This often results in the phenomenon known as predictive remapping, which has been observed through higher level visual and visuomotor areas of the brain (Duhamel et al., 1992; Walker et al., 1995; Umeno and Goldberg, 1997; Nakamura and Colby, 2002; Sommer and Wurtz, 2006).
Neurons that show predictive remapping are activated by saccades that will bring visual stimulus into their receptive fields (RFs) even before the actual movement of the eyes (Duhamel et al., 1992).
Here, we took a step in this direction by examining whether training on the motor aspects of spatial updating can produce the updating/remapping signals that have been observed in visual neurons.
This is often interpreted in terms of the visual properties of single neurons, i.e., as a transient extension of the RF toward the future location of the stimulus; the future RF.
However, some neurons that show predictive remapping also show sustained activity at the new eye position when the standard RF stabilizes over the previous stimulus location (Duhamel et al., 1992).
Moreover, our model makes the new prediction that, when uncertainty of input signals is incorporated in the model, neural population activity and receptive fields expand just before and during saccades.
These results suggest that visual remapping and motor updating are part of a common visuomotor mechanism, and that subjective perceptual constancy arises in part from training the visual system on motor tasks.
The model parameters and internal states (remembered target position) are estimated sequentially using the EKF method.
The proposed model replicates two fundamental experimental observations: continuous gaze-centered updating of visual memory-related activity during smooth pursuit, and predictive remapping of visual memory activity before and during saccades.
Past theoretical efforts have used control-system type models to explain the spatiotemporal and geometric aspects of updating (Quaia et al., 1998; Optican and Quaia, 2002; Blohm et al., 2006; Cromer and Waitzman, 2006; Van Pelt and Medendorp, 2007), and neural network models to predict specific signals (Zipser and Andersen, 1988; White and Snyder, 2004, 2007; Keith et al., 2010).
However, there is still no general theoretical framework for spatial updating and remapping.
SSMs provide an effective method for modeling dynamical systems and it can represent the internal behavior of these systems.
Here, we constrained our model to receive simulated inputs (Visual RFs, eye positions signals, and eye movements signals) that have already been physiologically verified (Walker et al., 1995; Hanes and Schall, 1996; Sommer and Wurtz, 2002; Marino et al., 2008; Morris et al., 2012), and trained it to update the location of saccade target after an intervening saccade or smooth pursuit movement.
Despite numerous investigations on this topic, many unanswered questions remain about the theory and mechanisms of visual and visuomotor updating (Thier and Ilg, 2005; Ibbotson and Krekelberg, 2011).