from developmental psychology. The learning problem concerns the growth of the transform between image space and motor space necessary for the control of visual saccades. An implementation is used to produce experimental results and these are presented and discussed. The algorithm is simple, extremely fast, self calibrating, adaptive to change, and exhibits emergent stages of behaviour as learning progresses.

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“…The last few years have seen the development of differential equation models (Gancarz and Grossberg, 1998;Broomhead et al, 2000) together with the application of time series analysis to eye movement recordings (Shelhamer, 1997;Abadi et al, 1997;Clement et al, 2002b,c).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear time series analysis of jerk congenital nystagmus

Akman

Broomhead

Clement³

et al. 2006

J Comput Neurosci

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Nonlinear dynamics provides a complementary framework to control theory for the quantitative analysis of the oculomotor control system. This paper presents a number of findings relating to the aetiology and mechanics of the pathological ocular oscillation jerk congenital nystagmus (jerk CN). A range of time series analysis techniques were applied to both recorded jerk CN waveforms and simulated waveforms produced by an established model in which the oscillations are a consequence of an unstable neural integrator. The results of the time series analysis were then interpreted within the framework of a generalised model of the unforced oculomotor system. This work suggests that for jerk oscillations, the origin of the instability lies in one of the five oculomotor subsystems, rather than in the final common pathway (the neural integrator and muscle plant). Additionally, experimental estimates of the linearised foveation dynamics imply that a refixating fast phase induced by a near-homoclinic trajectory will result in periodic oscillations. Local dimension calculations show that the dimension of the experimental jerk CN data increases during the fast phase, indicating that the oscillations are not periodic, and hence that the refixation mechanism is of greater complexity than a homoclinic reinjection. The dimension increase is hypothesised to result either from a signal-dependent noise process in the saccadic system, or the activation of additional oculomotor components at the beginning of the fast phase. The modification of a recent saccadic system model to incorporate biologically realistic signal-dependent noise is suggested, in order to test the first of these hypotheses.

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A neural model of the saccade generator in the reticular formation

Cited by 62 publications

References 32 publications

A competitive integration model of exogenous and endogenous eye movements

A competitive integration model of exogenous and endogenous eye movements

A developmental algorithm for ocular–motor coordination

Nonlinear time series analysis of jerk congenital nystagmus

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