Keywords

schizophrenia, contrast sensitivity, orientation tuning

Abstract

Computational modeling is being increasingly used to understand schizophrenia, but, to date, it has not been used to account for the common perceptual disturbances in the disorder. We manipulated schizophrenia-relevant parameters in the GCAL (gain control, adaptation, laterally connected) model (Stevens et al., 2013), run using the Topographica simulator (Bednar, 2012), to model low-level visual processing changes in the disorder. Our models incorporated: separate sheets for retinal, LGN, and V1 activity; gain control in the LGN; homeostatic adaptation in V1 based on a weighted sum of all inputs and limited by a logistic (sigmoid) nonlinearity; lateral excitation and inhibition in V1; and self-organization of synaptic weights based on Hebbian learning. Data indicated that: 1) findings of increased contrast sensitivity for low spatial frequency stimuli in first episode schizophrenia (FES) can be successfully modeled as a function of reduced retinal and LGN efferent activity within the context of normal LGN gain control and cortical mechanisms (see Figure 1); and 2) findings of reduced contrast sensitivity and broadened orientation tuning in chronic schizophrenia can be successfully modeled by a combination of reduced V1 lateral inhibition and an increase in the Hebbian learning rate at V1 synapses for LGN input (see Figures 1-3). These models are consistent with many current findings (Silverstein, 2016) and they predict relationships that have not yet been explored. They also have implications for understanding links between perceptual changes and psychotic symptom formation, and for understanding changes during the long-term course of the disorder.

Start Date

17-5-2017 9:00 AM

End Date

17-5-2017 9:22 AM

Location

Rutgers University

 
May 17th, 9:00 AM May 17th, 9:22 AM

Computational Modeling of Contrast Sensitivity and Orientation Tuning in Schizophrenia

Rutgers University

Computational modeling is being increasingly used to understand schizophrenia, but, to date, it has not been used to account for the common perceptual disturbances in the disorder. We manipulated schizophrenia-relevant parameters in the GCAL (gain control, adaptation, laterally connected) model (Stevens et al., 2013), run using the Topographica simulator (Bednar, 2012), to model low-level visual processing changes in the disorder. Our models incorporated: separate sheets for retinal, LGN, and V1 activity; gain control in the LGN; homeostatic adaptation in V1 based on a weighted sum of all inputs and limited by a logistic (sigmoid) nonlinearity; lateral excitation and inhibition in V1; and self-organization of synaptic weights based on Hebbian learning. Data indicated that: 1) findings of increased contrast sensitivity for low spatial frequency stimuli in first episode schizophrenia (FES) can be successfully modeled as a function of reduced retinal and LGN efferent activity within the context of normal LGN gain control and cortical mechanisms (see Figure 1); and 2) findings of reduced contrast sensitivity and broadened orientation tuning in chronic schizophrenia can be successfully modeled by a combination of reduced V1 lateral inhibition and an increase in the Hebbian learning rate at V1 synapses for LGN input (see Figures 1-3). These models are consistent with many current findings (Silverstein, 2016) and they predict relationships that have not yet been explored. They also have implications for understanding links between perceptual changes and psychotic symptom formation, and for understanding changes during the long-term course of the disorder.